Lecture 9: AI Ethics (Full Stack Deep Learning - Spring 2021)

1. Full Stack Deep Learning - UC Berkeley Spring 2021 - Sergey Karayev, Josh Tobin, Pieter Abbeel
Ethics

2. Full Stack Deep Learning - UC Berkeley Spring 2021
Preamble
• This is a huge subject, spanning many disciplines and addressing many
real different problems.
• We, ML practitioners, need to have a student mindset, and not assume we
have the answers. These are not easy problems.
• I am not an expert. Excellent resources recommended at the end.
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Tech Ethics Curricula
• 115 university tech ethics courses analyzed
• Huge variation in materials
• More consensus in outcomes: ability to critique, spot issues, and make and
communicate arguments.
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SIGCSE 2020

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One last preamble
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https://www.newyorker.com/books/page-turner/this-is-water

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Outline
• What is ethics
• Long-term ethical problems in AI
• Near-term ethical problems in AI
• Best practices
• Where to learn more
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What is ethics
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Ethics?
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Ethics ≠ Feelings Ethics ≠ Laws Ethics ≠ Societal Beliefs
https://www.scu.edu/ethics/ethics-resources/ethical-decision-making/what-is-ethics/

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Ethical Theories
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https://kevinbinz.com/2017/04/13/ethical-theory-intro/
Divine Command
- Moral behaviors are
those commanded
by the divine
- Criticism: not much
philosophy can say
Virtue Ethics
- Moral behaviors
uphold the person's
virtues
- Criticism: increasing
evidence that
character traits are
illusory
Deontology (Duty)
- Moral behaviors are
those that satisfy the
categorical
imperative (e.g. don't
lie, don't kill)
- Criticism:
unacceptable
inflexibility
Utilitarianism
- Moral behaviors are
those that bring the
most good to the
most people
- Criticism: How to
measure utility?

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Is one a clear winner?
• Professional philosophers are just about evenly split between these
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https://philpapers.org/surveys/results.pl

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Intuition through "Trolley Problems"
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Trolley Problems
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Ethical Theories
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https://www.researchgate.net/figure/Summary-of-major-ethical-theories_tbl2_229658531

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Ethics of Technology
• Ethics change with technological progress
• e.g. Industrial revolution
• e.g. Right to Internet access
• e.g. Birth control, surrogate pregnancy, embryo selection,
artificial womb
• e.g. Lab-grown meat
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Questions?
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Long-term problems
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Autonomous Weapons
• Tempting to dismiss as far-fetched at this
time
• But "the future is already here, just not evenly
distributed"
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Replacing human labor
• AI and robots replacing humans in
existing jobs
• Good and bad!
• Interesting spin: AI controlling human
labor
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Sfdpnnfoefe!
tipsu!tupsz

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Replacing humans entirely
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What's common in all these
long-term problems?
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Alignment
• "Paperclip maximizer": AGI given the goal of producing paperclips
eventually turns every atom in space into paperclips
• Old lesson: establishing and communicating our goals and values is hard,
and technology amplifies the difficulty
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Guiding principle:
AI systems we build need to be
aligned with our goals and values
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Questions?
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Deep topic
• Active area of research (active at Berkeley)
• Useful lens for near-term problems, too
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Near-term problems
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Hiring
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https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-
against-women-idUSKCN1MK08G

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Hiring
• ML model to predict hiring decision given a resume
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ML Model

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Hiring
• ML model to predict hiring decision given a resume
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ML Model
Data
Ijsjoh!efdjtjpot@!
Ps!kpc!qfsgpsnbodf@

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Hiring
• ML model to predict hiring decision given a resume
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ML Model
Data
World
Lopxo!up!cf!cjbtfe!jo!nboz!xbzt;!
.!ijsjoh!qjqfmjof!
.!ijsjoh!efdjtjpot!
.!qfsgpsnbodf!sbujoht
Tp!op!nbuufs!xibu!xf!qjdl-!
pvs!ebub!jt!bmtp!cjbtfe
Uifsfgpsf!pvs!
npefm!jt!cjbtfe

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Hiring
• ML model to predict hiring decision given a resume
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Action
Tpvsdjoh@!Epvcmf.difdljoh!ivnbo!
efdjtjpo@!Ijsjoh@
ML Model
Data
World

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Hiring
• ML model to predict hiring decision given a resume
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Action
Jo!nboz!dbtft-!bnqmjgzjoh!
fyjtujoh!cjbtft"
ML Model
Data
World

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Hiring
• ML model to predict hiring decision given a resume
• Amplifying existing biases is not aligned with our goals and values!
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Action
Jo!nboz!dbtft-!bnqmjgzjoh!
fyjtujoh!cjbtft"
ML Model
Data
World

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Questions?
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Fairness
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COMPAS
• Goal: predict recidivism, such that judges can consult 1-10 score in pre-trial
sentencing decisions.
• Motivation: be less biased than humans
• Solution:
• Gather data
• Exclude protected class attributes (race, etc)
• Ensure that our model's score corresponds to same probability of
recidivism across all groups
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Correctional Offender Management Profiling for Alternative Sanctions

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And yet!
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https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

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21 Fairness Definitions
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Aravind Narayanan - 21 Fairness Definitions
🙏

