A toolkit of dilemmas: Beyond debiasing and fairness formulas for responsible AI/ML
This addresses the need for more context-sensitive and socially aware methods in responsible AI/ML, offering a conceptual framework rather than incremental technical improvements.
The paper tackles the problem of simplistic approaches to fairness and ethics in AI/ML by introducing a tripartite decision-making toolkit based on dilemmas, such as data availability vs. problem statements and scalability vs. contextualizability, to advocate for more situated and creative reasoning beyond formulaic bias elimination.
Approaches to fair and ethical AI have recently fell under the scrutiny of the emerging, chiefly qualitative, field of critical data studies, placing emphasis on the lack of sensitivity to context and complex social phenomena of such interventions. We employ some of these lessons to introduce a tripartite decision-making toolkit, informed by dilemmas encountered in the pursuit of responsible AI/ML. These are: (a) the opportunity dilemma between the availability of data shaping problem statements vs problem statements shaping data; (b) the trade-off between scalability and contextualizability (too much data versus too specific data); and (c) the epistemic positioning between the pragmatic technical objectivism and the reflexive relativism in acknowledging the social. This paper advocates for a situated reasoning and creative engagement with the dilemmas surrounding responsible algorithmic/data-driven systems, and going beyond the formulaic bias elimination and ethics operationalization narratives found in the fair-AI literature.