Abstract
The current discourse on fairness, accountability, and transparency in machine learning is driven by two competing narratives: sociotechnical dogmatism, which holds that society is full of inefficiencies and imperfections that can only be solved by better algorithms; and sociotechnical skepticism, which opposes many instances of automation on principle. Both perspectives, we argue, are reductive and unhelpful. In this chapter, we review a large, diverse body of literature in an attempt to move beyond this restrictive duality, toward a pragmatic synthesis that emphasizes the central role of context and agency in evaluating new and emerging technologies. We show how epistemological and ethical considerations are inextricably intertwined in contemporary debates on algorithmic bias and explainability. We trace the dialectical interplay between dogmatic and skeptical narratives across disciplines, merging insights from social theory and philosophy. We review a number of theories of explanation, ultimately endorsing a sociotechnical pragmatism that combines elements of Floridi’s levelism and Mayo’s reliabilism to place a special emphasis on notions of agency and trust. We conclude that this hybrid does more to promote fairness, accountability, and transparency in machine learning than dogmatic or skeptical alternatives.
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Notes
- 1.
In 2018, the conference was called FAT; in 2019, it was rebranded FAT* (pronounced “FAT star”). As of 2020, it goes by the current name, FAccT, which we will use henceforth.
- 2.
As an exegetical aside, we observe that there is some dispute over the true extent to which Marx was in fact a technological determinist, at least in the uncompromising sense that the label is occasionally employed by modern authors. See (Bimber, 1990).
- 3.
The concept now referred to as Pareto frontier (also Pareto efficiency) is attributed to the Italian economist and sociologist Vilfredo Pareto and his work Course in Political Economy and Manual of Political Economy. For a more contemporary introduction to the concept, we recommend (Lockwood, 2017).
- 4.
Hempel (1965) proposes a new class of explanations called inductive-statistical (IS) to accommodate such cases, but the IS model struggles to account for low probability events. The alternatives analyzed below are better equipped to handle statistical explanations.
- 5.
This is a non-trivial assumption. According to the Duhem-Quine thesis, Popper’s falsificationism fails precisely because it is impossible to design a test that isolates the effects of L. We can always salvage any theory no matter how anomalous the observations E, provided we make sufficient amendments to the conjunct S, e.g. adding auxiliary hypotheses. See (Duhem, 1954; Quine, 1951).
- 6.
Technically, this example should be formalized in first-order logic to quantify predicates over sets. We stick with propositional logic here for consistency with previous sections and ease of presentation. The example is sufficiently simple and familiar that we doubt the ambiguity will lead to any confusion.
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Watson, D., Mökander, J. (2023). In Defense of Sociotechnical Pragmatism. In: Mazzi, F. (eds) The 2022 Yearbook of the Digital Governance Research Group. Digital Ethics Lab Yearbook. Springer, Cham. https://doi.org/10.1007/978-3-031-28678-0_10
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