Sociotechnical Harms: Scoping a Taxonomy for Harm Reduction

By R. Shelby, Shalaleh Rismani, Kathryn Henne, AJung Moon, Negar Rostamzadeh, P. Nicholas, N. Yilla, Jess Gallegos, Andrew Smart, Emilio Garcia and 1 more. 2022

35 citations recorded by the source corpus.

Subfields: Applied Psychology, Safety Research, Safety, Robustness & Interpretability, 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 Safety Research

1. Umang Bhatt
2. Denny Zhou
3. Jason Wei
4. Quoc V. Le
5. Irene Solaiman
6. Yi Tay
7. Adrian Weller
8. Shixiang Shane Gu

In Safety, Robustness & Interpretability

1. Umang Bhatt
2. Shixiang Shane Gu
3. David Krueger
4. Quoc V. Le
5. Jason Wei
6. Irene Solaiman
7. Nitarshan Rajkumar
8. Xinyun Chen

In AI Ethics, Fairness & Society

1. Umang Bhatt
2. Irene Solaiman
3. Adrian Weller
4. Shalaleh Rismani

Citations in the corpus

• Scaling Instruction-Finetuned Language Models (2022, background): “, 2022), and that our evaluations focus on model outputs without considering the wider sociotechnical context in which instruction-finetuned language models exist (Shelby et al., 2022).”
• The Gradient of Generative AI Release: Methods and Considerations (2023, background): “Work to characterize sociotechnical harms can narrow the scope [85].”
• Harms from Increasingly Agentic Algorithmic Systems (2023, background): “…have identified the impact of computational modeling and development in social change [Abebe et al. 2020; Jacobs and Wallach 2021; Selbst et al. 2019] and scoped numerous taxonomies of risks, harms, and failures of algorithmic systems [Raji et al. 2022; Shelby et al. 2022; Weidinger et al. 2022].”