Moving beyond computational questions in digital ethics research
In the ever expanding digital ethics literature, a number of researchers have been advocating a turn away from enticing technical questions—how to mathematically define fairness, for example—and towards a more expansive, foundational approach to the ethics of designing digital decision systems.
A 2018 paper by RODRIGO OCHIGAME, CHELSEA BARABAS, KARTHIK DINAKAR, MADARS VIRZA, and JOICHI ITO is an exemplary paper along these lines. The authors dissect the three most-discussed categories in the digital ethics space—fairness, interpretability, and accuracy—and argue that current approaches to these topics may unwittingly amount to a legitimation system for unjust practices. From the introduction:
“To contend with issues of fairness and interpretability, it is necessary to change the core methods and practices of machine learning. But the necessary changes go beyond those proposed by the existing literature on fair and interpretable machine learning. To date, ML researchers have generally relied on reductive understandings of fairness and interpretability, as well as a limited understanding of accuracy. This is a consequence of viewing these complex ethical, political, and epistemological issues as strictly computational problems. Fairness becomes a mathematical property of classification algorithms. Interpretability becomes the mere exposition of an algorithm as a sequence of steps or a combination of factors. Accuracy becomes a simple matter of ROC curves.
In order to deepen our understandings of fairness, interpretability, and accuracy, we should avoid reductionism and consider aspects of ML practice that are largely overlooked. While researchers devote significant attention to computational processes, they often lack rigor in other crucial aspects of ML practice. Accuracy requires close scrutiny not only of the computational processes that generate models but also of the historical processes that generate data. Interpretability requires rigorous explanations of the background assumptions of models. And any claim of fairness requires a critical evaluation of the ethical and political implications of deploying a model in a specific social context.
Ultimately, the main outcome of research on fair and interpretable machine learning might be to provide easy answers to concerns of regulatory compliance and public controversy"