We live in an increasingly data driven world, but without a real deep understanding of the ethical delimmas around it. In this presentation, we'll look at some recent ethical problems that have cropped up and discuss what can be done to address them
They look like fun bandages or stickers - eye candy for little children. That’s why parents are being warned about how toxic fentanyl pain patches have been linked to accidental exposures resulting in several child fatalities. The U.S. Food and Drug Administration (FDA) …
Continue Reading
- See more at: http://www.searcymasstort.com/blog/#sthash.CVxTezlA.dpuf
I gave this presentation at Deutsche Telekom AG's Digital Ethics Conference in Bonn on March 13 2019. It provides the background for how biases may occur in machine learning systems and what may go wrong if not corrected (or minimized).
The top 7 ethical dilemmas reported by ITBrian Jackson
A drug company asks a web developer to make sure its drug is recommended. A law firm asks a programmer to allow them to double-bill their clients for the same time. The U.S. Navy wants 'being gay' input into its database of offerences to prosecute. Ethics and programming can sometimes intersect and this presentation explores examples of that.
December 12, 2017
The Sixth Annual Health Law Year in P/Review symposium featured leading experts discussing major developments during 2017 and what to watch out for in 2018. The discussion at this day-long event covered hot topics in such areas as health policy under the new administration, regulatory issues in clinical research, law at the end-of-life, patient rights and advocacy, pharmaceutical policy, reproductive health, and public health law.
For more information, visit our website at: http://petrieflom.law.harvard.edu/events/details/sixth-annual-health-law-year-in-p-review
Keynote presentation at our Magyar Telekom "AI for Everyone" conference in Budapest at 21st of March 2017.
You will find a the blog companion here: https://aistrategyblog.com/ which provides insights into how we humans perceive AI. Enjoy the read if you get there.
If you would like to have the presentation or have any questions please get in touch, don't be shy!
They look like fun bandages or stickers - eye candy for little children. That’s why parents are being warned about how toxic fentanyl pain patches have been linked to accidental exposures resulting in several child fatalities. The U.S. Food and Drug Administration (FDA) …
Continue Reading
- See more at: http://www.searcymasstort.com/blog/#sthash.CVxTezlA.dpuf
I gave this presentation at Deutsche Telekom AG's Digital Ethics Conference in Bonn on March 13 2019. It provides the background for how biases may occur in machine learning systems and what may go wrong if not corrected (or minimized).
The top 7 ethical dilemmas reported by ITBrian Jackson
A drug company asks a web developer to make sure its drug is recommended. A law firm asks a programmer to allow them to double-bill their clients for the same time. The U.S. Navy wants 'being gay' input into its database of offerences to prosecute. Ethics and programming can sometimes intersect and this presentation explores examples of that.
December 12, 2017
The Sixth Annual Health Law Year in P/Review symposium featured leading experts discussing major developments during 2017 and what to watch out for in 2018. The discussion at this day-long event covered hot topics in such areas as health policy under the new administration, regulatory issues in clinical research, law at the end-of-life, patient rights and advocacy, pharmaceutical policy, reproductive health, and public health law.
For more information, visit our website at: http://petrieflom.law.harvard.edu/events/details/sixth-annual-health-law-year-in-p-review
Keynote presentation at our Magyar Telekom "AI for Everyone" conference in Budapest at 21st of March 2017.
You will find a the blog companion here: https://aistrategyblog.com/ which provides insights into how we humans perceive AI. Enjoy the read if you get there.
If you would like to have the presentation or have any questions please get in touch, don't be shy!
The Danger of Big Data by Kerry Bodine - Forrester research
Service design teams can glean big data insights from social media, financial systems, emails, surveys, call centers, and digital and analog sensors. But companies that fixate on amassing new data sources put themselves at risk of neglecting small data insights gathered through qualitative research methods. How can firms achieve balance?
