A Qualitative Exploration of Perceptions of Algorithmic Fairness

By Allison Woodruff, Sarah E. Fox, Steven Rousso-Schindler, J. Warshaw. 2018

254 citations recorded by the source corpus.

Algorithmic systems increasingly shape information people are exposed to as well as influence decisions about employment, finances, and other opportunities. In some cases, algorithmic systems may be more or less favorable to certain groups or individuals, sparking substantial discussion of algorithmic fairness in public policy circles, academia, and the press. We broaden this discussion by exploring how members of potentially affected communities feel about algorithmic fairness. We conducted workshops and interviews with 44 participants from several populations traditionally marginalized by categories of race or class in the United States. While the concept of algorithmic fairness was largely unfamiliar, learning about algorithmic (un)fairness elicited negative feelings that connect to current national discussions about racial injustice and economic inequality. In addition to their concerns about potential harms to themselves and society, participants also indicated that algorithmic fairness (or lack thereof) could substantially affect their trust in a company or product.

Subfields: Safety Research, Sociology and Political Science

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Citations in the corpus

How Do Fairness Definitions Fare?: Examining Public Attitudes Towards Algorithmic Definitions of Fairness (2018, background): “Since the public is affected by these algorithmic systems, it is important to investigate public views of algorithmic fairness (Lee and Baykal 2017; Lee, Kim, and Lizarondo 2017; Lee 2018; Binns et al. 2018; Woodruff et al. 2018).”
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? (2018, background): “In contrast, HCI researchers have empirically studied users’ expectations and perceptions related to fairness in algorithmic systems and found that these do not always align with existing mathematical definitions [14, 66, 67, 96].”
How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India (2022, background): “Prior work has sought to ease the burden on technology providers towards fulfilling transparency obligations [89, 102], and studied their impacts on users affected by ADS [42, 113, 122].”
Algorithmic bias: review, synthesis, and future research directions (2021, methodology): “Others have focused on perceived fairness, representing the human judgements associated with biases in algorithms (M. Lee, 2018a; Woodruff et al., 2018).”
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training (2021, background): “Testing for fairness in online learning: When testing an online-learning software system for fairness (where models are continuously updated), such as identity-inference-based advertisement targeting systems [7, 70, 101], multiple identical runs are necessary, and statistical test results should be ”
How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis (2020, background): “When it is unclear how much or whether to restrict use of a database, authors should engage with community groups and advocates to determine what uses are appropriate (e.g., [62, 171]).”
WeBuildAI (2019, background): “Lee et al. and Woodruff et al. conducted interview and workshop studies on what people think “fair” algorithms are in the contexts of donation allocation [50] and online ads [84].”
Critical Race Theory for HCI (2020, background): “At a meta-level, Woodruff et al. examined how people from marginalized groups perceive algorithmic fairness and found that teaching about notions of bias in algorithms incited negative emotions and connections to contemporary concerns about racial justice [137].”
All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda (2021, methodology): “Additionally, metaverse designers should open channels to collect the voices of diversified community groups and collaboratively design solutions that lead to fairness in the metaverse environments [633].”
Human-Centered Approaches to Fair and Responsible AI (2020, background): “Even when algorithms are fair in terms of the statistical outcomes, they can be perceived as unfair if the decision-making process is unfair or the AI does not respect community values and norms [18, 28].”