# A toolkit of dilemmas: Beyond debiasing and fairness formulas for responsible AI/ML

arXiv: https://arxiv.org/abs/2303.01930 · HTML: https://mlsanity.com/paper/2303.01930/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Large image datasets: A pyrrhic win for computer vision?](https://mlsanity.com/paper/2006.16923/) (2020)
- [On instabilities of deep learning in image reconstruction - Does AI come at a cost?](https://mlsanity.com/paper/1902.05300/) (2019)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [The Data-Production Dispositif](https://mlsanity.com/paper/2205.11963/) (2022)

## Built on by (most cited first, 2 of 2)

- [Data Ethics Emergency Drill: A Toolbox for Discussing Responsible AI for Industry Teams](https://mlsanity.com/paper/2403.10438/) (2024)
- [SEED-SET: Scalable Evolving Experimental Design for System-level Ethical Testing](https://mlsanity.com/paper/2603.01630/) (2026)

## Abstract (arXiv)

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.