SHOULD ALGORITHMS DECIDE YOUR FUTURE? This publication was prepared by Kilian Vieth and Joanna Bronowicka from Centre for Internet and Human Rights at European University Viadrina. It was prepared based on a publication “The Ethics of Algorithms: from radical content to self-driving cars” with contributions from Zeynep Tufekci, Jillian C. York, Ben Wagner and Frederike Kaltheuner and an event on the Ethics of Algorithms, which took place on March 9-10, 2015 in Berlin. The research was support- ed by the Dutch Ministry of Foreign Affairs. Find out more: cihr.eu/ethics-of-algorithms/ Follow the discussion on Twitter: #EoA2015 Graphic design by Thiago Parizi cihr.eu @cihr_eu 1 | ETHICS OF ALGORITHMS ETHICS OF ALGORITHMS | 2 WHAT IS AN ALGORITHM? ALGORITHMS SHAPE OUR WORLD(S)! Our everyday life is shaped by computers and our computers are shaped by algorithms. Digital computation is constantly changing how we commu- nicate, work, move, and learn. In short, digitally connected computers are changing how we live our lives. This revolution is unlikely to stop any time soon. Digitalization produces increasing amounts of datasets known as ‘big data’. So far, research focused on how ‘big data is produced and stored. Now, we begin to scrutinize how algorithms make sense of this growing amount of data Algorithms are the brains of our computers, mobiles, Internet of Things. Algorithms are increasingly used to make decisions for us, about us, or with us – oftentimes without us realizing it. This raises many questions about the ethical dimension of algorithms. WHY DO ALGORITHMS RAISE ETHICAL CONCERNS? First, let's have a closer look at some of the critical features of algorithms. What are typical functions they perform? What are negative impacts for human rights? Here are some examples that probably affect you too. THEY KEEP INFORMATION AWAY FROM US Increasingly, algorithms decide what gets attention, and what is ignored; and even what gets published at all, and what is censored. This is true for all kinds of search rankings, for example the way your social media news- feed looks. In other words, algorithms perform a gate-keeping function. EXAMPLE Hiring algorithms decide if you are invited for an interview. • Algorithms, rather than managers, are more and more taking part in hiring (and firing) of employees. Deciding who gets a job and who does not, is among the most powerful gate-keeping function in society. • Research shows that human managers display many different biases in hiring decisions, for example based on social class, race and gender. Clearly, human hiring systems are far from perfect. • Nevertheless, we may not simply assume that algorithmic hiring can easily overcome human biases. Algorithms might work more accurate in some areas, but can also create new, sometimes unintended, prob- lems depending on how they are programmed and what input data is used. Ethical.

1. SHOULD ALGORITHMS DECIDE YOUR FUTURE?
This publication was prepared by Kilian Vieth and
Joanna Bronowicka from Centre for Internet and
Human Rights at European University Viadrina. It was
prepared based on a publication “The Ethics of
Algorithms: from radical content to self-driving cars”
with contributions from Zeynep Tufekci, Jillian C. York,
Ben Wagner and Frederike Kaltheuner and an event
on the Ethics of Algorithms, which took place on
March 9-10, 2015 in Berlin. The research was support-
ed by the Dutch Ministry of Foreign Affairs.
Find out more: cihr.eu/ethics-of-algorithms/
Follow the discussion on Twitter: #EoA2015
Graphic design by Thiago Parizi
cihr.eu @cihr_eu

2. 1 | ETHICS OF ALGORITHMS ETHICS OF ALGORITHMS |
2
WHAT IS AN ALGORITHM?
ALGORITHMS SHAPE OUR WORLD(S)!
Our everyday life is shaped by computers and our computers are
shaped
by algorithms. Digital computation is constantly changing how
we commu-
nicate, work, move, and learn. In short, digitally connected
computers are
changing how we live our lives. This revolution is unlikely to
stop any time
soon.
Digitalization produces increasing amounts of datasets known
as ‘big
data’. So far, research focused on how ‘big data is produced and
stored.
Now, we begin to scrutinize how algorithms make sense of this
growing
amount of data
Algorithms are the brains of our computers, mobiles, Internet of
Things.
Algorithms are increasingly used to make decisions for us,
about us, or

3. with us – oftentimes without us realizing it. This raises many
questions
about the ethical dimension of algorithms.
WHY DO ALGORITHMS RAISE ETHICAL
CONCERNS?
First, let's have a closer look at some of the critical features of
algorithms.
What are typical functions they perform? What are negative
impacts for
human rights? Here are some examples that probably affect you
too.
THEY KEEP INFORMATION AWAY FROM US
Increasingly, algorithms decide what gets attention, and what is
ignored;
and even what gets published at all, and what is censored. This
is true for
all kinds of search rankings, for example the way your social
media news-
feed looks. In other words, algorithms perform a gate-keeping
function.
EXAMPLE
Hiring algorithms decide if you are invited for an interview.
• Algorithms, rather than managers, are more and more taking
part in

4. hiring (and firing) of employees. Deciding who gets a job and
who does
not, is among the most powerful gate-keeping function in
society.
• Research shows that human managers display many different
biases in
hiring decisions, for example based on social class, race and
gender.
Clearly, human hiring systems are far from perfect.
• Nevertheless, we may not simply assume that algorithmic
hiring can
easily overcome human biases. Algorithms might work more
accurate
in some areas, but can also create new, sometimes unintended,
prob-
lems depending on how they are programmed and what input
data is
used.
Ethical implications: Algorithms work as gatekeepers that
influence how
we perceive the world, often without us realizing it. They
channel our
attention, which implies tremendous power.

5. The term ‘algorithm’ refers to any computer code that carries
out a
set of instructions. Algorithms are essential to the way
computers
process data. Theoretically speaking, they are encoded
procedures,
which transform data based on specific calculations. They
consist of
a series of steps that are undertaken to solve a particular
problem,
like in a recipe. An algorithm is taking inputs (ingredients),
breaking
a task into its constituent parts, undertaking those tasks one by
one,
and then producing an output (e.g. a cake). A simple example of
an
algorithm is “find the largest number in this series of numbers”.
THEY MAKE SUBJECTIVE DECISIONS
Some algorithms deal with questions, which do not have a clear
‘yes or no’
answer. Thus, they move a way from a checkbox answer “Is this
right or
wrong?” to more complex judgements, such as “What is
important? Who is

6. the right person for the job? Who is a threat to public safety?
Who should I
date?” Quietly, these types of subjective decisions previously
made by
humans are turned over to algorithms.
EXAMPLE
Predictive policing based on statistical forecasting:
• In early 2014, the Chicago Police Department made national
headlines
in the US for visiting residents who were considered to be most
likely
involved in violent crime. The selection of individuals, wo were
not
necessarily under investigation, was guided by a computer-
generated
“heat list” – an algorithm that seeks to predict future
involvement in
violent crime.
• A key concern about predictive policing is that such
automated systems
may create an echo chamber or a self-fulfilling prophecy. In
fact, heavy
policing of a specific area can increase the likelihood that crime

7. will be
detected. Since more police means more opportunities to
observe
residents' activities, the algorithm might just confirm its own
predic-
tion.
• Right now, police departments around the globe are testing
and imple-
menting predictive policing algorithms, but lack safeguards for
discrim-
inatory biases.
Ethical implications: Predictions made by algorithms provide
no guaran-
tee that they are right. And officials acting on incorrect
predictions may
even create unjustified or biased investigations.
CORE ISSUES – SHOULD AN ALGORITHM
DECIDE YOUR FUTURE?
Without the help of algorithms, many present-day applications
would be
unusable. We need them to cope with the enormous amounts of
data we
produce every day. Algorithms make our lives easier and more
productive,

8. and we certainly don't want to lose those advantages. But we
need to be
aware of what they do and how they decide.
THEY CAN DISCRIMINATE AGAINST YOU,
JUST LIKE HUMANS
Computers are often regarded as objective and rational
machines. Howev-
er, algorithms are made by humans and can be just as biased.
We need to
be critical of the assumption, that algorithms can make “better”
decisions
than human beings. There are racist algorithms and sexist ones.
Algorithms
are not neutral, but rather they perpetuate the prejudices of their
creators.
Their creators, such as businesses or governments, can
potentially have
different goals in mind than the users would have.
THEY MUST BE KNOWN TO THE USER
Since algorithms make increasingly important decisions about
our lives,
users need to be informed about them. Knowledge about
automated

9. decision-making in everyday services is still very limited
among consumers.
Raising awareness should be at the heart of the debate about
ethics of
algorithms.
PUBLIC POLICY APPROACHES TO REGULATE
ALGORITHMS
How do you regulate a black box? We need to open a discussion
on how
policy-makers are trying to deal with ethical concerns around
algorithms.
There have been some attempts to provide algorithmic
accountability, but
we need better data and more in-depth studies.
If algorithms are written and used by corporations, it is
government
institutions like antitrust or consumer protection agencies, who
should
provide appropriate regulation and oversight. But who regulates
the use of
algorithms by the government itself? For cases like predictive
policing
ethical standards and legal safeguards are needed.

