Investigating opaque algorithms can be a challenge. This page has some references to examples of journalism that investigate, audit, or critique algorithms, some methodologically helpful resources, and some guidance on how you might use Freedom of Information (FOI) requests in the U.S. to find out more information.

You can also join the Algorithmic Accountability Reporting mailing list maintained by Algorithm Watch if you have questions or want to share examples or other resources with the community.

Examples of Journalistic Investigations

  • Swinging the Vote? TheMarkup. 2020 {Link} {Methods}
  • Aggression Detectors: The Unproven, Invasive Surveillance Technology Schools Are Using to Monitor Students. ProPublica. 2019 [Link] [Methods]
  • What a Report from Germany Teaches Us About Investigating Algorithms. Columbia Journalism Review. 2019. [Link]
  • Keep Track Of Who Facebook Thinks You Know With This Nifty Tool. Gizmodo. 2018. [Link]
  • What Happens When An Algorithm Cuts Your Health Care. The Verge. 2018. [Link]
  • Inside the Algorithm That Tries to Predict Gun Violence in Chicago. New York Times. 2017. [Link]
  • ProPublica Seeks Source Code for New York City’s Disputed DNA Software. ProPublica. 2017. [Link]
  • Minority Neighborhoods Pay Higher Car Insurance Premiums Than White Areas With the Same Risk. ProPublica. 2017. [Link]
  • Machine Bias. ProPublica. 2016. [Link]
  • Uber seems to offer better service in areas with more white people. That raises some tough questions. Washington Post. 2016.  [Link
  • Amazon Says It Puts Customers First. But Its Pricing Algorithm Doesn’t. ProPublica. 2016. [Link]
  • How Google Shapes the News You See About the Candidates. Slate. 2016.  [Link
  • Googling Politics. Slate. 2016. [Link]
  • Be Suspicious Of Online Movie Ratings, Especially Fandango’s. FiveThirtyEight. 2015. [Link]
  • The Tiger Mom Tax: Asians Are Nearly Twice as Likely to Get a Higher Price from Princeton Review. ProPublica. 2015.  [Link]
  • Why Google Search Results Favor Democrats. Slate. 2015.  [Link
  • Sex, Violence, and Autocomplete Algorithms. Slate. 2013. [Link]
  • The Apple ‘Kill List’: What Your iPhone Doesn’t Want You to Type. The Daily Beast, 2013. [Link]
  • Readying for Sandy, NJ Transit erred in modeling storm. Reuters. [Link]
  • Websites Vary Prices, Deals Based on Users’ Information. Wall Street Journal. 2012.  [Link]
  • Message Machine: Reverse Engineering the 2012 Campaign. ProPublica. 2012. [Link]

Method References

  • How Journalists Can Systematically Critique Algorithms. Proc. Computation + Journalism Symposium. 2020. {Link}
  • Acing the Algorithmic Beat, Journalism’s Next Frontier. Nieman Lab. 2019. [Link]
  • “The Algorithms Beat” in Data Journalism Handbook. 2018. [Link]
  • Algorithmic Accountability Policy Toolkit. AI Now. 2018. [Link]
  • Data Journalism and the Computer Fraud and Abuse Act: Tips for Moving Forward in an Uncertain Landscape. ACLU. 2017. [Link]
  • How to Report on Algorithms Even If You’re Not a Data Whiz. Columbia Journalism Review. 2017. [Link]
  • How to Hold Algorithms Accountable. MIT Technology Review. 2016. [Link]
  • Algorithmic Accountability: Journalistic Investigation of Computational Power Structures. Digital Journalism. 2015. [Link]
  • Algorithmic Transparency in the News Media. Digital Journalism. 2016. [Link]
  • Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms.  preconference at the 64th Annual Meeting of the International Communication Association. 2014. [Link]
  • Auditing Algorithms @ Northeastern University. [Link]

Background References

  • Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies. Feb. 2020. {Link}
  • AI in Adjudication and Administration: A Status Report on Governmental Use of Algorithmic Tools in the United States. Brooklyn Law Review. 2020. {Link}
  • “Algorithmic Accountability Reporting” in Automating the News: How Algorithms are Rewriting the Media. Harvard University Press. 2019. [Link]
  • Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. 2018. [Link]
  • Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. 2016. [Link]
  • The Black Box Society: The Secret Algorithms That Control Money and Information. 2015. [Link]
  • Algorithmic Accountability Reporting: On the Investigation of Black Boxes. Tow Center for Digital Journalism. 2014. [Link]

Relevant Conferences

  • Computation + Journalism Symposium [Link]
  • FAccT* Conference [Link]
  • Fairness, Accountability and Transparency in Machine Learning (FATML). [Link]
  • Data and Algorithmic Transparency (DAT). [Link]
  • AAAI/ACM Conference on AI, Ethics, and Society [Link]

Related Projects & Repositories

  • Data Justice Lab. Data Scores [Link]
  • Muckrock. Algorithmic Control: Automated Decisionmaking in America’s Cities [Link]
  • UPenn. Optimizing Government: Policy Challenges in the Machine Learning Age [Link]
  • government repositories tagged with “artificial intelligence” [Link]
  • The original archived Algorithm Tips database is still available but is no longer updated [Link]

How to FOIA an Algorithm

In the Fall of 2015 we instructed students at the University of Maryland to use Freedom of Information (FOI) requests to obtain information about criminal risk assessment algorithms in use in different states in the U.S. This endeavor is detailed in this article:

  • We need to know the algorithms the government uses to make important decisions about us. The Conversation. 2016. [Link]

Here’s the language that we suggested students use in their requests:

  • Copies of any open-source statistical assessment tools and any contracts for digital or statistical-assessment tools or related services used in bail decisions.
  • Copies of any open-source statistical assessment tools and any contracts for digital or statistical assessment tools or  related services used in sentencing decisions.
  • Copies of any open-source statistical assessment tools and any contracts for digital or statistical assessment tools or  related services used in parole and probation decisions.
  • Variables that form the basis of the assessment tools.
  • Data used as the basis for training the assessment tools.
  • Algorithms or algorithmic processes applied to bail, sentencing and/or parole and probation decisions.
  • Source code for such algorithms.
  • Mathematical descriptions of such algorithms or assessments.
  • Assessments or evaluations of such digital tools and/or algorithms.
  • Any memos or communications or reports dealing with these algorithms.

There are a growing number of FOI requests for algorithms that people have submitted via the Muckrock platform. Examining the approaches and language used in those requests may also be instructive. There are a variety of example requests available on the Muckrock platform via their Uncovering Algorithms project. 

Academic research has also begun to FOIA algorithms. See, for instance:

  • Algorithmic Transparency for the Smart City. Yale Journal of Law & Technology. 2018. [Link]
  • Opening the Government’s Black Boxes: Freedom of Information and Algorithmic Accountability. Information, Communication, & Society. 2018. [Link]