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Data Science & AI
Dr. Ahmet Bulut

Computer Science Department

Istanbul Sehir University
email: ahmetbulut@sehir.edu.tr
“Senator, we run ads”
“… Advertising has us chasing cars and clothes,
working jobs we hate so we can buy shit we don't
need…” —Tyler Durden
http://kirkakahoshi.com/Finding-My-Inner-Tyler-Durden.html
Advertising Spend
Source: Statista
Advertising
Advertising
shifted online!
https://www.iab.com/wp-content/uploads/2018/05/IAB-2017-Full-Year-Internet-Advertising-Revenue-Webinar-Presentation.pdf
• my friends and family, my shopping basket,
• my books, my photos, my music,
• my code, my meal, …
๏ I am all in!
Me and my, oh my!
https://www.octos.com.au/blog/4-marketing-lessons-learn-race-stops-nation/
Digital Me
• My cloud of information is used to construct a
Digital “Me”.
• Online advertisers advertise to “digital me”.
• What should we measure for representing users
“accurately”?
• We can measure (as in data collection) gender, age,
salary, occupation, location, device, browser, pleasure,
pain, …
“Measure what is measurable, and
make measurable what is not so.”
— Galileo Galilei
Measuring Happiness*
✤ Memory-based approach treats users retrospective
evaluations of past episodes and situations as valid
data.
✓ Moment-based approach derives the experienced
utility of an episode from real-time measures of 

the pleasure and pain that a user experienced
during that episode.
* Daniel Kahneman. (2000). “Experienced Utility and Objective Happiness: A Moment-Based Approach” in Choices, Values and Frames, pp. 673-692, Cambridge University Press.
• Everything that can be measured and collected will
eventually be measured and collected …*huh?what!
> excuse me?



• Should we be worried?
Privacy
Photography Credit: Jack Wallen
Trading Privacy for Value
• “There is no clear right and plenty of well-known
wrongs. I think what is ultimately going to matter is the
notion of value for data, and for a lot of these large
systems, it will be value for privacy, if you will.”
• “Nobody wants to give up their privacy, even
incrementally, for vanity purposes, but if you’re giving 

it up and realize that you’re giving up 10 pounds of
unwanted fat, or regaining three hours of sleep, then I
think people will just figure out how to cope with it and
move on.”



— Max Levchin, CEO of Affirm (PayPal Founder).
Problem of Trust
• We may have been abused without knowing it.

- Why was my insurance claim denied? 

- Why was I not approved for that last loan? 

- Were those decisions made by a system that was trained
on biased data, discriminating against me?
• In the start-up culture of the 21st century, we live by
the motto “move fast and break things.” What if
what gets broken is society*?
• Panel Question: is the problem of trust real, and if
so what are the consequences?
* https://www.oreilly.com/ideas/the-five-cs
Responsible Data Science
• We rarely talk about how to build a data product in
a responsible way:

- How can we treat others’ data as we would treat our
own?

- As a data scientist, have you actually thought about
how your own data might be used and abused? 
• Panel Question: how can we build data products
and services that use data ethically & responsibly?
Team Sport
• Many companies struggle with getting value out of
their data projects, most of which stay as PoCs.
• Data Analysts, Data Engineers, Data Scientists, …
There is lack of unity, and failure to work together.
• Panel Question: how do companies take a data
(science) project from lab to production
successfully?
Spirit
Intelligent Systems
• Systems that can explain their decisions.
• Systems that can provide meaningful notions of
their uncertainty.
• Systems that pursue long-term goals, and actively
collect data in service of those goals.
https://www.awakeningfromalzheimers.com/peoples-brains-are-shrinking-at-an-alarming-rate/
We have been myopic!
• We designed (recommendation) systems that
interact with one user at a time, that make one
recommendation at a time.
• And these systems have grown up to be myopic; we
recommend

the same book to every reader,

the same movie to every kid on the block,

the same restaurant to every diner,

the same street to every driver, 

…
Source: Thinkstock Images
• “We need to design systems that create markets
where data flows between producers and
consumers, and economic value can flow.” 

— Michael I. Jordan of UC Berkeley.
• Instead of rich get richer, we can 

have the pie get bigger.
• Panel Question: how can we 

interconnect the web of data, its 

agents, and their decisions to 

enlarge the pie?
Data Economy
Photography Credit: Elise Bauer

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Data Economy: Lessons learned and the Road ahead!