10. Recently, regulatory approaches to algorithms have circled
around trans-
parency, notification, and direct regulation. Yet, experience
shows that
policy-makers are facing certain dilemmas of regulation when it
comes to
algorithms.
TRANSPARENCY | MAKE OPAQUE BIASES VISIBLE
If you are faced with a complex and obscure algorithm, one
common
reaction is a demand for more transparency about what and how
it works.
The concern about black-box algorithms is that they make
inherently
subjective decisions, which might contain implicit or explicit
biases. At the
same time, making complex algorithms fully transparent can be
extremely
challenging:
• It is not enough to merely publish the source code of an
algorithm,
because machine-learning systems will inevitably make
decisions that

11. have not been programmed directly. Complete transparency
would
require that we are able to explain why any particular outcome
was
produced.
• Some investigations have reverse-engineered algorithms in
order to
create greater public awareness about them. That is one way
how the
public can perform a watchdog function.
• Often, there might be good reasons why complex algorithms
operate
opaquely, because public access would make them much more
vulnera-
ble to manipulation. If every company knew how Google ranks
its
search results, it could optimize their behavior and render the
ranking
algorithm useless.
NOTIFICATION | GIVING USERS THE RIGHT TO KNOW
A different form of transparency is to give consumers control
over their
personal information that feeds into algorithms. Notification

12. includes the
rights to correct that personal information and demand it be
excluded
from databases of data vendors. Regaining control over your
personal
information ensures accountability to the users.
DIRECT REGULATION | WHEN ALGORITHMS BECOME
CRITICAL INFRASTRUCTURE
• In some cases public regulators have been prone to create
ways to
manipulate algorithms directly. This is especially relevant for
core
infrastructure.
Debate about algorithmic regulation is most advanced in the
area of
finance. Automated high-speed trading has potentially
destabilizing
effects on financial markets, so regulators have begun to
demand the
ability to modify these algorithms.
• The ongoing antitrust investigations into Google's ‘search
neutrality’
revolve around the same question: can regulators may require
access to

13. and modification of the search algorithm in the interest of the
public?
This approach is based on a contested assumption that it is
possible to
predict objectively how a certain algorithms will respond. Yet,
there is
simply no ‘right’ answer to how Google should rank its results.
Antitrust
agencies in the US and the EU have not yet found an regulatory
response to this issue.
In some cases direct regulation or complete and public
transparency might
be necessary. However, there is no one-size-fits-all regulatory
response.
More scrutiny of algorithms must be enabled, which requires
new practices
from industry and technologists. More consumer protection and
direct
regulation should be introduced where appropriate.
WHY DO ALGORITHMS RAISE ETHICAL
CONCERNS?
First, let's have a closer look at some of the critical features of

14. algorithms.
What are typical functions they perform? What are negative
impacts for
human rights? Here are some examples that probably affect you
too.
THEY KEEP INFORMATION AWAY FROM US
Increasingly, algorithms decide what gets attention, and what is
ignored;
and even what gets published at all, and what is censored. This
is true for
all kinds of search rankings, for example the way your social
media news-
feed looks. In other words, algorithms perform a gate-keeping
function.
EXAMPLE
Hiring algorithms decide if you are invited for an interview.
• Algorithms, rather than managers, are more and more taking
part in
hiring (and firing) of employees. Deciding who gets a job and
who does
not, is among the most powerful gate-keeping function in
society.
• Research shows that human managers display many different
biases in

15. hiring decisions, for example based on social class, race and
gender.
Clearly, human hiring systems are far from perfect.
• Nevertheless, we may not simply assume that algorithmic
hiring can
easily overcome human biases. Algorithms might work more
accurate
in some areas, but can also create new, sometimes unintended,
prob-
lems depending on how they are programmed and what input
data is
used.
Ethical implications: Algorithms work as gatekeepers that
influence how
we perceive the world, often without us realizing it. They
channel our
attention, which implies tremendous power.
3 | ETHICS OF ALGORITHMS ETHICS OF ALGORITHMS |
4
THEY MAKE SUBJECTIVE DECISIONS
Some algorithms deal with questions, which do not have a clear
‘yes or no’
answer. Thus, they move a way from a checkbox answer “Is this

16. right or
wrong?” to more complex judgements, such as “What is
important? Who is
the right person for the job? Who is a threat to public safety?
Who should I
date?” Quietly, these types of subjective decisions previously
made by
humans are turned over to algorithms.
EXAMPLE
Predictive policing based on statistical forecasting:
• In early 2014, the Chicago Police Department made national
headlines
in the US for visiting residents who were considered to be most
likely
involved in violent crime. The selection of individuals, wo were
not
necessarily under investigation, was guided by a computer-
generated
“heat list” – an algorithm that seeks to predict future
involvement in
violent crime.
• A key concern about predictive policing is that such
automated systems

17. may create an echo chamber or a self-fulfilling prophecy. In
fact, heavy
policing of a specific area can increase the likelihood that crime
will be
detected. Since more police means more opportunities to
observe
residents' activities, the algorithm might just confirm its own
predic-
tion.
• Right now, police departments around the globe are testing
and imple-
menting predictive policing algorithms, but lack safeguards for
discrim-
inatory biases.
Ethical implications: Predictions made by algorithms provide
no guaran-
tee that they are right. And officials acting on incorrect
predictions may
even create unjustified or biased investigations.
WE DON’T ALWAYS KNOW HOW THEY WORK
Complexity is huge: Many present day algorithms are very
complicated
and can be hard for humans to understand, even if their source

18. code is
shared with competent observers. What adds to the problem is
opacity, as
in lack of transparency of the code. Algorithms perform
complex calcula-
tions, which follow many potential steps along the way. They
can consist of
thousands, or even millions, of individual data points.
Sometimes not even
the programmers can predict how an algorithm will decide on a
certain
case.
EXAMPLE
Facebook newsfeed algorithms is complex and opaque:
• Many users are not aware that when we open Facebook, an
algorithm
that puts together the postings, pictures and ads that we see.
Indeed, it
is an algorithm that decides what to show us and what to hold
back.
Facebook newsfeed algorithm filters the content you see, but do
you
know the principles it uses to hold back information from you?

19. • In fact, a team of researchers tweaks this algorithm every
week – they
take thousands and thousands of metrics into consideration.
This is
why the effects of newsfeed algorithms are hard to predict -
even by
Facebook engineers!
• If we asked Facebook how the algorithm works, they would
not tell us.
The principles behind the way newsfeed work (the source code)
are in
fact a business secret. Without knowing the exact code, nobody
can
evaluate how your newsfeed is composed.
Ethical implications: Complex algorithms are often practically
incompre-
hensible to outsiders, but they inevitably have values, biases,
and potential
discrimination built in.
CORE ISSUES – SHOULD AN ALGORITHM
DECIDE YOUR FUTURE?
Without the help of algorithms, many present-day applications
would be
unusable. We need them to cope with the enormous amounts of

20. data we
produce every day. Algorithms make our lives easier and more
productive,
and we certainly don't want to lose those advantages. But we
need to be
aware of what they do and how they decide.
THEY CAN DISCRIMINATE AGAINST YOU,
JUST LIKE HUMANS
Computers are often regarded as objective and rational
machines. Howev-
er, algorithms are made by humans and can be just as biased.
We need to
be critical of the assumption, that algorithms can make “better”
decisions
than human beings. There are racist algorithms and sexist ones.
Algorithms
are not neutral, but rather they perpetuate the prejudices of their
creators.
Their creators, such as businesses or governments, can
potentially have
different goals in mind than the users would have.
THEY MUST BE KNOWN TO THE USER
Since algorithms make increasingly important decisions about

21. our lives,
users need to be informed about them. Knowledge about
automated
decision-making in everyday services is still very limited
among consumers.
Raising awareness should be at the heart of the debate about
ethics of
algorithms.
PUBLIC POLICY APPROACHES TO REGULATE
ALGORITHMS
How do you regulate a black box? We need to open a discussion
on how
policy-makers are trying to deal with ethical concerns around
algorithms.
There have been some attempts to provide algorithmic
accountability, but
we need better data and more in-depth studies.
If algorithms are written and used by corporations, it is
government
institutions like antitrust or consumer protection agencies, who
should
provide appropriate regulation and oversight. But who regulates
the use of
algorithms by the government itself? For cases like predictive

22. policing
ethical standards and legal safeguards are needed.
Recently, regulatory approaches to algorithms have circled
around trans-
parency, notification, and direct regulation. Yet, experience
shows that
policy-makers are facing certain dilemmas of regulation when it
comes to
algorithms.
TRANSPARENCY | MAKE OPAQUE BIASES VISIBLE
If you are faced with a complex and obscure algorithm, one
common
reaction is a demand for more transparency about what and how
it works.
The concern about black-box algorithms is that they make
inherently
subjective decisions, which might contain implicit or explicit
biases. At the
same time, making complex algorithms fully transparent can be
extremely
challenging:
• It is not enough to merely publish the source code of an
algorithm,

23. because machine-learning systems will inevitably make
decisions that
have not been programmed directly. Complete transparency
would
require that we are able to explain why any particular outcome
was
produced.
• Some investigations have reverse-engineered algorithms in
order to
create greater public awareness about them. That is one way
how the
public can perform a watchdog function.
• Often, there might be good reasons why complex algorithms
operate
opaquely, because public access would make them much more
vulnera-
ble to manipulation. If every company knew how Google ranks
its
search results, it could optimize their behavior and render the
ranking
algorithm useless.
NOTIFICATION | GIVING USERS THE RIGHT TO KNOW

24. A different form of transparency is to give consumers control
over their
personal information that feeds into algorithms. Notification
includes the
rights to correct that personal information and demand it be
excluded
from databases of data vendors. Regaining control over your
personal
information ensures accountability to the users.
DIRECT REGULATION | WHEN ALGORITHMS BECOME
CRITICAL INFRASTRUCTURE
• In some cases public regulators have been prone to create
ways to
manipulate algorithms directly. This is especially relevant for
core
infrastructure.
Debate about algorithmic regulation is most advanced in the
area of
finance. Automated high-speed trading has potentially
destabilizing
effects on financial markets, so regulators have begun to
demand the
ability to modify these algorithms.
• The ongoing antitrust investigations into Google's ‘search

25. neutrality’
revolve around the same question: can regulators may require
access to
and modification of the search algorithm in the interest of the
public?
This approach is based on a contested assumption that it is
possible to
predict objectively how a certain algorithms will respond. Yet,
there is
simply no ‘right’ answer to how Google should rank its results.
Antitrust
agencies in the US and the EU have not yet found an regulatory
response to this issue.
In some cases direct regulation or complete and public
transparency might
be necessary. However, there is no one-size-fits-all regulatory
response.
More scrutiny of algorithms must be enabled, which requires
new practices
from industry and technologists. More consumer protection and
direct
regulation should be introduced where appropriate.

