Workshop & Non-Archival Paper
"A Cold, Technical Decision-Maker": Can AI Provide Explainability, Negotiability, and Humanity?
Allison Woodruff, Yasmin Asare Anderson, Katherine Jameson Armstrong, Marina Gkiza, Jay Jennings, Christopher Moessner, Fernanda Viégas, Martin Wattenberg, Lynette Webb, Fabian Wrede, Patrick Gage Kelley
arXiv preprint arXiv:2012.00874, 2020
December 2020
Part ofAI Explainability →
Abstract
Algorithmic systems are increasingly deployed to make decisions in many areas of people's lives. The shift from human to algorithmic decision-making has been accompanied by concern about potentially opaque decisions that are not aligned with social values, as well as proposed remedies such as explainability. We present results of a qualitative study of algorithmic decision-making, comprised of five workshops conducted with a total of 60 participants in Finland, Germany, the United Kingdom, and the United States. We invited participants to reason about decision-making qualities such as explainability and accuracy in a variety of domains. Participants viewed AI as a decision-maker that follows rigid criteria and performs mechanical tasks well, but is largely incapable of subjective or morally complex judgments. We discuss participants' consideration of humanity in decision-making, and introduce the concept of 'negotiability', the ability to go beyond formal criteria and work flexibly around the system.
- accountability
- decision-making
- ai
- explainability
- interpretability
- transparency
Cite this paper
@misc{woodruff2020a,
title = {"A Cold, Technical Decision-Maker": Can AI Provide Explainability, Negotiability, and Humanity?},
author = {Woodruff, Allison and Anderson, Yasmin Asare and Armstrong, Katherine Jameson and Gkiza, Marina and Jennings, Jay and Moessner, Christopher and Viégas, Fernanda and Wattenberg, Martin and Webb, Lynette and Wrede, Fabian and Kelley, Patrick Gage},
year = {2020},
eprint = {2012.00874},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2012.00874},
}