  • 1. Data Science & AI Dr. Ahmet Bulut
 Computer Science Department
 Istanbul Sehir University email: ahmetbulut@sehir.edu.tr
  • 3. “… Advertising has us chasing cars and clothes, working jobs we hate so we can buy shit we don't need…” —Tyler Durden http://kirkakahoshi.com/Finding-My-Inner-Tyler-Durden.html
  • 6.
  • 7. • my friends and family, my shopping basket, • my books, my photos, my music, • my code, my meal, … ๏ I am all in! Me and my, oh my! https://www.octos.com.au/blog/4-marketing-lessons-learn-race-stops-nation/
  • 8. Digital Me • My cloud of information is used to construct a Digital “Me”. • Online advertisers advertise to “digital me”.
  • 9. • What should we measure for representing users “accurately”? • We can measure (as in data collection) gender, age, salary, occupation, location, device, browser, pleasure, pain, … “Measure what is measurable, and make measurable what is not so.” — Galileo Galilei
  • 10. Measuring Happiness* ✤ Memory-based approach treats users retrospective evaluations of past episodes and situations as valid data. ✓ Moment-based approach derives the experienced utility of an episode from real-time measures of 
 the pleasure and pain that a user experienced during that episode. * Daniel Kahneman. (2000). “Experienced Utility and Objective Happiness: A Moment-Based Approach” in Choices, Values and Frames, pp. 673-692, Cambridge University Press.
  • 11. • Everything that can be measured and collected will eventually be measured and collected …*huh?what! > excuse me?
 
 • Should we be worried? Privacy Photography Credit: Jack Wallen
  • 12. Trading Privacy for Value • “There is no clear right and plenty of well-known wrongs. I think what is ultimately going to matter is the notion of value for data, and for a lot of these large systems, it will be value for privacy, if you will.” • “Nobody wants to give up their privacy, even incrementally, for vanity purposes, but if you’re giving 
 it up and realize that you’re giving up 10 pounds of unwanted fat, or regaining three hours of sleep, then I think people will just figure out how to cope with it and move on.”
 
 — Max Levchin, CEO of Affirm (PayPal Founder).
  • 13. Problem of Trust • We may have been abused without knowing it.
 - Why was my insurance claim denied? 
 - Why was I not approved for that last loan? 
 - Were those decisions made by a system that was trained on biased data, discriminating against me? • In the start-up culture of the 21st century, we live by the motto “move fast and break things.” What if what gets broken is society*? • Panel Question: is the problem of trust real, and if so what are the consequences? * https://www.oreilly.com/ideas/the-five-cs
  • 14. Responsible Data Science • We rarely talk about how to build a data product in a responsible way:
 - How can we treat others’ data as we would treat our own?
 - As a data scientist, have you actually thought about how your own data might be used and abused?  • Panel Question: how can we build data products and services that use data ethically & responsibly?
  • 15. Team Sport • Many companies struggle with getting value out of their data projects, most of which stay as PoCs. • Data Analysts, Data Engineers, Data Scientists, … There is lack of unity, and failure to work together. • Panel Question: how do companies take a data (science) project from lab to production successfully? Spirit
  • 16. Intelligent Systems • Systems that can explain their decisions. • Systems that can provide meaningful notions of their uncertainty. • Systems that pursue long-term goals, and actively collect data in service of those goals. https://www.awakeningfromalzheimers.com/peoples-brains-are-shrinking-at-an-alarming-rate/
  • 17. We have been myopic! • We designed (recommendation) systems that interact with one user at a time, that make one recommendation at a time. • And these systems have grown up to be myopic; we recommend
 the same book to every reader,
 the same movie to every kid on the block,
 the same restaurant to every diner,
 the same street to every driver, 
 … Source: Thinkstock Images
  • 18. • “We need to design systems that create markets where data flows between producers and consumers, and economic value can flow.” 
 — Michael I. Jordan of UC Berkeley. • Instead of rich get richer, we can 
 have the pie get bigger. • Panel Question: how can we 
 interconnect the web of data, its 
 agents, and their decisions to 
 enlarge the pie? Data Economy Photography Credit: Elise Bauer