26. WHY DO ALGORITHMS RAISE ETHICAL
CONCERNS?
First, let's have a closer look at some of the critical features of
algorithms.
What are typical functions they perform? What are negative
impacts for
human rights? Here are some examples that probably affect you
too.
THEY KEEP INFORMATION AWAY FROM US
Increasingly, algorithms decide what gets attention, and what is
ignored;
and even what gets published at all, and what is censored. This
is true for
all kinds of search rankings, for example the way your social
media news-
feed looks. In other words, algorithms perform a gate-keeping
function.
EXAMPLE
Hiring algorithms decide if you are invited for an interview.
• Algorithms, rather than managers, are more and more taking
part in
hiring (and firing) of employees. Deciding who gets a job and
who does
not, is among the most powerful gate-keeping function in
society.

27. • Research shows that human managers display many different
biases in
hiring decisions, for example based on social class, race and
gender.
Clearly, human hiring systems are far from perfect.
• Nevertheless, we may not simply assume that algorithmic
hiring can
easily overcome human biases. Algorithms might work more
accurate
in some areas, but can also create new, sometimes unintended,
prob-
lems depending on how they are programmed and what input
data is
used.
Ethical implications: Algorithms work as gatekeepers that
influence how
we perceive the world, often without us realizing it. They
channel our
attention, which implies tremendous power.
THEY MAKE SUBJECTIVE DECISIONS
Some algorithms deal with questions, which do not have a clear
‘yes or no’

28. answer. Thus, they move a way from a checkbox answer “Is this
right or
wrong?” to more complex judgements, such as “What is
important? Who is
the right person for the job? Who is a threat to public safety?
Who should I
date?” Quietly, these types of subjective decisions previously
made by
humans are turned over to algorithms.
EXAMPLE
Predictive policing based on statistical forecasting:
• In early 2014, the Chicago Police Department made national
headlines
in the US for visiting residents who were considered to be most
likely
involved in violent crime. The selection of individuals, wo were
not
necessarily under investigation, was guided by a computer-
generated
“heat list” – an algorithm that seeks to predict future
involvement in
violent crime.
• A key concern about predictive policing is that such
automated systems

29. may create an echo chamber or a self-fulfilling prophecy. In
fact, heavy
policing of a specific area can increase the likelihood that crime
will be
detected. Since more police means more opportunities to
observe
residents' activities, the algorithm might just confirm its own
predic-
tion.
• Right now, police departments around the globe are testing
and imple-
menting predictive policing algorithms, but lack safeguards for
discrim-
inatory biases.
Ethical implications: Predictions made by algorithms provide
no guaran-
tee that they are right. And officials acting on incorrect
predictions may
even create unjustified or biased investigations.
5 | ETHICS OF ALGORITHMS ETHICS OF ALGORITHMS |
6
CORE ISSUES – SHOULD AN ALGORITHM
DECIDE YOUR FUTURE?

30. Without the help of algorithms, many present-day applications
would be
unusable. We need them to cope with the enormous amounts of
data we
produce every day. Algorithms make our lives easier and more
productive,
and we certainly don't want to lose those advantages. But we
need to be
aware of what they do and how they decide.
THEY CAN DISCRIMINATE AGAINST YOU,
JUST LIKE HUMANS
Computers are often regarded as objective and rational
machines. Howev-
er, algorithms are made by humans and can be just as biased.
We need to
be critical of the assumption, that algorithms can make “better”
decisions
than human beings. There are racist algorithms and sexist ones.
Algorithms
are not neutral, but rather they perpetuate the prejudices of their
creators.
Their creators, such as businesses or governments, can
potentially have
different goals in mind than the users would have.

31. THEY MUST BE KNOWN TO THE USER
Since algorithms make increasingly important decisions about
our lives,
users need to be informed about them. Knowledge about
automated
decision-making in everyday services is still very limited
among consumers.
Raising awareness should be at the heart of the debate about
ethics of
algorithms.
PUBLIC POLICY APPROACHES TO REGULATE
ALGORITHMS
How do you regulate a black box? We need to open a discussion
on how
policy-makers are trying to deal with ethical concerns around
algorithms.
There have been some attempts to provide algorithmic
accountability, but
we need better data and more in-depth studies.
If algorithms are written and used by corporations, it is
government
institutions like antitrust or consumer protection agencies, who
should

32. provide appropriate regulation and oversight. But who regulates
the use of
algorithms by the government itself? For cases like predictive
policing
ethical standards and legal safeguards are needed.
Recently, regulatory approaches to algorithms have circled
around trans-
parency, notification, and direct regulation. Yet, experience
shows that
policy-makers are facing certain dilemmas of regulation when it
comes to
algorithms.
TRANSPARENCY | MAKE OPAQUE BIASES VISIBLE
If you are faced with a complex and obscure algorithm, one
common
reaction is a demand for more transparency about what and how
it works.
The concern about black-box algorithms is that they make
inherently
subjective decisions, which might contain implicit or explicit
biases. At the
same time, making complex algorithms fully transparent can be
extremely

33. challenging:
• It is not enough to merely publish the source code of an
algorithm,
because machine-learning systems will inevitably make
decisions that
have not been programmed directly. Complete transparency
would
require that we are able to explain why any particular outcome
was
produced.
• Some investigations have reverse-engineered algorithms in
order to
create greater public awareness about them. That is one way
how the
public can perform a watchdog function.
• Often, there might be good reasons why complex algorithms
operate
opaquely, because public access would make them much more
vulnera-
ble to manipulation. If every company knew how Google ranks
its
search results, it could optimize their behavior and render the
ranking

34. algorithm useless.
NOTIFICATION | GIVING USERS THE RIGHT TO KNOW
A different form of transparency is to give consumers control
over their
personal information that feeds into algorithms. Notification
includes the
rights to correct that personal information and demand it be
excluded
from databases of data vendors. Regaining control over your
personal
information ensures accountability to the users.
DIRECT REGULATION | WHEN ALGORITHMS BECOME
CRITICAL INFRASTRUCTURE
• In some cases public regulators have been prone to create
ways to
manipulate algorithms directly. This is especially relevant for
core
infrastructure.
Debate about algorithmic regulation is most advanced in the
area of
finance. Automated high-speed trading has potentially
destabilizing
effects on financial markets, so regulators have begun to
demand the

35. ability to modify these algorithms.
• The ongoing antitrust investigations into Google's ‘search
neutrality’
revolve around the same question: can regulators may require
access to
and modification of the search algorithm in the interest of the
public?
This approach is based on a contested assumption that it is
possible to
predict objectively how a certain algorithms will respond. Yet,
there is
simply no ‘right’ answer to how Google should rank its results.
Antitrust
agencies in the US and the EU have not yet found an regulatory
response to this issue.
In some cases direct regulation or complete and public
transparency might
be necessary. However, there is no one-size-fits-all regulatory
response.
More scrutiny of algorithms must be enabled, which requires
new practices
from industry and technologists. More consumer protection and
direct

36. regulation should be introduced where appropriate.
WHY DO ALGORITHMS RAISE ETHICAL
CONCERNS?
First, let's have a closer look at some of the critical features of
algorithms.
What are typical functions they perform? What are negative
impacts for
human rights? Here are some examples that probably affect you
too.
THEY KEEP INFORMATION AWAY FROM US
Increasingly, algorithms decide what gets attention, and what is
ignored;
and even what gets published at all, and what is censored. This
is true for
all kinds of search rankings, for example the way your social
media news-
feed looks. In other words, algorithms perform a gate-keeping
function.
EXAMPLE
Hiring algorithms decide if you are invited for an interview.
• Algorithms, rather than managers, are more and more taking
part in
hiring (and firing) of employees. Deciding who gets a job and

37. who does
not, is among the most powerful gate-keeping function in
society.
• Research shows that human managers display many different
biases in
hiring decisions, for example based on social class, race and
gender.
Clearly, human hiring systems are far from perfect.
• Nevertheless, we may not simply assume that algorithmic
hiring can
easily overcome human biases. Algorithms might work more
accurate
in some areas, but can also create new, sometimes unintended,
prob-
lems depending on how they are programmed and what input
data is
used.
Ethical implications: Algorithms work as gatekeepers that
influence how
we perceive the world, often without us realizing it. They
channel our
attention, which implies tremendous power.
THEY MAKE SUBJECTIVE DECISIONS

38. Some algorithms deal with questions, which do not have a clear
‘yes or no’
answer. Thus, they move a way from a checkbox answer “Is this
right or
wrong?” to more complex judgements, such as “What is
important? Who is
the right person for the job? Who is a threat to public safety?
Who should I
date?” Quietly, these types of subjective decisions previously
made by
humans are turned over to algorithms.
EXAMPLE
Predictive policing based on statistical forecasting:
• In early 2014, the Chicago Police Department made national
headlines
in the US for visiting residents who were considered to be most
likely
involved in violent crime. The selection of individuals, wo were
not
necessarily under investigation, was guided by a computer-
generated
“heat list” – an algorithm that seeks to predict future
involvement in

39. violent crime.
• A key concern about predictive policing is that such
automated systems
may create an echo chamber or a self-fulfilling prophecy. In
fact, heavy
policing of a specific area can increase the likelihood that crime
will be
detected. Since more police means more opportunities to
observe
residents' activities, the algorithm might just confirm its own
predic-
tion.
• Right now, police departments around the globe are testing
and imple-
menting predictive policing algorithms, but lack safeguards for
discrim-
inatory biases.
Ethical implications: Predictions made by algorithms provide
no guaran-
tee that they are right. And officials acting on incorrect
predictions may
even create unjustified or biased investigations.
CORE ISSUES – SHOULD AN ALGORITHM

