Nose to Glass: Looking In to Get Beyond

TL;DR

This paper advocates for increased ethnographic research in responsible AI to better understand implementation challenges and organizational norms, aiming to improve harm mitigation strategies.

Contribution

It highlights the importance of ethnographic methods in responsible AI research and discusses challenges and benefits of this approach.

Findings

Ethnographic work reveals organizational norms affecting AI implementation.

Implementation of responsible AI tools remains limited despite increased awareness.

Ethnography can uncover friction points in deploying responsible AI practices.

Abstract

Brought into the public discourse through investigative work by journalists and scholars, awareness of algorithmic harms is at an all-time high. An increasing amount of research has been conducted under the banner of enhancing responsible artificial intelligence (AI), with the goal of addressing, alleviating, and eventually mitigating the harms brought on by the roll out of algorithmic systems. Nonetheless, implementation of such tools remains low. Given this gap, this paper offers a modest proposal: that the field, particularly researchers concerned with responsible research and innovation, may stand to gain from supporting and prioritising more ethnographic work. This embedded work can flesh out implementation frictions and reveal organisational and institutional norms that existing work on responsible artificial intelligence AI has not yet been able to offer. In turn, this can contribute to more insights about the anticipation of risks and mitigation of harm. This paper reviews similar empirical work typically found elsewhere, commonly in science and technology studies and safety science research, and lays out challenges of this form of inquiry.