Numbers Can Lie: When algorithms work perfectly but fail miserably

The famous saying, “numbers don’t lie,” might work when reporting the score of a football game, but even then, they don’t tell the whole story. In this lecture, you will learn the basics behind data science and discover how easily human bias can be encoded into computer models. The results of algorithms have implications for not only who may obtain a fair loan, but with who stays in prison and who’s released, and who will be favored by machine learning “decisions.” With so many parts of our lives impacted by Big Data, how do scientists balance algorithms and ethics?
Mar 29, 2024

Top 10 Learning Moments

  1. To develop technologies that can have a meaningful impact, we have to be there. We have to be imbedded in those communities to understand. — Nitesh Chawla
  2. Facial recognition has become much more effective. It has greater accuracy and the error rates have gone down substantially. That’s made it go from something that people play around with in the lab to things that people actually use in the real world. — Roger Woodard
  3. Data analysis is the first step, but really pulling out the story behind the data is often such a big part of it. — Roger Woodard
  4. Research is very intellectually refreshing for me and I can’t stand to not have the questions we are working on go unanswered. — Marie Lynn Miranda
  5. Biometrics is the idea that we are going to measure something about a living entity in order to recognize the identity of that person. — Kevin Bowyer
  6. With the power of data also comes great responsibility. It is essential to understand and appreciate the societal and ethical implications of both the data and the models developed on the data. — Nitesh Chawla
  7. Everything that we do today touches data or creates data. — Nitesh Chawla
  8. All of us in biometrics think nothing is like what you see on C.S.I or any similar show. — Kevin Bowyer
  9. Images of 2 different women look more similar on average than images of 2 different men. — Kevin Bowyer
  10. Data science can be democratized across all disciplines. — Nitesh Chawla

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Featured Speakers

Roger Woodard, Teaching Professor in the Department of Applied and Computational Mathematics and Statistics and Director of the Online MS in Data Science, University of Notre Dame

Nitesh Chawla, the Frank M. Freimann Professor in the Department of Computer Science and Engineering and Founding Director of the Lucy Family Institute for Data and Society, University of Notre Dame

Marie Lynn Miranda, Charles and Jill Fischer Provost and Professor of Applied and Computational Mathematics and Statistics, University of Notre Dame

Kevin Bowyer, Shubmehl-Prein Professor of Computer Science and Engineering, University of Notre Dame

When Numbers Lie: An Overview

Learn what data science tools can do and the ethical challenges they bring about. We’ll describe artificial intelligence, algorithms, machine learning, and neural networks — terms you may have heard before, but aren’t completely familiar with.

Tech Ethics at Notre Dame

Algorithms and data shape our technologies and lives, and Notre Dame is providing an education on a range of technology and ethics issues through interdisciplinary research.

“With the power of data also comes great responsibility. It is essential to understand and appreciate the societal and ethical implications of both the data and the models developed on the data”

– Nitesh Chawla