40. DECIDE YOUR FUTURE?
Without the help of algorithms, many present-day applications
would be
unusable. We need them to cope with the enormous amounts of
data we
produce every day. Algorithms make our lives easier and more
productive,
and we certainly don't want to lose those advantages. But we
need to be
aware of what they do and how they decide.
THEY CAN DISCRIMINATE AGAINST YOU,
JUST LIKE HUMANS
Computers are often regarded as objective and rational
machines. Howev-
er, algorithms are made by humans and can be just as biased.
We need to
be critical of the assumption, that algorithms can make “better”
decisions
than human beings. There are racist algorithms and sexist ones.
Algorithms
are not neutral, but rather they perpetuate the prejudices of their
creators.
Their creators, such as businesses or governments, can
potentially have

41. different goals in mind than the users would have.
THEY MUST BE KNOWN TO THE USER
Since algorithms make increasingly important decisions about
our lives,
users need to be informed about them. Knowledge about
automated
decision-making in everyday services is still very limited
among consumers.
Raising awareness should be at the heart of the debate about
ethics of
algorithms.
PUBLIC POLICY APPROACHES TO REGULATE
ALGORITHMS
How do you regulate a black box? We need to open a discussion
on how
policy-makers are trying to deal with ethical concerns around
algorithms.
There have been some attempts to provide algorithmic
accountability, but
we need better data and more in-depth studies.
If algorithms are written and used by corporations, it is
government
institutions like antitrust or consumer protection agencies, who
should

42. provide appropriate regulation and oversight. But who regulates
the use of
algorithms by the government itself? For cases like predictive
policing
ethical standards and legal safeguards are needed.
Recently, regulatory approaches to algorithms have circled
around trans-
parency, notification, and direct regulation. Yet, experience
shows that
policy-makers are facing certain dilemmas of regulation when it
comes to
algorithms.
TRANSPARENCY | MAKE OPAQUE BIASES VISIBLE
If you are faced with a complex and obscure algorithm, one
common
reaction is a demand for more transparency about what and how
it works.
The concern about black-box algorithms is that they make
inherently
subjective decisions, which might contain implicit or explicit
biases. At the
same time, making complex algorithms fully transparent can be
extremely

43. challenging:
• It is not enough to merely publish the source code of an
algorithm,
because machine-learning systems will inevitably make
decisions that
have not been programmed directly. Complete transparency
would
require that we are able to explain why any particular outcome
was
produced.
• Some investigations have reverse-engineered algorithms in
order to
create greater public awareness about them. That is one way
how the
public can perform a watchdog function.
• Often, there might be good reasons why complex algorithms
operate
opaquely, because public access would make them much more
vulnera-
ble to manipulation. If every company knew how Google ranks
its
search results, it could optimize their behavior and render the
ranking

44. algorithm useless.
NOTIFICATION | GIVING USERS THE RIGHT TO KNOW
A different form of transparency is to give consumers control
over their
personal information that feeds into algorithms. Notification
includes the
rights to correct that personal information and demand it be
excluded
from databases of data vendors. Regaining control over your
personal
information ensures accountability to the users.
DIRECT REGULATION | WHEN ALGORITHMS BECOME
CRITICAL INFRASTRUCTURE
• In some cases public regulators have been prone to create
ways to
manipulate algorithms directly. This is especially relevant for
core
infrastructure.
Debate about algorithmic regulation is most advanced in the
area of
finance. Automated high-speed trading has potentially
destabilizing
effects on financial markets, so regulators have begun to

45. demand the
ability to modify these algorithms.
• The ongoing antitrust investigations into Google's ‘search
neutrality’
revolve around the same question: can regulators may require
access to
and modification of the search algorithm in the interest of the
public?
This approach is based on a contested assumption that it is
possible to
predict objectively how a certain algorithms will respond. Yet,
there is
simply no ‘right’ answer to how Google should rank its results.
Antitrust
agencies in the US and the EU have not yet found an regulatory
response to this issue.
In some cases direct regulation or complete and public
transparency might
be necessary. However, there is no one-size-fits-all regulatory
response.
More scrutiny of algorithms must be enabled, which requires
new practices
from industry and technologists. More consumer protection and

46. direct
regulation should be introduced where appropriate.
7 | ETHICS OF ALGORITHMS ETHICS OF ALGORITHMS |
8
EDS 1021 Week 6 Interactive Assignment
Radiometric Dating
Objective: Using a simulated practical application, explore the
concepts of radioactive decay and the half-life of
radioactive elements, and then apply the concept of radiometric
dating to estimate the age of various objects.
Background: Review the topics Half-Life, Radiometric Dating,
and Decay Chains in Chapter 12 of The Sciences.
Instructions:
1. PRINT a hard copy of this entire document, so that the
experiment instructions may be easily referred to,
and the data tables and questions (on the last three pages) can
be completed as a rough draft.
2. Download the Radiometric Dating Game Answer Sheet from
the course website. Transfer your data
values and question answers from the completed rough draft to

47. the answer sheet. Be sure to put your
NAME on the answer sheet where indicated. Save your
completed answer sheet on your computer.
3. SUBMIT ONLY the completed answer sheet, by uploading
your file to the digital drop box for the
assignment.
Introduction to the Simulation
1. After reviewing the background information for this
assignment, go to the website for the interactive
simulation “Radioactive Dating Game” at
http://phet.colorado.edu/en/simulation/radioactive-dating-game.
Click on DOWNLOAD to run the simulation locally on your
computer.
2. Software Requirements: You must have the latest version of
Java software (free) loaded on your computer
to run the simulation. If you do not or are not sure if you have
the latest version, go to
http://www.java.com/en/download/index.jsp .
3. Explore and experiment on the 4 different “tabs” (areas) of
the simulation. While playing around, think about
how the concepts of radioactive decay are being illustrated in
the simulation.

48. Half Life Tab – observe a sample of radioactive atoms decaying
- Carbon-14, Uranium-238, or ? (a custom-
made radioactive atom). Clicking on the “add 10” button adds
10 atoms at a time to the “decay area”. There
are a total of 100 atoms in the bucket, so clicking the “add 10”
button 10 times will empty the bucket into the
decay area. Observe the pie chart and time graph as atoms
decay. You can PAUSE, STEP (buttons at the
bottom of the screen) the simulation in time as atoms are
decaying, and RESET the simulation.
Decay Rates Tab – Similar to the half-life tab, but different!
Atom choices are carbon-14 and uranium-238.
The bucket has a total of 1000 atoms. Drag the slide bar on the
bucket to the right to increase the number
of atoms added to the decay area. Observe the pie chart and
time graph as atoms decay. Note that the
graph for the Decay Rates tab provides different information
than the graph for the Half Life tab. You can
PAUSE, STEP (buttons at the bottom of the screen) the
simulation in time as atoms are decaying, and
RESET the simulation.

49. Measurement Tab – Use a probe to virtually measure
radioactive decay within an object - a tree or a
volcanic rock. The probe can be set to detect either the decay of
carbon-14 atoms, or the decay of uranium-
238 atoms. Follow prompts on the screen to run a simulation of
a tree growing and dying, or of a volcano
erupting and creating a rock, and then measuring the decay of
atoms within each object.
Dating Game Tab – Use a probe to virtually measure the
percentage of radioactive atoms remaining within
various objects and, knowing the half-life of radioactive
elements being detected, estimate the ages of
objects. The probe can be set to either detect carbon-14,
uranium-238, or other “mystery” elements, as
appropriate for determining the age of the object. Drag the
probe over an object, select which element to
measure, and then slide the arrow on the graph to match the
percentage of atoms measured by the probe.
The time (t) shown for the matching percentage can then be
entered as the estimate in years of the object’s
age.
After playing around with the simulation, conduct the following
four (4) short experiments. As you conduct

50. the experiments and collect data, fill in the data tables and
answer the questions on the last three pages of
this document.
http://phet.colorado.edu/en/simulation/radioactive-dating-game
http://www.java.com/en/download/index.jsp
http://www.java.com/en/download/index.jsp
Experiment 1: Half Life
1. Click on the Half Life tab at the top of the simulation screen.
2. Procedure:
Part I - Carbon-14
a. Click the blue “Pause” button at the bottom of the screen
(i.e., set it so that it shows the “play” arrow).
Click the “Add 10” button below the “Bucket o’ Atoms”
repeatedly, until there are no more atoms left in
the bucket. There are now 100 carbon-14 atoms in the decay
area.
b. The half-life of carbon-14 is about 5700 years. Based on the
definition of half-life, if you left these 100
carbon-14 atoms to sit around for 5700 years, what is your
prediction of how many carbon-14 atoms will
decay into the stable element nitrogen-14 during that time?
Write your prediction in the “prediction”

51. column for the row labeled “carbon-14”, in data table 1.
c. Click the blue “Play” arrow at the bottom of the screen. As
the simulation runs, carefully observe what
is happening to the carbon-14 atoms in the decay area, and the
graphs at the top of the screen (both
the pie chart and the time graph). Once all atoms have decayed
into the stable isotope nitrogen-14,
click the blue “Pause” button at the bottom of the screen (i.e.,
set it so that it shows the “play” arrow),
and “Reset All Nuclei” button in the decay area.
d. Repeat step c. until you have a good idea of what is going on
in this simulation.
e. Repeat step c. again, but this time, watch the graph at the top
of the window carefully, and click
“pause” when TIME reaches 5700 years (when the carbon-14
atom moving across the graph reaches
the red dashed line labeled HALF LIFE on the TIME graph).
f. If you do NOT pause the simulation on or very close to the
red dashed line, click the “Reset All Nuclei”
button and repeat step e.
g. Once you have paused the simulation in the correct spot, look
at the pie graph and determine the
number of nuclei that have decayed into nitrogen-14 at Time =
HALF LIFE. Write this number in data
table 1, in the row labeled “carbon-14”, under “trial 1”.
h. Click the “Reset All Nuclei” button in the decay area.
i. Repeat steps e through h for two more trials. For each trial,
write down in data table 1, the number of