Get it straight from the experts: Learn what you should and shouldn't do, gray areas and potential legal pitfalls in the ever emerging world of social media.
Chapter 4 • Data Mining Process, Methods, and Algorithms 243.docxadkinspaige22
Chapter 4 • Data Mining Process, Methods, and Algorithms 243
then subsequently augmented the passenger data with additional information such as fam-
ily sizes and Social Security numbers—information purchased from the data broker Acxiom.
The consolidated personal database was intended to be used for a data mining project to
develop potential terrorist profiles. All of this was done without notification or consent of
passengers. When news of the activities got out, however, dozens of privacy lawsuits were
filed against JetBlue, Torch, and Acxiom, and several U.S. senators called for an investiga-
tion into the incident (Wald, 2004). Similar, but not as dramatic, privacy-related news was
reported in the recent past about popular social network companies that allegedly were
selling customer-specific data to other companies for personalized target marketing.
Another peculiar story about privacy concerns made it to the headlines in 2012.
In this instance, the company, Target, did not even use any private and/or personal
data. Legally speaking, there was no violation of any laws. The story is summarized in
Application Case 4.7.
In early 2012, an infamous story appeared concern-
ing Target’s practice of predictive analytics. The story
was about a teenage girl who was being sent adver-
tising flyers and coupons by Target for the kinds of
things that a mother-to-be would buy from a store like
Target. The story goes like this: An angry man went
into a Target outside of Minneapolis, demanding to
talk to a manager: “My daughter got this in the mail!”
he said. “She’s still in high school, and you’re sending
her coupons for baby clothes and cribs? Are you trying
to encourage her to get pregnant?” The manager had
no idea what the man was talking about. He looked at
the mailer. Sure enough, it was addressed to the man’s
daughter and contained advertisements for maternity
clothing, nursery furniture, and pictures of smiling
infants. The manager apologized and then called a few
days later to apologize again. On the phone, though,
the father was somewhat abashed. “I had a talk with
my daughter,” he said. “It turns out there’s been some
activities in my house I haven’t been completely aware
of. She’s due in August. I owe you an apology.”
As it turns out, Target figured out a teen girl
was pregnant before her father did! Here is how
the company did it. Target assigns every customer a
Guest ID number (tied to his or her credit card, name,
or e-mail address) that becomes a placeholder that
keeps a history of everything the person has bought.
Target augments these data with any demographic
information that it had collected from the customer
or had bought from other information sources. Using
this information, Target looked at historical buying
data for all the females who had signed up for Target
baby registries in the past. They analyzed the data
from all directions, and soon enough, some useful
patterns emerged. For ex.
Educational technology is not without its costs. Increasingly one of the costs of using EdTech is the storage and use of student data. Each time a student used a device or logs into a web site a corporation or start-up collects data about what that student did and for how long. What happens to that data? How is it used? Who owns it and whose responsibility is to protect the rights of students in this digital learning environment? We'll explore these and any associated issues as we examine what might happen when educators trade student information for free services and try and decide what we can do to protect student data rights.
Participants will leave the session with a better understanding of some of the potential dangers of using educational technology, how data is used and some questions to consider about the potential costs to students when we use EdTech.
Even as we grow in our ability to extract vital information from big data, the scientific community still faces roadblocks that pose major data mining challenges. In this article, we will discuss 10 key issues that we face in modern data mining and their possible solutions.
Roundtable at the 2018 AoIR conference.
Anatoliy Gruzd, Jenna Jacobson, Ryerson University, Canada
Jacquelyn Burkell, Western University
Joanne McNeish, Ryerson University
Anabel Quan-Haase, Western University
Abstract
The transnational flows of information across nations and borders make it difficult to introduce and implement privacy-preserving policies relating to social media data. Social media data are a rich source of behavioural data that can reveal how we connect and interact with each other online in real time. Furthermore, the materiality of new digital intermediaries (such as the Internet of Things, AI, and algorithms) raises additional anticipated and unanticipated privacy challenges that need to be addressed as we continue to speed towards an increasingly digitally-mediated future.
