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

www.ericafreas.bandcamp.com
https://rumbletowne.blogspot.com/

Song for a Mermaid

melt

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

You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq

InfoQ Podcast

Show Notes

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

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#79 Boy in Photo

Campaign fatigue, advertising and viral video, voting technology, intellectualism in the design community, the art of David Pease, the 1986 Mets

Episode 43: The End Is Near

Our dangerous reliance on big data: in an episode recorded before the election, Rich and Paul talk to Cathy O’Neil, author of  Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. They discuss Cathy’s origins in the math world, her years at a hedge fund on the brink of the 2008 financial crisis, the lack of transparency in the Department of Education’s data, and the various examples of “weapons of math destruction” in her book—all the ways that data is used to harm.

#0039: Cathy O'Neil on “Weapons of Math Destruction”

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.

Welcome Frances Kay Merinell 
<3 July 18, 2018

Frances Kay Merinell

Little Sunrise

Industry veterans Gina Trapani and Chris LoSacco chat with their friends and team about technology, business, and leadership. Decades of experience inform their insightful discussions about what digital transformation really means. Created by Postlight, the digital strategy, product, design, and engineering firm they lead in New York City.

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#0039: Cathy O'Neil on “Weapons of Math Destruction”

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