52. nuclei that have decayed into nitrogen-14 at Time = HALF
LIFE, in the row labeled “carbon-14”, under
“trial 2” and “trial 3” respectively.
Part II – Uranium-238
a. Click “reset all” on the right side of the screen in the Decay
Isotope box, and click “yes” in the box that
pops up. Click on the radio button for uranium-238 in the Decay
Isotope box. Click the blue “Pause”
button at the bottom of the screen (i.e., set it so that it shows
the “play” arrow). Click the “Add 10”
button below the “Bucket o’ Atoms” repeatedly, until there are
no more atoms left in the bucket. There
are now 100 uranium-238 atoms in the decay area.
b. The half-life of uranium-238 is 4.5 billion years!* Based on
the definition of half-life, if you left these 100
uranium-238 atoms to sit around for 4.5 billion years, what is
your prediction of how many uranium
atoms will decay into lead-206 during that time? Write your
prediction in the “prediction” column for the
second row, labeled “uranium-238”, in data table 1.
c. Click the “Play” button at the bottom of the window. Watch
the graph at the top of the window
carefully, and click “pause” when the time reaches 4.5 billion
years (when the uranium-238 atom
moving across the graph reaches the red dashed line labeled
HALF LIFE on the time graph).
d. If you don’t pause the simulation on or very close to the red
line, click the “Reset All Nuclei” button and

53. repeat step c.
e. Once you have paused the simulation in the correct spot, look
at the pie graph and determine the
number of nuclei that have decayed into lead-206 at Time =
HALF LIFE. Write this number in data table
1, in the row labeled “uranium-238”, under “trial 1”.
f. Click the “Reset All Nuclei” button in the decay area.
g. Repeat steps c through e for two more trials. For each trial,
write down in data table 1, the number of
nuclei that have decayed into lead-206 at Time = HALF LIFE,
in the row labeled “uranium-238”, under
“trial 2” and “trial 3” respectively.
3. Calculate the average number of atoms that decayed for all
three trials of carbon-14 decay. Do the same for
all three trials uranium-238 decay. Write the average values for
each element under “averages”, the last
column in data table 1.
4. Answer the five (5) questions for Experiment 1 on the
Questions page.
* Unlike Carbon-14, which undergoes only one radioactive
decay to reach the stable nitrogen-14, uranium-238 undergoes
MANY decays into many intermediate unstable elements before
finally getting to the stable element lead-206 (see the decay
chain for uranium-238 in chapter 12 for details).

54. Experiment 2: Decay Rates
1. Set Up: Click on the Decay Rates tab at the top of the
simulation screen.
2. Procedure
Part I – Carbon-14
a. In the Choose Isotope area on the right side of the screen,
click the button next to carbon-14. Recall
that carbon-14 has a half-life of about 5700 years.
b. Drag the slide bar on the bucket of atoms all the way to the
right. This will put 1,000 radioactive atoms
into the decay area. When you let go of the slide bar, the
simulation will start right away. If you missed
seeing the simulation run from the start, start it over again by
clicking “Reset All Nuclei”, and keep your
eyes on the screen. Watch the graph at the bottom of the screen
as you allow the simulation to run until
all atoms have decayed.
c. Carefully interpret the graph to obtain the data requested to
fill in data table 2 for “carbon-14”.
Part II – Uranium-238

55. a. In the Choose Isotope area on the right side of the screen,
click the button next to uranium-238.
Recall that the half-life of uranium-238 is about 4.5 billion
years.
b. Repeat step b. of part I for uranium-238.
c. Carefully interpret the graph to obtain the data requested to
fill in data table 2 for “uranium-238”.
3. Answer the two (2) questions for Experiment 2 on the
Questions page.
Experiment 3: Measurement
1. Set Up: Click on the Measurement tab at the top of the
simulation screen.
2. Procedure
a. In Choose an Object, click on the button for Tree. In the
Probe Type box, click on the buttons for
“carbon-14”, and “Objects”.
b. Click “Plant Tree” at the bottom of the screen. Observe that
the tree grows and lives for about 1200
years, then die and begin to decay. As the simulation runs,
observe the probe reading (upper left box)
and graph (upper right box) at the top of the screen. The graph
is plotting the probe’s measurement of
the percentage of carbon-14 remaining in the tree over time.

56. c. On the right side of the screen, in the “Choose an Object”
box, click the reset button. Set the probe to
measure uranium-238 instead of carbon-14 (in the Probe Type
box, click on the button for “uranium-
238”). Click “Plant Tree” and again observe the probe reading
and graph. This time, the graph will plot
the probe’s measurement of uranium-238 in the tree.
d. On the right side of the screen, in the “Choose an Object”
box, click the reset button and click the
button for Rock. Keep the probe type set on “uranium-238”.
e. Click “Erupt Volcano” and observe the volcano creating and
ejecting HOT igneous rocks. As the
simulation runs, observe the probe reading (upper left box) and
graph (upper right box) at the top of the
screen. The graph is plotting the probe’s measurement of
uranium-238 in the rock over time.
f. On the right side of the screen, in the “Choose an Object”
box, click the reset button. Set the probe to
measure carbon-14 instead of uranium-238 (in the Probe Type
box, click on the button for “carbon-14”).
Click “erupt volcano” and again observe the probe reading and
graph. This time, the graph will plot the
probe’s measurement of carbon-14 in the rock.
3. Answer the six (6) questions for Experiment 3 on the
Questions page. Repeat the Measurement
experiments as necessary to answer the questions.

57. Experiment 4: Dating Game
1. Set Up: Click on the Dating Game tab at the top of the
simulation screen. Verify that the “objects” button is
clicked below the Probe Type box.
2. Procedure
a. Select an item either at or below the Earth’s surface to
determine the age of. Set the Probe Type to
either carbon-14 or uranium-238, as appropriate for the item
you are measuring, based on your findings
in experiment 3.
b. Drag the probe directly over the item you chose. With the
probe over the item, the box in the upper left,
above “Probe Type”, shows the percentage of element that
remains in the item.
c. Drag the green arrow on the graph to the right or left, until
the percentage in the box on the graph
matches the percentage of element in the object. Once you have
the arrow positioned correctly, enter,
in the “estimate” box, the time (t) shown, as the estimate for the
age of the rock or fossil. Click “Check
Estimate” to see if the estimate is correct.
d. Example:

58. 1) Drag the probe over the dead tree to the right of the house.
Since this is a once-living thing, set the
Probe Type to Carbon-14.
2) Look at the probe reading: it shows that there is 97.4% of
carbon-14 remaining in the dead tree.
3) Drag the green arrows on the graph at the top of the screen to
the right or left until the top line tells
you that the carbon-14 percentage is 97.4%, matching the
reading from the probe.
4) When the graph reads 97.4%, it shows that time (t) equals
220 years.
5) Type “220” into the box that says “Estimate age of dead
tree:” and click “Check Estimate”.
6) You should see a green smiley face, indicating that you have
correctly figured out the age of the
dead tree, 220 years. This means that the tree died 220 years
ago.
e. Repeat step c. for all the other items on the Dating Game
screen. Fill in data table 3.
Hints:
*When entering the estimate for the age of an object, you
MUST enter it in YEARS. So, if t = 134.8 MY
on the graph, which means 134.8 million years, you would enter
the number 134,800,000 as the
estimate in years for the age of a rock. The estimates entered

59. do not have to be EXACT to be
registered as correct, but must be CLOSE. For example, if
134,000,000 were entered for the above
example, the simulation would return a green smiley face for a
correct estimate.
**For the last four items on the list, neither carbon-14 nor
uranium-238 will work to determine the item’s
age. Select “Custom”, and pick a half-life from the drop-down
menu that allows the probe to detect
something (i.e., a reading other than 0.0%). All of the “custom”
elements are, like carbon-14, elements
that would be found in living or once living things.
3. Answer the (1) question for Experiment 4 on the Questions
page.
Name:
Data
Each box to be filled in with a value is worth 1 point.
Data Table 1

60. Radioactive
Element
Number of atoms in the
sample at Time = 0
Prediction of # atoms
that will decay when
time reaches one half-
life
Number of atoms that
have decayed when
Time = Half Life
Average number of
atoms that have
decayed when
Time = Half Life Trial
#1
Trial
#2

61. Trial
#3
Carbon-14 100
Uranium-238 100
Data Table 2
Radioactive
Element
Percentage of the original element remaining after:
Percentage of the decay product present after:
1 half-life 2 half-lives 3 half-lives 1 half-life 2 half-lives
3 half-lives
Carbon-14
Uranium-238
Data Table 3

62. Item Age Element Used
Animal Skull
House
Living Tree
Dead Tree
Bone
Wooden Cup
Large Human Skull
Fish Bones
Rock 1
Rock 3
Rock 5

63. Fish Fossil*
Dinosaur Skull*
Trilobite*
Small Human Skull*
Questions
For multiple choice questions, choose the letter of the best
answer choice from the list below the question.
For short answer questions, type your answer in the space
provided below the question.
Note that there are TWO PAGES OF QUESTIONS below. Each
multiple question is worth 3 points and each
short answer question is worth 5 points.
Experiment 1
1. In 1 or 2 sentences, describe, in the space below, what you
observed happening to the atoms in the decay area

64. when the simulation is in “play” mode.
2. In this simulation (and in nature), carbon-14 is radioactively
decays to nitrogen-14. What type of radioactive
decay is carbon-14 undergoing?
a. alpha decay b. beta decay c. gamma decay
3. In data table 1, compare your calculations for the average
number of atoms that decayed to your predictions for
how many atoms would decay. How did your predictions
compare to the averages?
a. Close b. Exact c. Nowhere Close
4. Which of the following statements best describes the decay of
a sample of radioactive atoms?
a. A statistical process, where the sample as a whole decays
predictably, but individual atoms in the sample
decay like popcorn kernels popping.
b. An exact process where the time of decay of each atom in the
sample can be predicted.
c. A completely random process that is in no way predictable.

