Seeing Like an Algorithmic Error: What Are Algorithmic Mistakes, Why Do They Matter, How Might They Be Public Problems?

Article Source: Yale Journal of Law & Technology, Vol. 24, pp. 1-21, 2022
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The errors made by machine learning-based systems such as remote proctoring tools can reveal deeper economic and policy issues, such as bias against students of color or low-income students.


Policy Relevance:

Some errors present deeper public problems and require collective solutions.


Key Takeaways:
  • Social problems such as algorithmic errors are made, not found; they are a product of people, perspectives, experiences, and assumptions.
    • Some errors are idiosyncratic one-off mistakes, easily corrected.
    • Other errors are a result of systemic structural forces.
  • Some algorithmic errors make good public problems, revealing systemic failures and how they can be remedied; policymakers would benefit from classifying errors more precisely.
  • A study of error involving a university’s use of remote proctoring tools (RPTs) illustrates the different ways in which errors are perceived and corrected; RPTs use machine learning to detect cheating by students while taking tests online.
  • When using the RPT, dark-skinned students were told to use extra lighting and hold their heads still to avoid being flagged as cheaters; also, the RPT required exams to be taken in rooms free of audio and visual distractions.
    • Technically, the RPT system erred in being unable to detect some faces accurately.
    • The requirement that rooms be free of distractions is a type of error, as it biases the system against low-income students living in shared housing.
    • The RPT might erroneously cause faculty to believe that students of color cheat more often.
    • One could see the university’s economic model, which depends on large classes using standardized tests for evaluation, as another type of error.
  • In response to concerns about the RPT, the university discontinued use of one proctoring system; however, alternatively, the university could have decided that mass surveillance was incompatible with the school’s educational mission, reducing class sizes and/or stopping use of RPTs entirely.
  • Depending on how an error is framed, different aspects of socio-technical systems appear more alterable or worthy of reform.
    • One may more easily make a biased dataset larger and more inclusive than question whether algorithmic surveillance is acceptable at all.
    • One may more easily ask students of color to illuminate themselves than ask how learning models that require surveillance respect autonomy and ethical standards.
  • Precision in diagnosing algorithmic errors will create communities of people who diagnose and remedy errors similarly.
  • When correction of algorithmic errors requires collective action, the errors become public problems.



Mike Ananny

About Mike Ananny

Mike Ananny is Associate Professor of Communication and Journalism at USC’s Annenberg School for Communication and Journalism. He studies how technologies and cultures of media production have the power to shape public life. His research focuses on the public ethics of communication systems, specifically intersections of journalism practice and technology design, the sociotechnical dynamics of networked news infrastructures, and the power of algorithmic systems.