Algorithmic realism: expanding the boundaries of algorithmic thought

By Ben Green, Salomé Viljöen. 2020

172 citations recorded by the source corpus.

Although computer scientists are eager to help address social problems, the field faces a growing awareness that many well-intentioned applications of algorithms in social contexts have led to significant harm. We argue that addressing this gap between the field's desire to do good and the harmful impacts of many of its interventions requires looking to the epistemic and methodological underpinnings of algorithms. We diagnose the dominant mode of algorithmic reasoning as "algorithmic formalism" and describe how formalist orientations lead to harmful algorithmic interventions. Addressing these harms requires pursuing a new mode of algorithmic thinking that is attentive to the internal limits of algorithms and to the social concerns that fall beyond the bounds of algorithmic formalism. To understand what a methodological evolution beyond formalism looks like and what it may achieve, we turn to the twentieth century evolution in American legal thought from legal formalism to legal realism. Drawing on the lessons of legal realism, we propose a new mode of algorithmic thinking---"algorithmic realism"---that provides tools for computer scientists to account for the realities of social life and of algorithmic impacts. These realist approaches, although not foolproof, will better equip computer scientists to reduce algorithmic harms and to reason well about doing good.

Subfields: Artificial Intelligence, Safety Research, AI Ethics, Fairness & Society, Social Sciences & Humanities

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Citations in the corpus

Re-imagining Algorithmic Fairness in India and Beyond (2021, background): “The community also often repurposes terminology from US legal domains, such as “disparate impact”, “disparate treatment”, and “equal opportunity”, or use them as points of triangulation, in order to compare technical properties of fairness through analogy with legal concepts [81, 82].”
What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness (2020, background): “interest of those it is about and that the fairness achieved will be more than a satisfied formalized constraint [23, 30, 50, 58, 61].”
Consumers and Artificial Intelligence: An Experiential Perspective (2020, unknown): “Instead of considering algorithms as neutral tools, AI designers should recognize that their interventions are “inherently political” and interrogate themselves on “the relationship between their design choices, their professional role, and their vision of the good” (Green and Viljoen 2020, p. 26).”
Algorithmic Bias in Education (2021, background): “Work by Green and colleagues (Green, 2020; Green & Hu, 2018; Green & Viljoen, 2020), for example, suggests that the conflation of fairness defined as statistical measures of group parity with fairness in society creates its own obstacles to addressing social inequities.”
Expanding Explainability: Towards Social Transparency in AI systems (2021, background): “Certain techno-centric pitfalls that are deeply embedded in AI and Computer Science, such as Solutionism (always seeking technical solutions) and Formalism (seeking abstract, mathematical solutions) [32, 87], are likely to further widen these gaps.”
Evaluating Machines by their Real-World Language Use (2020, background): “We as a community should (continue to) study and be mindful of these kinds of dual use issues (Hovy and Spruit, 2016; Green and Viljoen, 2020).”
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (2022, background): “With increased awareness of the need to anticipate harms early in product development [195], designers and researchers are central actors in pursuing harm reduction [40, 58, 97].”
Data Science as Political Action: Grounding Data Science in a Politics of Justice (2018, background): “Recent surveys show public support for such reforms[66, 67].”
Back to the Future: On Potential Histories in NLP (2022, result): “Such work stands in contrast to a large body of literature, which uncritically collects and uses data, with the result of producing ML models that recreate discriminatory contemporaries (e.g. Green and Viljoen, 2020; Gitelman, 2013).”
TuringAdvice: A Generative and Dynamic Evaluation of Language Use (2021, background): “Moreover, there are signiﬁcant dual-use risks with models that understand language (Hovy and Spruit, 2016; Green and Viljoen, 2020).”
Sociotechnical Harms: Scoping a Taxonomy for Harm Reduction (2022, background): “With increased awareness of the need to anticipate harms early in product development [172], designers and researchers are central actors in pursuing harm reduction [34, 49, 87].”