65. 5. Based on your observations, the data you collected, and the
comparison of the calculated averages to
predictions in experiment 1, write in the space below, IN YOUR
OWN WORDS, a definition of “half-life”. Phrase
the definition in a way that you would explain the concept to
one of your classmates.
Experiment 2
1. Suppose you found a rock. Using radiometric dating, you
determined that the rock contained the
same percentage of lead-206 and uranium-238. How old would
you conclude the rock to be?
a. 2.25 billion years old b. 4.5 billion years old c. 9 billion
years old
2. Using the data in data table 2, if the original sample
contained 24 grams of carbon-14, how many
grams of carbon-14 would be left, and how many grams of
nitrogen-14 would be present, after 2
half-lives?
a. 12; 12 b. 6; 18 c. 2; 22

66. Experiment 3
1. When half of the carbon-14 atoms in the tree have decayed
into nitrogen-14, it has been approximately 5700
years since the tree _____.
a. was planted b. died
2. The probe measures that the percentage of carbon-14 in the
tree is 100% while the tree is alive, and then
measures the percentage of carbon-14 decreasing with time after
the tree dies. This means that radiometric
dating using carbon-14 is applicable to:
a. living objects. B. once-living objects c. both living and
once-living objects.
3. When half of the Uranium-238 in the rock has decayed,
______ years have passed since the volcano erupted.
a. 4.5 billion years b. 5700 years

67. 4. In step c of experiment 3, the probe was used to measure
uranium-238 in the tree. In step f of experiment 3, the
probe was used to measure carbon-14 in the rock. In both steps
(c and f), the probe reading was _____.
a. 100%, then decreasing with time. b. zero (0) . c. decreasing
with time.
5. Uranium-238 must be used to measure the age of the rock,
and not carbon-14, because:
a. The isotope carbon-14 did not exist when the rock was
created.
b. rocks do not contain carbon-14, and even if they did, the
carbon-14 would have likely decayed away
prior to measuring the rock’s age.
c. it is easier to measure uranium in rocks than it is carbon.
6. Based on your observations for experiment 3, and the answers
to your questions above, _______ should be
used to determine the ages of once-living things, and _______
should be used to determine the ages of rocks.
a. carbon-14; uranium-238 b. uranium-238; carbon-14 c.

68. either can be used for both objects.
Experiment 4
1. Briefly explain why neither carbon-14 nor uranium-238 could
be used to determine the ages of the last 4 items in
data table 3.
EDS 1021 Week 6 Interactive Assignment
Radiometric Dating
Objective: Using a simulated practical application, explore the
concepts of radioactive decay and the half-life of
radioactive elements, and then apply the concept of radiometric
dating to estimate the age of various objects.
Background: Review the topics Half-Life, Radiometric Dating,
and Decay Chains in Chapter 12 of The Sciences.
Instructions:
1. PRINT a hard copy of this entire document, so that the
experiment instructions may be easily referred to,

69. and the data tables and questions (on the last three pages) can
be completed as a rough draft.
2. Download the Radiometric Dating Game Answer Sheet from
the course website. Transfer your data
values and question answers from the completed rough draft to
the answer sheet. Be sure to put your
NAME on the answer sheet where indicated. Save your
completed answer sheet on your computer.
3. SUBMIT ONLY the completed answer sheet, by uploading
your file to the digital drop box for the
assignment.
Introduction to the Simulation
1. After reviewing the background information for this
assignment, go to the website for the interactive
simulation “Radioactive Dating Game” at
http://phet.colorado.edu/en/simulation/radioactive-dating-game.
Click on DOWNLOAD to run the simulation locally on your
computer.
2. Software Requirements: You must have the latest version of
Java software (free) loaded on your computer
to run the simulation. If you do not or are not sure if you have
the latest version, go to
http://www.java.com/en/download/index.jsp .

70. 3. Explore and experiment on the 4 different “tabs” (areas) of
the simulation. While playing around, think about
how the concepts of radioactive decay are being illustrated in
the simulation.
Half Life Tab – observe a sample of radioactive atoms decaying
- Carbon-14, Uranium-238, or ? (a custom-
made radioactive atom). Clicking on the “add 10” button adds
10 atoms at a time to the “decay area”. There
are a total of 100 atoms in the bucket, so clicking the “add 10”
button 10 times will empty the bucket into the
decay area. Observe the pie chart and time graph as atoms
decay. You can PAUSE, STEP (buttons at the
bottom of the screen) the simulation in time as atoms are
decaying, and RESET the simulation.
Decay Rates Tab – Similar to the half-life tab, but different!
Atom choices are carbon-14 and uranium-238.
The bucket has a total of 1000 atoms. Drag the slide bar on the
bucket to the right to increase the number
of atoms added to the decay area. Observe the pie chart and
time graph as atoms decay. Note that the
graph for the Decay Rates tab provides different information
than the graph for the Half Life tab. You can

71. PAUSE, STEP (buttons at the bottom of the screen) the
simulation in time as atoms are decaying, and
RESET the simulation.
Measurement Tab – Use a probe to virtually measure
radioactive decay within an object - a tree or a
volcanic rock. The probe can be set to detect either the decay of
carbon-14 atoms, or the decay of uranium-
238 atoms. Follow prompts on the screen to run a simulation of
a tree growing and dying, or of a volcano
erupting and creating a rock, and then measuring the decay of
atoms within each object.
Dating Game Tab – Use a probe to virtually measure the
percentage of radioactive atoms remaining within
various objects and, knowing the half-life of radioactive
elements being detected, estimate the ages of
objects. The probe can be set to either detect carbon-14,
uranium-238, or other “mystery” elements, as
appropriate for determining the age of the object. Drag the
probe over an object, select which element to
measure, and then slide the arrow on the graph to match the
percentage of atoms measured by the probe.
The time (t) shown for the matching percentage can then be

72. entered as the estimate in years of the object’s
age.
After playing around with the simulation, conduct the following
four (4) short experiments. As you conduct
the experiments and collect data, fill in the data tables and
answer the questions on the last three pages of
this document.
http://phet.colorado.edu/en/simulation/radioactive-dating-game
http://www.java.com/en/download/index.jsp
http://www.java.com/en/download/index.jsp
Experiment 1: Half Life
1. Click on the Half Life tab at the top of the simulation screen.
2. Procedure:
Part I - Carbon-14
a. Click the blue “Pause” button at the bottom of the screen
(i.e., set it so that it shows the “play” arrow).
Click the “Add 10” button below the “Bucket o’ Atoms”
repeatedly, until there are no more atoms left in
the bucket. There are now 100 carbon-14 atoms in the decay
area.

73. b. The half-life of carbon-14 is about 5700 years. Based on the
definition of half-life, if you left these 100
carbon-14 atoms to sit around for 5700 years, what is your
prediction of how many carbon-14 atoms will
decay into the stable element nitrogen-14 during that time?
Write your prediction in the “prediction”
column for the row labeled “carbon-14”, in data table 1.
c. Click the blue “Play” arrow at the bottom of the screen. As
the simulation runs, carefully observe what
is happening to the carbon-14 atoms in the decay area, and the
graphs at the top of the screen (both
the pie chart and the time graph). Once all atoms have decayed
into the stable isotope nitrogen-14,
click the blue “Pause” button at the bottom of the screen (i.e.,
set it so that it shows the “play” arrow),
and “Reset All Nuclei” button in the decay area.
d. Repeat step c. until you have a good idea of what is going on
in this simulation.
e. Repeat step c. again, but this time, watch the graph at the top
of the window carefully, and click
“pause” when TIME reaches 5700 years (when the carbon-14
atom moving across the graph reaches
the red dashed line labeled HALF LIFE on the TIME graph).
f. If you do NOT pause the simulation on or very close to the
red dashed line, click the “Reset All Nuclei”
button and repeat step e.
g. Once you have paused the simulation in the correct spot, look
at the pie graph and determine the
number of nuclei that have decayed into nitrogen-14 at Time =

74. HALF LIFE. Write this number in data
table 1, in the row labeled “carbon-14”, under “trial 1”.
h. Click the “Reset All Nuclei” button in the decay area.
i. Repeat steps e through h for two more trials. For each trial,
write down in data table 1, the number of
nuclei that have decayed into nitrogen-14 at Time = HALF
LIFE, in the row labeled “carbon-14”, under
“trial 2” and “trial 3” respectively.
Part II – Uranium-238
a. Click “reset all” on the right side of the screen in the Decay
Isotope box, and click “yes” in the box that
pops up. Click on the radio button for uranium-238 in the Decay
Isotope box. Click the blue “Pause”
button at the bottom of the screen (i.e., set it so that it shows
the “play” arrow). Click the “Add 10”
button below the “Bucket o’ Atoms” repeatedly, until there are
no more atoms left in the bucket. There
are now 100 uranium-238 atoms in the decay area.
b. The half-life of uranium-238 is 4.5 billion years!* Based on
the definition of half-life, if you left these 100
uranium-238 atoms to sit around for 4.5 billion years, what is
your prediction of how many uranium
atoms will decay into lead-206 during that time? Write your
prediction in the “prediction” column for the
second row, labeled “uranium-238”, in data table 1.
c. Click the “Play” button at the bottom of the window. Watch
the graph at the top of the window

75. carefully, and click “pause” when the time reaches 4.5 billion
years (when the uranium-238 atom
moving across the graph reaches the red dashed line labeled
HALF LIFE on the time graph).
d. If you don’t pause the simulation on or very close to the red
line, click the “Reset All Nuclei” button and
repeat step c.
e. Once you have paused the simulation in the correct spot, look
at the pie graph and determine the
number of nuclei that have decayed into lead-206 at Time =
HALF LIFE. Write this number in data table
1, in the row labeled “uranium-238”, under “trial 1”.
f. Click the “Reset All Nuclei” button in the decay area.
g. Repeat steps c through e for two more trials. For each trial,
write down in data table 1, the number of
nuclei that have decayed into lead-206 at Time = HALF LIFE,
in the row labeled “uranium-238”, under
“trial 2” and “trial 3” respectively.
3. Calculate the average number of atoms that decayed for all
three trials of carbon-14 decay. Do the same for
all three trials uranium-238 decay. Write the average values for
each element under “averages”, the last
column in data table 1.
4. Answer the five (5) questions for Experiment 1 on the
Questions page.