A by-product of the large-scale social media adoption is social media data mining; publicly available social media data is largely free and legally available to be mined, analyzed, and used (Kennedy 2016) for whatever purposes by third parties. Researchers have begun to suggest that ethics need to be considered even if the data is public (boyd & Crawford 2012).
In the wake of the EU's recent legislation of the General Data Protection Regulation and the Right to be Forgotten, as well as increasing critical attention around the world, the roundtable will discuss how to navigate the transnational and material, as well as the complex and competing, interests associated with using social media, including ethics, privacy, security, and intellectual property rights. By balancing people's individual rights to exercise autonomy over "their" data and the societal benefits of using and analyzing the data for insights, the roundtable aims to generate theoretically-rich discussion and debate with internet researchers about the ethics, privacy, and best practices of using social media data.
A Survey of Security & Privacy in Online Social Networks (OSN) with regards t...Frances Coronel
Published December 14, 2015, in Social media
Research Presentation on Online Social Networks (OSN) Privacy.
CSC 425
Senior Seminar
Hampton University
Fall 2015
---
FVCproductions
https://fvcproductions.com
Age Verification / “Doing the Right Thing”IDology, Inc
http://www.idology.com/about-idology/about-idology/ | The most important thing to know about us is to understand what we do. IDology, Inc provides real-time technology solutions that verify an individual’s identity and age for anyone conducting business online or in a customer-not-present environment. What makes us different is that we do this in a way that builds more confidence with your customers – byprotecting sensitive data and promoting consumer privacy.
SIM RTP Meeting - So Who's Using Open Source Anyway?Alex Meadows
Open Source has been around for several decades now, but there is still a bit of mystery around what makes open source work and concern about using it in the enterprise. Open Source technologies are being widely used in many industries, including analytics, software development, social media, data center management, and more.
The discussion will be moderated by Julie Batchelor and panelists include:
* Todd Lewis, Open Source evangelist
* Jason Hibbets, Open Source Community Manager
* Jim Salter, Co-Owner and Chief Technology Officer at Openoid, LLC
* Alex Meadows, data scientist
Data Warehousing is a data architecture that separates reporting and analytics needs from operational transaction systems. This presentation is an introduction into traditional data warehousing architectures and how to determine if your environment requires a data warehouse.
The Danger of Big Data by Kerry Bodine - Forrester research
Service design teams can glean big data insights from social media, financial systems, emails, surveys, call centers, and digital and analog sensors. But companies that fixate on amassing new data sources put themselves at risk of neglecting small data insights gathered through qualitative research methods. How can firms achieve balance?
Get it straight from the experts: Learn what you should and shouldn't do, gray areas and potential legal pitfalls in the ever emerging world of social media.
Chapter 4 • Data Mining Process, Methods, and Algorithms 243.docxadkinspaige22
Chapter 4 • Data Mining Process, Methods, and Algorithms 243
then subsequently augmented the passenger data with additional information such as fam-
ily sizes and Social Security numbers—information purchased from the data broker Acxiom.
The consolidated personal database was intended to be used for a data mining project to
develop potential terrorist profiles. All of this was done without notification or consent of
passengers. When news of the activities got out, however, dozens of privacy lawsuits were
filed against JetBlue, Torch, and Acxiom, and several U.S. senators called for an investiga-
tion into the incident (Wald, 2004). Similar, but not as dramatic, privacy-related news was
reported in the recent past about popular social network companies that allegedly were
selling customer-specific data to other companies for personalized target marketing.
Another peculiar story about privacy concerns made it to the headlines in 2012.
In this instance, the company, Target, did not even use any private and/or personal
data. Legally speaking, there was no violation of any laws. The story is summarized in
Application Case 4.7.
