Feeling

Product (mathematics)

Inequality

Communication

Neuroscience

Social psychology

Public relations

Gender studies

Computer science

Qualitative research

Psychology

Mathematics

Political science

Sociology

Mathematical analysis

Artificial intelligence

Geometry

Injustice

Race (biology)

Perception

Class (philosophy)

Affect (linguistics)

Social science

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.

A Qualitative Exploration of Perceptions of Algorithmic Fairness