\(~\)

Book Selections

\(~\) \(~\)

Weapons of Math Destruction by Cathy O’Neil

This book is an excellent starter to the field of algorithmic bias, discussing the topic in general–with a focus on the dangers of hidden bias–and then proceeding into fascinating case studies from a variety of fields (examples include college rankings, hiring processes, and credit scores). If you want a simple overview that does not go into too much detail about any one particular field, this would be a great read for you. If you want to delve into more detail, it might not be the right fit.

\(~\) \(~\)

Automating Inequality by Virginia Eubanks

\(~\)

\(~\)

This book also focuses on case studies, though each is covered in much more extensive detail than anything you would find in Weapons of Math Destruction. The three topics covered are: 1) An Indiana algorithm that denies huge numbers of people of healthcare, food stamps, and cash benefits over tiny errors. 2) A Los Angeles algorithm used to determine which homeless people are prioritized for permanent housing. 3) A Pittsburgh child welfare algorithm that attempts to predict when children might be future victims of abuse, which leads to families being needlessly ripped apart. If you’re interested in detailed descriptions of the history, operation, and policy regarding algorithms, this would be an excellent book to read.

\(~\) \(~\)

The Rise of Big Data Policing by Andrew Ferguson

\(~\)

\(~\)

If you’re interested in focusing on data for policing specifically, this book provides an excellent overview of the ways in which police use big data, the history of that use, and the dangers of the adoption of advanced technologies by the police force, including discussions of bias and invasion of privacy. Topics that will be covered include: mass surveillance, facial recognition, social media monitoring, and algorithms that predict crime.

\(~\) \(~\)

Biased by Jennifer Eberhardt

\(~\)

\(~\)

If you’re looking for an overview that focuses a lot on solutions and actions you can take, this might be a good read. Biased discusses how racial bias infiltrates all parts of our society – neighborhoods, schools, offices, the criminal justice system, etc – and what we can do to address it. It is less algorithm/data focused than some of the other books mentioned here, but still addresses the topic throughout.

\(~\) \(~\)

Algorithms of Oppression by Safiya Umoja Noble

\(~\)

\(~\)

This book challenges the idea that popular search engines treat all ideas, identities, and activities equally. Through a focused look at Google search, Safiya Noble argues that private interests, combined with the almost-monopoly status of Google search leads to biased search algorithms that privilage white indiviuals while discriminating against people–and particularly women–of color.

\(~\)

Blogs and Articles

\(~\)

Non-technical overviews and examples of algorithmic bias

\(~\)

An Excellent Overview of Algorithmic Bias: https://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practices-and-policies-to-reduce-consumer-harms/

\(~\)

Law Review on “Big Data’s Disparate Impact”: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2477899

\(~\)

Case Studies in the Use of Big Data (Discusses problems, ways big data could be used to solve them, and challenges–often bias related–that come with doing so): https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/2016_0504_data_discrimination.pdf

\(~\)

Different Types of Bias, And How to Reduce Them: https://towardsdatascience.com/5-types-of-bias-how-to-eliminate-them-in-your-machine-learning-project-75959af9d3a0

\(~\)

Bias detectives: the researchers trying to make algorithms fair https://www.nature.com/articles/d41586-018-05469-3

\(~\)

“Out Data Bodies – Human Rights and Data Justice” Blog: https://www.odbproject.org/blog/

\(~\)

Example of a Biased Algorithm – Medical Referrals https://www.nature.com/articles/d41586-019-03228-6#ref-CR1

\(~\)

Example of a Biased Algorithm – Google Search https://time.com/5209144/google-search-engine-algorithm-bias-racism/

\(~\)

Social Inequality Will Not Be Solved By an App – by author Safiya Umoja Noble; an excerpt from her recent work titled Algorithms of Oppression https://www.wired.com/story/social-inequality-will-not-be-solved-by-an-app/

\(~\)

Technical Explanations of Bias

\(~\)

Measuring Fairness of Algorithms: https://towardsdatascience.com/a-tutorial-on-fairness-in-machine-learning-3ff8ba1040cb

\(~\)

Machine Learning Fairness in International Development (See Ch 3 & 4): https://d-lab.mit.edu/sites/default/files/inline-files/Exploring_fairness_in_machine_learning_for_international_development_28022020_pages.pdf

\(~\)

Data in the Criminal Justice System

\(~\)

Example of a Biased Algorithm – Criminal Sentencing: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

\(~\)

Different Ways Police Use Big Data and Related Concerns: https://www.annualreviews.org/doi/pdf/10.1146/annurev-criminol-062217-114209

\(~\)

Law Review – An Analysis of Big Data’s Use for Predictive Policing: https://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=6306&context=law_lawreview

\(~\)

Law Review – Challenging Racist Predictive Policing Algorithms Under the Equal Protection Clause https://www.nyulawreview.org/wp-content/uploads/2019/06/NYULawReview-94-3-ODonnell.pdf

\(~\)

Law Review – Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3333423

\(~\)

George Floyd Protests - Facial Recognition https://www.verdict.co.uk/facial-recognition-technology-racist-police-protests/

\(~\)

How the Movement for Black Lives Is Using Data to Transform Cities https://nextcity.org/daily/entry/how-the-movement-for-black-lives-will-transform-america-cities