76. * Unlike Carbon-14, which undergoes only one radioactive
decay to reach the stable nitrogen-14, uranium-238 undergoes
MANY decays into many intermediate unstable elements before
finally getting to the stable element lead-206 (see the decay
chain for uranium-238 in chapter 12 for details).
Experiment 2: Decay Rates
1. Set Up: Click on the Decay Rates tab at the top of the
simulation screen.
2. Procedure
Part I – Carbon-14
a. In the Choose Isotope area on the right side of the screen,
click the button next to carbon-14. Recall
that carbon-14 has a half-life of about 5700 years.
b. Drag the slide bar on the bucket of atoms all the way to the
right. This will put 1,000 radioactive atoms
into the decay area. When you let go of the slide bar, the
simulation will start right away. If you missed
seeing the simulation run from the start, start it over again by
clicking “Reset All Nuclei”, and keep your
eyes on the screen. Watch the graph at the bottom of the screen
as you allow the simulation to run until
all atoms have decayed.

77. c. Carefully interpret the graph to obtain the data requested to
fill in data table 2 for “carbon-14”.
Part II – Uranium-238
a. In the Choose Isotope area on the right side of the screen,
click the button next to uranium-238.
Recall that the half-life of uranium-238 is about 4.5 billion
years.
b. Repeat step b. of part I for uranium-238.
c. Carefully interpret the graph to obtain the data requested to
fill in data table 2 for “uranium-238”.
3. Answer the two (2) questions for Experiment 2 on the
Questions page.
Experiment 3: Measurement
1. Set Up: Click on the Measurement tab at the top of the
simulation screen.
2. Procedure
a. In Choose an Object, click on the button for Tree. In the
Probe Type box, click on the buttons for
“carbon-14”, and “Objects”.
b. Click “Plant Tree” at the bottom of the screen. Observe that
the tree grows and lives for about 1200

78. years, then die and begin to decay. As the simulation runs,
observe the probe reading (upper left box)
and graph (upper right box) at the top of the screen. The graph
is plotting the probe’s measurement of
the percentage of carbon-14 remaining in the tree over time.
c. On the right side of the screen, in the “Choose an Object”
box, click the reset button. Set the probe to
measure uranium-238 instead of carbon-14 (in the Probe Type
box, click on the button for “uranium-
238”). Click “Plant Tree” and again observe the probe reading
and graph. This time, the graph will plot
the probe’s measurement of uranium-238 in the tree.
d. On the right side of the screen, in the “Choose an Object”
box, click the reset button and click the
button for Rock. Keep the probe type set on “uranium-238”.
e. Click “Erupt Volcano” and observe the volcano creating and
ejecting HOT igneous rocks. As the
simulation runs, observe the probe reading (upper left box) and
graph (upper right box) at the top of the
screen. The graph is plotting the probe’s measurement of
uranium-238 in the rock over time.
f. On the right side of the screen, in the “Choose an Object”
box, click the reset button. Set the probe to
measure carbon-14 instead of uranium-238 (in the Probe Type
box, click on the button for “carbon-14”).
Click “erupt volcano” and again observe the probe reading and
graph. This time, the graph will plot the
probe’s measurement of carbon-14 in the rock.

79. 3. Answer the six (6) questions for Experiment 3 on the
Questions page. Repeat the Measurement
experiments as necessary to answer the questions.
Experiment 4: Dating Game
1. Set Up: Click on the Dating Game tab at the top of the
simulation screen. Verify that the “objects” button is
clicked below the Probe Type box.
2. Procedure
a. Select an item either at or below the Earth’s surface to
determine the age of. Set the Probe Type to
either carbon-14 or uranium-238, as appropriate for the item
you are measuring, based on your findings
in experiment 3.
b. Drag the probe directly over the item you chose. With the
probe over the item, the box in the upper left,
above “Probe Type”, shows the percentage of element that
remains in the item.
c. Drag the green arrow on the graph to the right or left, until
the percentage in the box on the graph

80. matches the percentage of element in the object. Once you have
the arrow positioned correctly, enter,
in the “estimate” box, the time (t) shown, as the estimate for the
age of the rock or fossil. Click “Check
Estimate” to see if the estimate is correct.
d. Example:
1) Drag the probe over the dead tree to the right of the house.
Since this is a once-living thing, set the
Probe Type to Carbon-14.
2) Look at the probe reading: it shows that there is 97.4% of
carbon-14 remaining in the dead tree.
3) Drag the green arrows on the graph at the top of the screen to
the right or left until the top line tells
you that the carbon-14 percentage is 97.4%, matching the
reading from the probe.
4) When the graph reads 97.4%, it shows that time (t) equals
220 years.
5) Type “220” into the box that says “Estimate age of dead
tree:” and click “Check Estimate”.
6) You should see a green smiley face, indicating that you have
correctly figured out the age of the
dead tree, 220 years. This means that the tree died 220 years
ago.
e. Repeat step c. for all the other items on the Dating Game
screen. Fill in data table 3.
Hints:

81. *When entering the estimate for the age of an object, you
MUST enter it in YEARS. So, if t = 134.8 MY
on the graph, which means 134.8 million years, you would enter
the number 134,800,000 as the
estimate in years for the age of a rock. The estimates entered
do not have to be EXACT to be
registered as correct, but must be CLOSE. For example, if
134,000,000 were entered for the above
example, the simulation would return a green smiley face for a
correct estimate.
**For the last four items on the list, neither carbon-14 nor
uranium-238 will work to determine the item’s
age. Select “Custom”, and pick a half-life from the drop-down
menu that allows the probe to detect
something (i.e., a reading other than 0.0%). All of the “custom”
elements are, like carbon-14, elements
that would be found in living or once living things.
3. Answer the (1) question for Experiment 4 on the Questions
page.
Name:
Data

82. Each box to be filled in with a value is worth 1 point.
Data Table 1
Radioactive
Element
Number of atoms in the
sample at Time = 0
Prediction of # atoms
that will decay when
time reaches one half-
life
Number of atoms that
have decayed when
Time = Half Life
Average number of
atoms that have
decayed when
Time = Half Life Trial

83. #1
Trial
#2
Trial
#3
Carbon-14 100
Uranium-238 100
Data Table 2
Radioactive
Element
Percentage of the original element remaining after:
Percentage of the decay product present after:
1 half-life 2 half-lives 3 half-lives 1 half-life 2 half-lives
3 half-lives
Carbon-14

84. Uranium-238
Data Table 3
Item Age Element Used
Animal Skull
House
Living Tree
Dead Tree
Bone
Wooden Cup
Large Human Skull
Fish Bones
Rock 1

85. Rock 3
Rock 5
Fish Fossil*
Dinosaur Skull*
Trilobite*
Small Human Skull*
Questions
For multiple choice questions, choose the letter of the best
answer choice from the list below the question.
For short answer questions, type your answer in the space
provided below the question.
Note that there are TWO PAGES OF QUESTIONS below. Each
multiple question is worth 3 points and each
short answer question is worth 5 points.

86. Experiment 1
1. In 1 or 2 sentences, describe, in the space below, what you
observed happening to the atoms in the decay area
when the simulation is in “play” mode.
2. In this simulation (and in nature), carbon-14 is radioactively
decays to nitrogen-14. What type of radioactive
decay is carbon-14 undergoing?
a. alpha decay b. beta decay c. gamma decay
3. In data table 1, compare your calculations for the average
number of atoms that decayed to your predictions for
how many atoms would decay. How did your predictions
compare to the averages?
a. Close b. Exact c. Nowhere Close
4. Which of the following statements best describes the decay of
a sample of radioactive atoms?
a. A statistical process, where the sample as a whole decays
predictably, but individual atoms in the sample

87. decay like popcorn kernels popping.
b. An exact process where the time of decay of each atom in the
sample can be predicted.
c. A completely random process that is in no way predictable.
5. Based on your observations, the data you collected, and the
comparison of the calculated averages to
predictions in experiment 1, write in the space below, IN YOUR
OWN WORDS, a definition of “half-life”. Phrase
the definition in a way that you would explain the concept to
one of your classmates.
Experiment 2
1. Suppose you found a rock. Using radiometric dating, you
determined that the rock contained the
same percentage of lead-206 and uranium-238. How old would
you conclude the rock to be?
a. 2.25 billion years old b. 4.5 billion years old c. 9 billion
years old
2. Using the data in data table 2, if the original sample
contained 24 grams of carbon-14, how many
grams of carbon-14 would be left, and how many grams of

88. nitrogen-14 would be present, after 2
half-lives?
a. 12; 12 b. 6; 18 c. 2; 22
Experiment 3
1. When half of the carbon-14 atoms in the tree have decayed
into nitrogen-14, it has been approximately 5700
years since the tree _____.
a. was planted b. died
2. The probe measures that the percentage of carbon-14 in the
tree is 100% while the tree is alive, and then
measures the percentage of carbon-14 decreasing with time after
the tree dies. This means that radiometric
dating using carbon-14 is applicable to:
a. living objects. B. once-living objects c. both living and
once-living objects.