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Statistical Bias
• Statistical bias: Difference between estimator's
expected value and the true value
• In this sense, COMPAS scores are not biased,
w.r.t re-arrest
• But is it an adequate fairness criterion? Is it
aligned with our values?
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https://www.washingtonpost.com/news/monkey-cage/wp/2016/10/17/can-an-algorithm-be-racist-our-analysis-is-more-cautious-
than-propublicas/
Jnqpsubou!dbwfbu"!Xf!pomz!
ibwf!ebub!gps!bssftut-!opu!
dsjnft!dpnnjuufe/!Uifsf!nbz!
xfmm!cf!cjbt!jo!bssftut/

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Different perspectives on fairness
• What do different stakeholders want from the
classifier?
• Decision-maker: of those I've labeled high risk,
how many recidivated?
• Predictive value: TP / (TP + FP)
• Defendant: what's the probability I'll be incorrectly
classified as high risk?
• False positive rate: FP / (FP + TN)
• Society: is the selected set demographically
balanced?
• Demographic parity
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Aravind Narayanan - 21 Fairness Definitions

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Group Fairness
• Do outcomes differ between
groups (e.g. demographic), which
we have no reason to believe are
actually different?
• Motivation of the Pro-Publica
article
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Aravind Narayanan - 21 Fairness Definitions
Gbmtf!
Qptjujwf
Gbmtf!
Ofhbujwf

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No "correct" group fairness definition
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Aravind Narayanan - 21 Fairness Definitions

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It gets worse...
• Okay, then let's just pick most important
two metrics (FPR and FNR), and allow the
model to use protected class attributes.
• Now we fail individual fairness ->
• Fine, let's just pick one! (e.g. equal FPR)
• We still sacrifice some utility (e.g. public
safety, or number of defendants released)
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Aravind Narayanan - 21 Fairness Definitions

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Interactive Example
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https://research.google.com/bigpicture/attacking-discrimination-in-ml/

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By the way...
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Aravind Narayanan - 21 Fairness Definitions

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Trade-offs
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Aravind Narayanan - 21 Fairness Definitions

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Seeing the water
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https://www.mobilizegreen.org/blog/2018/9/30/environmental-equity-vs-environmental-justice-whats-the-difference

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https://www.mobilizegreen.org/blog/2018/9/30/environmental-equity-vs-environmental-justice-whats-the-difference

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https://www.mobilizegreen.org/blog/2018/9/30/environmental-equity-vs-environmental-justice-whats-the-difference

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Questions?
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Representation
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Representation
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https://twitter.com/nke_ise/status/897756900753891328

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Not a new problem, sadly
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...or a problem in just our field
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Recent improvement!

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Large part of the solution
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https://www.nytimes.com/2021/03/15/technology/artificial-intelligence-google-bias.html

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Another example
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Recent improvement!

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Translation
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Aravind Narayanan - 21 Fairness Definitions
Recent improvement!

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Word Embeddings
• Word2Vec introduced vector
math on word embeddings
• Reveal harmful biases
encoded in our language
corpora
• Potential solution: de-bias at
training time, but at least
make user aware
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GPT-3
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Recent Language Models
• Paper apparently led to
authors being fired by
Google
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http://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf

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Seeing the water
• Whether language models should reflect the world as it is in the data, or
as society believes it should be, depends on what use they are applied to.
• And when we do want them to reflect the world as it should be, do we
agree on what that is?
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Face Recognition
• Old context: no expectation of
privacy in public
• New context: "in public" now
means "on any street in any city, or
on any website on the internet"
• Is it ethical to work on this?
• Is it a problem if it does not work as
well on some ethnicities?
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https://fairmlbook.org/introduction.html

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Questions?
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Best practices
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What do practitioners need?
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• support in fairness-aware data collection and
curation
• overcoming teams’ blind spots
• implementing more proactive fairness auditing
processes
• auditing complex ML systems
• deciding how to address particular instances of
unfairness
• addressing biases in the humans embedded
throughout the ML development pipeline
2019

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Some suggestions
• Ethical risk sweeping
treat like cybersecurity penetration testing
• Expanding the ethical circle
whose interests, desires, experiences, values have we just assumed instead of
consulted?
• Think about the terrible people
Who might abuse, steal, weaponize what we build? What incentives are we
creating?
• Closing the loop
Remember that this is not a process to complete and forget. Set up ways to keep
improving.
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https://www.youtube.com/watch?v=av7utkFXbU4

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Model Cards
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https://modelcards.withgoogle.com/face-detection

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Bias Audit
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http://www.datasciencepublicpolicy.org/projects/aequitas/

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http://www.datasciencepublicpolicy.org/projects/aequitas/

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AI bias is rooted in societal inequities,
manifested in data, and amplified by AI.
Unbiased societal AI is an impossible goal
for a single project to achieve, but greatly
mitigating its harmful effects is not.
Visualization courtesy of Eric Wang

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Build a diverse AI team to bring lived
experience to their work
Display insights about bias contextually and
intuitively in product
Make AI fairness and transparency a key
component of our work
Visualization courtesy of Eric Wang

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A professional code of ethics?
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Medicine
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Armed Forces
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Questions?
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Learn more
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Experts
• Practical Data Ethics course from Rachel
Thomas: https://ethics.fast.ai
• Single lecture from Fast.ai 2020 course: https://
www.youtube.com/watch?v=krIVOb23EH8
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https://fairmlbook.org

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https://dssg.github.io/fairness_tutorial/

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Thank you!
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