Algorithms, the Internet, STEAM, and the Continuing Struggle for Justice

Sometimes when we say an algorithm discriminates, we mean it can tell the difference between two types of items.  But in this episode, we'll talk about another, more troublesome side to discrimination: algorithms can be... racist?  Sexist?  Ageist?  Yes to all of the above.  It's an important thing to be aware of, especially when doing people-centered data science.  We'll discuss how and why this happens, and what solutions are out there (or not).

Relevant Links:
http://www.nytimes.com/2015/07/10/upshot/when-algorithms-discriminate.html
http://techcrunch.com/2015/08/02/machine-learning-and-human-bias-an-uneasy-pair/
http://www.sciencefriday.com/segments/why-machines-discriminate-and-how-to-fix-them/
https://medium.com/@geomblog/when-an-algorithm-isn-t-2b9fe01b9bb5#.auxqi5srz




Discriminatory Algorithms


Algorithms have become a powerful force in the world, but for all the impressive good they do, they sometimes show some worrying tendencies. Algorithms that discriminate are a problem that nobody's found a solution for yet. This week, we discuss why some algorithms appear to be racist, and whether there's anything that can be done about it.

The Techdirt Podcast

Is Your Algorithm Racist?

In this week's podcast InfoQ’s editor-in-chief Charles Humble talks to Data Scientist Cathy O’Neil. O'Neil is the author of the blog mathbabe.org. She was the former Director of the Lede Program in Data Practices at Columbia University Graduate School of Journalism, Tow Center and was employed as Data Science Consultant at Johnson Research Labs. O'Neil earned a mathematics Ph.D. from Harvard University. Topics discussed include her book “Weapons of Math Destruction,” predictive policing models, the teacher value added model, approaches to auditing algorithms and whether government regulation of the field is needed.

Why listen to this podcast:

 - There is a class of pernicious big data algorithms that are increasingly controlling society but are not open to scrutiny.
 - Flawed data can result in an algorithm that is, for instance, racist and sexist.  For example, the data used in predictive policing models is racist. But people tend to be overly trusting of algorithms because they are mathematical.
 - Data scientists have to make ethical decisions even if they don’t acknowledge it. Often problems stem from an abdication of responsibility.
 - Auditing for algorithms is still a very young field with ongoing academic research exploring approaches.
 - Government regulation of the industry may well be required.

Notes and links can be found on http://bit.ly/2eYVb9q

Weapons of math destruction

0m:43s - The central thesis of the book is that whilst not all algorithms are bad, there is a class of pernicious big data algorithms that are increasingly controlling society.
1m:32s - The classes of algorithm that O'Neil is concerned about - the weapons of math destruction - have three characteristics: they are widespread and impact on important decisions like whether someone can go to college or get a job, they are somehow secret so that the people who are being targeted don’t know they are being scored or don’t understand how their score is computed; and the third characteristic is they are destructive - they ruin lives.
2m:51s - These characteristics undermine the original intention of the algorithm, which is often trying to solve big society problems with the help of data.

More on this:

Quick scan our curated show notes on InfoQ. http://bit.ly/2eYVb9q

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InfoQ Podcast

Show Notes

Cathy O'Neil on Pernicious Machine Learning Algorithms and How to Audit Them

ThePLUG Data 4 Black Lives Interview with Cathy O'Neil

When we covered the Word2Vec algorithm for embedding words, we mentioned parenthetically that the word embeddings it produces can sometimes be a little bit less than ideal--in particular, gender bias from our society can creep into the embeddings and give results that are sexist. For example, occupational words like "doctor" and "nurse" are more highly aligned with "man" or "woman," which can create problems because these word embeddings are used in algorithms that help people find information or make decisions. However, a group of researchers has released a new paper detailing ways to de-bias the embeddings, so we retain gender info that's not particularly problematic (for example, "king" vs. "queen") while correcting bias.

Debiasing Word Embeddings

Writer Sara Wachter-Boettcher explains how racial and sexual bias finds its way from society into digital products (and back into society) as a result of the tech industry's unwillingness to examine its own values, and they way their work engages with and shapes society beyond the mostly white, male corporate campuses of Silicon Valley.

Sara is author of "Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech" from W.W. Norton: http://books.wwnorton.com/books/978-0-393-63463-1/

Social inequality is coded deep within the devices of Silicon Valley.

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ThePLUG Data 4 Black Lives Interview with Cathy O'Neil

theplug