In early 2012, an infamous story appeared concern-
ing Target’s practice of predictive analytics. The story
was about a teenage girl who was being sent adver-
tising flyers and coupons by Target for the kinds of
things that a mother-to-be would buy from a store like
Target. The story goes like this: An angry man went
into a Target outside of Minneapolis, demanding to
talk to a manager: “My daughter got this in the mail!”
he said. “She’s still in high school, and you’re sending
her coupons for baby clothes and cribs? Are you trying
to encourage her to get pregnant?” The manager had
no idea what the man was talking about. He looked at
the mailer. Sure enough, it was addressed to the man’s
daughter and contained advertisements for maternity
clothing, nursery furniture, and pictures of smiling
infants. The manager apologized and then called a few
days later to apologize again. On the phone, though,
the father was somewhat abashed. “I had a talk with
my daughter,” he said. “It turns out there’s been some
activities in my house I haven’t been completely aware
of. She’s due in August. I owe you an apology.”
As it turns out, Target figured out a teen girl
was pregnant before her father did! Here is how
the company did it. Target assigns every customer a
Guest ID number (tied to his or her credit card, name,
or e-mail address) that becomes a placeholder that
keeps a history of everything the person has bought.
Target augments these data with any demographic
information that it had collected from the customer
or had bought from other information sources. Using
this information, Target looked at historical buying
data for all the females who had signed up for Target
baby registries in the past. They analyzed the data
from all directions, and soon enough, some useful
patterns emerged. For ex.
Educational technology is not without its costs. Increasingly one of the costs of using EdTech is the storage and use of student data. Each time a student used a device or logs into a web site a corporation or start-up collects data about what that student did and for how long. What happens to that data? How is it used? Who owns it and whose responsibility is to protect the rights of students in this digital learning environment? We'll explore these and any associated issues as we examine what might happen when educators trade student information for free services and try and decide what we can do to protect student data rights.
Participants will leave the session with a better understanding of some of the potential dangers of using educational technology, how data is used and some questions to consider about the potential costs to students when we use EdTech.
Even as we grow in our ability to extract vital information from big data, the scientific community still faces roadblocks that pose major data mining challenges. In this article, we will discuss 10 key issues that we face in modern data mining and their possible solutions.
Roundtable at the 2018 AoIR conference.
Anatoliy Gruzd, Jenna Jacobson, Ryerson University, Canada
Jacquelyn Burkell, Western University
Joanne McNeish, Ryerson University
Anabel Quan-Haase, Western University
Abstract
The transnational flows of information across nations and borders make it difficult to introduce and implement privacy-preserving policies relating to social media data. Social media data are a rich source of behavioural data that can reveal how we connect and interact with each other online in real time. Furthermore, the materiality of new digital intermediaries (such as the Internet of Things, AI, and algorithms) raises additional anticipated and unanticipated privacy challenges that need to be addressed as we continue to speed towards an increasingly digitally-mediated future.
A by-product of the large-scale social media adoption is social media data mining; publicly available social media data is largely free and legally available to be mined, analyzed, and used (Kennedy 2016) for whatever purposes by third parties. Researchers have begun to suggest that ethics need to be considered even if the data is public (boyd & Crawford 2012).
In the wake of the EU's recent legislation of the General Data Protection Regulation and the Right to be Forgotten, as well as increasing critical attention around the world, the roundtable will discuss how to navigate the transnational and material, as well as the complex and competing, interests associated with using social media, including ethics, privacy, security, and intellectual property rights. By balancing people's individual rights to exercise autonomy over "their" data and the societal benefits of using and analyzing the data for insights, the roundtable aims to generate theoretically-rich discussion and debate with internet researchers about the ethics, privacy, and best practices of using social media data.
A Survey of Security & Privacy in Online Social Networks (OSN) with regards t...Frances Coronel
Published December 14, 2015, in Social media
Research Presentation on Online Social Networks (OSN) Privacy.
