This is further reflected in an increasing number of job ads for AI ethicists that list a computer science degree as a requirement, “prioritising technical computer science infrastructure over the social science skills that can evaluate AI’s social impact. (Gallery Britto image under Creative Commons Attribution-Share Alike 4.0 International) In a paper on exclusionary practices in AI ethics, an interdisciplinary team wrote of the “indifference, devaluation, and lack of mutual support between CS and humanistic social science (HSS), the myth of technologists as ‘ethical unicorns’ that can do it all, though their disciplinary tools are ultimately limited.” There are challenges when mixing computer scientsits with social scientists. Even when cross-disciplinary partnerships occur, they often fall into “normal disciplinary divisions of labour: social scientists observe, data scientists make social scientists do ethics, data scientists do science social scientists do the incalculable, data scientists do the calculable.” The solution is not for computer scientists to absorb a shallow understanding of the social sciences, but for deeper collaborations. Unfortunately, there is often a large divide between computer scientists and social scientists, with over-simplified assumptions and fundamental misunderstandings of one another. Attempting to measure racial bias leads to qualitative questions Similarly, qualitative research is necessary to understand AI systems operating in society: evaluating system performance beyond what can be captured in short term metrics, understanding what is missed by large-scale studies (which can elide details and overlook outliers), and shedding light on the circumstances in which data is produced (often by crowd-sourced or poorly paid workers).
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Throughout AI, qualitative decisions are made about what metrics to optimise for, which categories to use, how to define their bounds, who applies the labels.
Following the thread of any seemingly quantitative issue around AI ethics quickly leads to a host of qualitative questions. But how was race categorised– through a census record, a police officer’s guess, or by an annotator? Each possible answer raises another set of questions. You might calculate model performance across groups with different races.
Suppose you wanted to find out whether a machine learning system being adopted - to recruit candidates, lend money, or predict future criminality - exhibited racial bias. Harvard Social Scientist and Statistician Gary King “All research is qualitative some is also quantitative” Qualitative humanities research is crucial to AI Louisa Bartolo and Rachel Thomas
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