When not to design, build, or deploy

By Solon Barocas, Asia J. Biega, Benjamin Fish, Jędrzej Niklas, Luke Stark. 2020

40 citations recorded by the source corpus.

Recent debate within the FAT* community has focused on how the field conceptualizes the problems it seeks to address, what approach the field should take in attempting to address these problems, and whether the field should even pursue some of the proposed remedies. Questions regarding when not to design, build, or deploy a technology are perhaps the most common expression of this trend. Identifying the problems to address is inextricably linked to the broader question of how to collectively make decisions about what technologies our societies need and want.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Information Systems and Management, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Hal Daumé Iii
2. Hanna M. Wallach
3. Patrick Hall
4. Solon Barocas
5. Su Lin Blodgett
6. Trevor Darrell
7. Kate Saenko
8. Jiashi Feng

In Electrical and Electronic Engineering

1. Hal Daumé Iii

In Information Systems and Management

1. Hanna M. Wallach
2. Solon Barocas
3. Adrian Weller

In AI Ethics, Fairness & Society

1. Hanna M. Wallach
2. Hal Daumé Iii
3. Patrick Hall
4. Solon Barocas
5. Su Lin Blodgett
6. Adrian Weller
7. Berk Ustun

Citations in the corpus

• Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “…and events have begun to explore how to empower stakeholders in the development and deployment of technology (Vaccaro et al., 2019; Givens and Morris, 2020; Sassaman et al., 2020)4 and how to help researchers and practitioners consider when not to build systems at all (Barocas et al., 2020).”
• Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (2022, background): “cisions that happen throughout, and beyond, the AI lifecycle–such as whether technology is even a solution to a given task or problem [3, 109].”
• Aligning AI Optimization to Community Well-Being (2020, background): “While ethical outcomes can sometimes be achieved through changes to optimization goals, there are also situations that could conceivably require avoiding features, products, or business models altogether (Barocas et al. 2020).”