CSC 425
Senior Seminar
Hampton University
Fall 2015
---
FVCproductions
https://fvcproductions.com
Age Verification / “Doing the Right Thing”IDology, Inc
http://www.idology.com/about-idology/about-idology/ | The most important thing to know about us is to understand what we do. IDology, Inc provides real-time technology solutions that verify an individual’s identity and age for anyone conducting business online or in a customer-not-present environment. What makes us different is that we do this in a way that builds more confidence with your customers – byprotecting sensitive data and promoting consumer privacy.
SIM RTP Meeting - So Who's Using Open Source Anyway?Alex Meadows
Open Source has been around for several decades now, but there is still a bit of mystery around what makes open source work and concern about using it in the enterprise. Open Source technologies are being widely used in many industries, including analytics, software development, social media, data center management, and more.
The discussion will be moderated by Julie Batchelor and panelists include:
* Todd Lewis, Open Source evangelist
* Jason Hibbets, Open Source Community Manager
* Jim Salter, Co-Owner and Chief Technology Officer at Openoid, LLC
* Alex Meadows, data scientist
Data Warehousing is a data architecture that separates reporting and analytics needs from operational transaction systems. This presentation is an introduction into traditional data warehousing architectures and how to determine if your environment requires a data warehouse.
Building next generation data warehousesAlex Meadows
All Things Open 2016 Talk - discussing technologies used to augment traditional data warehousing. Those technologies are:
* data vault
* anchor modeling
* linked data
* NoSQL
* data virtualization
* textual disambiguation
How Linked Data Can Speed Information DiscoveryAlex Meadows
Linked data platforms are now making it easier than ever to perform data exploration and discovery without having to wait to get the data integrated into the data warehouse. In this presentation, we discuss what linked data is and show a case study on integrating separate source systems so that scientists don't have to learn the source systems structures to get to their data.
Triple stores are finally seeing mainstream use, but what exactly is all this talk about linked data? In this deck, we discuss what the semantic web is and how to map your relational data sets into a triple store database using open source software.
Slides used for a presentation to introduce the field of business analytics. Covers what BA is, how it is a part of business intelligence, and what areas make up BA.
Big Data has been around long enough that there are some common issues that occur whenever an organization tries to implement and integrate it into their ecosystem. This presentation covers some of those pitfalls, which also impact traditional data warehouses/business intelligence ecosystems
"Big Data" is big business, but what does it really mean? How will big data impact industries and consumers? This slide deck goes through some of the high level details of the market and how it is revolutionizing the world.
Providing value to the customer is one of the biggest challenges for any team to succeed in, let alone BI teams. Agile allows for moving into a faster delivery mode by slowing down to speed up. In this presentation, we cover tips for setting up an Agile practice, common pitfalls to avoid, and why Agile is just now taking off in the BI space.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
2. 2
● General warning – The examples shared can be very
unsettling and uncomfortable.
● Trying to keep bias out of these conversations can be difficult,
especially when talking actual examples.
● Let’s do our best
● Also, while an answer may be obvious to you – it may be
slightly/completely different to others, please respect that
4. 4
● Target (and most companies) assigns every
customer a Guest ID
- loyalty/debit/credit card
- Email
- Name
● Tracks every purchase ever made
● Attaches any demographic information known
● New parents are a gold mine
- Don’t care as much about brand loyalty
- Frazzled and overwhelmed
● Birth records are public
- Everyone markets to parents upon birth
● How can companies market to expectant
parents in first/second trimester?
5. 5
“[Pole] ran test after test, analyzing the data, and before
long some useful patterns emerged. Lotions, for
example. Lots of people buy lotion, but one of Pole’s
colleagues noticed that women on the baby registry
were buying larger quantities of unscented lotion
around the beginning of their second trimester.
Another analyst noted that sometime in the first 20
weeks, pregnant women loaded up on supplements
like calcium, magnesium and zinc. Many shoppers
purchase soap and cotton balls, but when someone
suddenly starts buying lots of scent-free soap and
extra-big bags of cotton balls, in addition to hand
sanitizers and washcloths, it signals they could be
getting close to their delivery date.”
