# Towards a Non-Ideal Methodological Framework for Responsible ML

arXiv: https://arxiv.org/abs/2401.11131 · HTML: https://mlsanity.com/paper/2401.11131/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2024.

## Built on by (most cited first, 4 of 4)

- [Talking About the Assumption in the Room](https://mlsanity.com/paper/2502.13268/) (2025)
- [Beyond Predictive Algorithms in Child Welfare](https://mlsanity.com/paper/2403.05573/) (2024)
- [Advancing Data Equity: Practitioner Responsibility and Accountability in NLP Data Practices](https://mlsanity.com/paper/2508.10071/) (2025)
- [What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research](https://mlsanity.com/paper/2608.10431/) (2026)

## Abstract (arXiv)

Though ML practitioners increasingly employ various Responsible ML (RML) strategies, their methodological approach in practice is still unclear. In particular, the constraints, assumptions, and choices of practitioners with technical duties -- such as developers, engineers, and data scientists -- are often implicit, subtle, and under-scrutinized in HCI and related fields. We interviewed 22 technically oriented ML practitioners across seven domains to understand the characteristics of their methodological approaches to RML through the lens of ideal and non-ideal theorizing of fairness. We find that practitioners' methodological approaches fall along a spectrum of idealization. While they structured their approaches through ideal theorizing, such as by abstracting RML workflow from the inquiry of applicability of ML, they did not pay deliberate attention and systematically documented their non-ideal approaches, such as diagnosing imperfect conditions. We end our paper with a discussion of a new methodological approach, inspired by elements of non-ideal theory, to structure technical practitioners' RML process and facilitate collaboration with other stakeholders.