89. 3. When half of the Uranium-238 in the rock has decayed,
______ years have passed since the volcano erupted.
a. 4.5 billion years b. 5700 years
4. In step c of experiment 3, the probe was used to measure
uranium-238 in the tree. In step f of experiment 3, the
probe was used to measure carbon-14 in the rock. In both steps
(c and f), the probe reading was _____.
a. 100%, then decreasing with time. b. zero (0) . c. decreasing
with time.
5. Uranium-238 must be used to measure the age of the rock,
and not carbon-14, because:
a. The isotope carbon-14 did not exist when the rock was
created.
b. rocks do not contain carbon-14, and even if they did, the
carbon-14 would have likely decayed away
prior to measuring the rock’s age.
c. it is easier to measure uranium in rocks than it is carbon.
6. Based on your observations for experiment 3, and the answers

90. to your questions above, _______ should be
used to determine the ages of once-living things, and _______
should be used to determine the ages of rocks.
a. carbon-14; uranium-238 b. uranium-238; carbon-14 c.
either can be used for both objects.
Experiment 4
1. Briefly explain why neither carbon-14 nor uranium-238 could
be used to determine the ages of the last 4 items in
data table 3.
Innovations
Perspective
Is AI the end of jobs or a new beginning?
By Vivek Wadhwa May 31 2017 WASHINGTON POST
A visitor plays chess against a robot during a COMPUTEX
event in Taiwan on May 30. This year’s COMPUTEX focuses
on artificial intelligence and robotics as well as virtual reality
and the Internet of things. (Ritchie B. Tongo/European
Pressphoto Agency)
Artificial Intelligence (AI) is advancing so rapidly that even its
developers are being caught off guard. Google co-founder
Sergey Brin said in Davos, Switzerland, in January that it
“touches every single one of our main projects, ranging from

91. search to photos to ads … everything we do … it definitely
surprised me, even though I was sitting right there.”
The long-promised AI, the stuff we’ve seen in science fiction, is
coming and we need to be prepared. Today, AI is powering
voice assistants such as Google Home, Amazon Alexa and
Apple Siri, allowing them to have increasingly natural
conversations with us and manage our lights, order food and
schedule meetings. Businesses are infusing AI into their
products to analyze the vast amounts of data and improve
decision-making. In a decade or two, we will have robotic
assistants that remind us of Rosie from “The Jetsons” and R2-
D2 of “Star Wars.”
This has profound implications for how we live and work, for
better and worse. AI is going to become our guide and
companion — and take millions of jobs away from people. We
can deny this is happening, be angry or simply ignore it. But if
we do, we will be the losers. As I discussed in my new book,
“Driver in the Driverless Car,” technology is now advancing on
an exponential curve and making science fiction a reality. We
can’t stop it. All we can do is to understand it and use it to
better ourselves — and humanity.
Rosie and R2-D2 may be on their way but AI is still very
limited in its capability, and will be for a long time. The voice
assistants are examples of what technologists call narrow AI:
systems that are useful, can interact with humans and bear some
of the hallmarks of intelligence — but would never be mistaken
for a human. They can, however, do a better job on a very
specific range of tasks than humans can. I couldn’t, for
example, recall the winning and losing pitcher in every baseball
game of the major leagues from the previous night.
Narrow-AI systems are much better than humans at accessing
information stored in complex databases, but their capabilities
exclude creative thought. If you asked Siri to find the perfect
gift for your mother for Valentine’s Day, she might make a
snarky comment but couldn’t venture an educated guess. If you
asked her to write your term paper on the Napoleonic Wars, she

92. couldn’t help. That is where the human element comes in and
where the opportunities are for us to benefit from AI — and
stay employed.
In his book “Deep Thinking: Where Machine Intelligence Ends
and Human Creativity Begins,” chess grandmaster Garry
Kasparov tells of his shock and anger at being defeated by
IBM’s Deep Blue supercomputer in 1997. He acknowledges that
he is a sore loser but was clearly traumatized by having a
machine outsmart him. He was aware of the evolution of the
technology but never believed it would beat him at his own
game. After coming to grips with his defeat, 20 years later, he
says fail-safes are required … but so is courage.
Kasparov wrote: “When I sat across from Deep Blue twenty
years ago I sensed something new, something unsettling.
Perhaps you will experience a similar feeling the first time you
ride in a driverless car, or the first time your new computer boss
issues an order at work. We must face these fears in order to get
the most out of our technology and to get the most out of
ourselves. Intelligent machines will continue that process,
taking over the more menial aspects of cognition and elevating
our mental lives toward creativity, curiosity, beauty, and joy.
These are what truly make us human, not any particular activity
or skill like swinging a hammer — or even playing chess.”
In other words, we better get used to it and ride the wave.
Human superiority over animals is based on our ability to create
and use tools. The mental capacity to make things that improved
our chances of survival led to a natural selection of better
toolmakers and tool users. Nearly everything a human does
involves technology. For adding numbers, we used abacuses and
mechanical calculators and now spreadsheets. To improve our
memory, we wrote on stones, parchment and paper, and now
have disk drives and cloud storage.
AI is the next step in improving our cognitive functions and
decision-making.
Think about it: When was the last time you tried memorizing
your calendar or Rolodex or used a printed map? Just as we

93. instinctively do everything on our smartphones, we will rely on
AI. We may have forfeited skills such as the ability to add up
the price of our groceries but we are smarter and more
productive. With the help of Google and Wikipedia, we can be
experts on any topic, and these don’t make us any dumber than
encyclopedias, phone books and librarians did.
A valid concern is that dependence on AI may cause us to
forfeit human creativity. As Kasparov observes, the chess games
on our smartphones are many times more powerful than the
supercomputers that defeated him, yet this didn’t cause human
chess players to become less capable — the opposite happened.
There are now stronger chess players all over the world, and the
game is played in a better way.
As Kasparov explains: “It used to be that young players might
acquire the style of their early coaches. If you worked with a
coach who preferred sharp openings and speculative attacking
play himself, it would influence his pupils to play similarly. …
What happens when the early influential coach is a computer?
The machine doesn’t care about style or patterns or hundreds of
years of established theory. It counts up the values of the chess
pieces, analyzes a few billion moves, and counts them up again.
It is entirely free of prejudice and doctrine. … The heavy use of
computers for practice and analysis has contributed to the
development of a generation of players who are almost as free
of dogma as the machines with which they train.”
Perhaps this is the greatest benefit that AI will bring —
humanity can be free of dogma and historical bias; it can do
more intelligent decision-making. And instead of doing
repetitive data analysis and number crunching, human workers
can focus on enhancing their knowledge and being more
creative.
https://www.technologyreview.com/s/607970/experts-predict-
when-artificial-intelligence-will-exceed-human-performance/
Intelligent Machines

94. Experts Predict When Artificial Intelligence Will Exceed
Human Performance
Trucking will be computerized long before surgery, computer
scientists say.
· by Emerging Technology from the arXiv
· May 31, 2017
Artificial intelligence is changing the world and doing it at
breakneck speed. The promise is that intelligent machines will
be able to do every task better and more cheaply than humans.
Rightly or wrongly, one industry after another is falling under
its spell, even though few have benefited significantly so far.
And that raises an interesting question: when will artificial
intelligence exceed human performance? More specifically,
when will a machine do your job better than you?
Today, we have an answer of sorts thanks to the work of Katja
Grace at the Future of Humanity Institute at the University of
Oxford and a few pals. To find out, these guys asked the
experts. They surveyed the world’s leading researchers in
artificial intelligence by asking them when they think intelligent
machines will better humans in a wide range of tasks. And many
of the answers are something of a surprise.
The experts that Grace and co coopted were academics and
industry experts who gave papers at the International
Conference on Machine Learning in July 2015 and the Neural
Information Processing Systems conference in December 2015.
These are two of the most important events for experts in
artificial intelligence, so it’s a good bet that many of the
world’s experts were on this list.
Grace and co asked them all—1,634 of them—to fill in a survey
about when artificial intelligence would be better and cheaper
than humans at a variety of tasks. Of these experts, 352
responded. Grave and co then calculated their median responses
The experts predict that AI will outperform humans in the next
10 years in tasks such as translating languages (by 2024),
writing high school essays (by 2026), and driving trucks (by

95. 2027).
But many other tasks will take much longer for machines to
master. AI won’t be better than humans at working in retail
until 2031, able to write a bestselling book until 2049, or
capable of working as a surgeon until 2053.
The experts are far from infallible. They predicted that AI
would be better than humans at Go by about 2027. (This was in
2015, remember.) In fact, Google’s DeepMind subsidiary has
already developed an artificial intelligence capable of beating
the best humans. That took two years rather than 12. It’s easy to
think that this gives the lie to these predictions.
The experts go on to predict a 50 percent chance that AI will be
better than humans at more or less everything in about 45 years.
That’s the kind of prediction that needs to be taken with a pinch
of salt. The 40-year prediction horizon should always raise
alarm bells. According to some energy experts, cost-effective
fusion energy is about 40 years away—but it always has been. It
was 40 years away when researchers first explored fusion more
than 50 years ago. But it has stayed a distant dream because the
challenges have turned out to be more significant than anyone
imagined.
Forty years is an important number when humans make
predictions because it is the length of most people’s working
lives. So any predicted change that is further away than that
means the change will happen beyond the working lifetime of
everyone who is working today. In other words, it cannot
happen with any technology that today’s experts have any
practical experience with. That suggests it is a number to be
treated with caution.
But teasing apart the numbers shows something interesting. This
45-year prediction is the median figure from all the experts.
Perhaps some subset of this group is more expert than the
others?
To find out if different groups made different predictions, Grace
and co looked at how the predictions changed with the age of
the researchers, the number of their citations (i.e., their

96. expertise), and their region of origin.
It turns out that age and expertise make no difference to the
prediction, but origin does. While North American researchers
expect AI to outperform humans at everything in 74 years,
researchers from Asia expect it in just 30 years.
That’s a big difference that is hard to explain. And it raises an
interesting question: what do Asian researchers know that North
Americans don’t (or vice versa)?
Ref: arxiv.org/abs/1705.08807 : When Will AI Exceed Human
Performance? Evidence from AI Experts