10. 10
"We’re appalled and genuinely sorry that this
happened. We are taking immediate action to
prevent this type of result from appearing. There
is still clearly a lot of work to do with automatic
image labeling, and we’re looking at how we can
prevent these types of mistakes from happening
in the future.“ – Google rep to Ars Technica
11. 11
But computer algorithms aren't perfect, and when
they identify images incorrectly, the results can
be disastrous.
Some concentration camp photos received
inappropriate tags, including "sport" and "jungle
gym."
Flickr had also been tagging some images of
people as "ape" and "animal," including a photo
of a black man named William taken by
photographer Corey Deshon, according to the
Guardian.
The photo service had also labeled a white
woman wearing face paint as "ape" and "animal,"
so Flickr's algorithm does not appear to be taking
a person's skin color into consideration when
auto-tagging them.
13. 13
● GEDMatch – free/open
genealogy genetics database
● Takes data from all major DNA
testing companies
● Allows cross-analysis
● Also can catch criminals
14. 14
● DNA from over 100 crime scenes uploaded
● Police create fake profiles, upload suspect data and triangulate
● Forced GEDMatch to change their Terms of Service to allow for criminal investigation
● Users have mixed reactions
- A few deleted their data
- Others were thankful
• [GEDMatch’s co-creator] says a woman wrote that her father was a serial killer, and she
wanted her data out there to give the families of his victim’s closure.
● Police also have CODIS – the government criminal DNA database
- Only looks at small snippets of DNA – 20 locations in human genome
- DNA Tests/GEDMatch contains full DNA sequences – 600,000 locations in human genome
15. 15
● Problems
- Is it legal? Yes, and admissible.
- Accuracy?
• Officers could use this evidence as ‘smoking gun’
• Need to strengthen case with other evidence
- How about those hidden family secrets?
• Children up for adoption
• Not the biological parent situations
• Do ‘lost’ family members want to be found?
- Who owns the data after the donor dies?
17. 17
● Many insurance companies are starting to offer fitness tracking discounts
● Subscribers provide fitness tracking data
● Insurance company provides discounts based on activity
● Same model used in car companies
● Problem:
- If health issue found, what are the ramifications?
• Cut policy?
• Raise rates?
- If pre-existing condition?
- What if subscriber cheats?
19. 19
● Mylan bought rights to Epipen in 2016 (originally invented in the 1970s).
● Pre-Mylan, Epipens cost $57
● Post-Mylan, Epipens cost $600 - this was a data driven decision
- Also created a generic for $300 – this was a public relations decision
● Problem:
- If market demand will support the higher cost, is that okay?
- What about folks who can’t afford even the generic?
- Could this impact other medicine costs?
21. 21
One of the ads, called "Supermarket," also
fools with a stereotype: It shows a father at
a market with his daughter getting the
ingredients for a traditional recipe, three-milk
cake (generally made with condensed, fresh
and evaporated milk). Latino men are
typically not depicted in such a place. So
why show a man in a market?
"Because Hispanic women love it," said Jeff
Manning, the executive director of the milk
board who worked for 25 years in the
advertising industry before taking the post in
1993.
"They love the idea. It is aspirational. They
look at it and say, 'That makes me feel
good,' " said Manning.
Editor's Notes
Source: https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/#127b50f96668
Target (and other companies) are smart enough to know when you are expecting a kid. In 2012 Forbes reported on the story that a family in Minneapolis where a baby coupon book was sent and addressed to their teen daughter. The father, being quite angry went down to the Target and demanded an apology. A few days later, the manager called the man back to apologize again but was actually apologized to. It turns out that the daughter was expecting. We in the data community know of course how they are able to.
So, have you done a DNA test? This is about as personal as information can get. Do you know what the company you took your test with is allowed to do with your data?