On the importance of ethnographic methods in AI research
By Vidushi Marda, Shiv Narayan. 2021
39 citations recorded by the source corpus.
Subfields: Safety Research, Evaluation & Benchmarks
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
- Abeba Birhane
- William S. Isaac
- Iason Gabriel
- Jonas Schuett
- Luciano Floridi
- Geoffrey Irving
- Jakob Mokander
- Myra Cheng
In Evaluation & Benchmarks
- Iason Gabriel
- Borja Balle
- Geoffrey Irving
- Lisa Anne Hendricks
- Po-Sen Huang
- Zachary Kenton
- Atoosa Kasirzadeh
- Laura Weidinger
Citations in the corpus
- Ethical and social risks of harm from Language Models (2021, background): “Similarly, ethnographic research is not standardly part of the LM evaluation toolkit, but is critical for surfacing and tracing risks from LTs in particular embedded settings, as exemplified in an ethnographic study of predictive policing tools in the New Delhi police force (Marda and Narayan, 2021)”
- Taxonomy of Risks posed by Language Models (2022, background): “The stereotypes at play in a given local context may also only be knowable through committed ethnographic work on the ground [123] or the lived experience of affected groups [177].”
- Auditing large language models: a three-layered approach (2023, background): “, creating more diverse developer teams [79], human-in-the-loop protocols [80] and qualitative evaluation tools based on ethnographic methods [81].”