  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.
