Public Perception of Algorithmic Fairness

Wednesday, January 17, 2018 - 4:00 pm4:30 pm

Allison Woodruff, User Experience Researcher, Google


We explore the topic of algorithmic fairness and how it relates to user trust. We conducted participatory design workshops and interviews with 44 participants from several populations that have traditionally been marginalized, specifically, Black, Hispanic, and low socioeconomic status participants in the United States. While the concept of algorithmic fairness was largely unfamiliar to these participants and they tended to underestimate the scale, complexity, and impact of algorithmic systems, participants also indicated that algorithmic fairness (or perceived lack thereof) could substantially affect their trust in a company or product. Our findings suggest that in addition to providing important benefits to society and individuals, improving algorithmic fairness can also enhance user trust. We outline positive steps companies and organizations can take to include algorithmic fairness as a value in product design and development.

Allison Woodruff, User Experience Researcher, Google

Allison Woodruff is a user experience researcher on Google’s Security & Privacy team. She received her PhD in Computer Science from UC Berkeley. Prior to working at Google, Allison worked at the Palo Alto Research Center (PARC) and Intel Labs Berkeley. Allison is a co-inventor on 20 issued patents and has published over 60 papers on topics such as privacy, mobile computing, domestic technology, sustainability, citizen science, and information visualization. She has conducted research in a wide range of settings, such as green homes, low-income neighborhoods, religious environments, museums, amusement parks, traditional work environments, and street sweeper maintenance yards.

@inproceedings {208127,
author = {Allison Woodruff},
title = {Public Perception of Algorithmic Fairness},
booktitle = {Enigma 2018 (Enigma 2018)},
year = {2018},
address = {Santa Clara, CA},
url = {},
publisher = {USENIX Association},
month = jan