Role of fairness, accountability, and transparency in algorithmic affordance

By D. Shin, Yong Jin Park. Computers in Human Behavior, 2019

491 citations recorded by the source corpus.

Abstract As algorithm-based services increase, social topics such as fairness, transparency, and accountability (FAT) must be addressed. This study conceptualizes such issues and examines how they influence the use and adoption of algorithm services. In particular, we investigate how trust is related to such issues and how trust influences the user experience of algorithm services. A multi-mixed method was used by integrating interpretive methods and surveys. The overall results show the heuristic role of fairness, accountability, and transparency, regarding their fundamental links to trust. Despite the importance of algorithms, no single testable definition has been observed. We reconstructed the understandings of algorithm and its affordance with user perception, invariant properties, and contextuality. The study concludes by arguing that algorithmic affordance offers a distinctive perspective on the conceptualization of algorithmic process. Individuals’ perceptions of FAT and how they actually perceive them are important topics for further study.

Subfields: Information Systems, Safety Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/bffed6c8a6cb1e24578c2cdc032b625a28b5d50e

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Citations in the corpus

User Perceptions of Algorithmic Decisions in the Personalized AI System:Perceptual Evaluation of Fairness, Accountability, Transparency, and Explainability (2020, methodology): “This finding is in line with the arguments of previous studies, which have shown the contextual nature of such variables (Shin & Park, 2019).”
Trust in AI and Its Role in the Acceptance of AI Technologies (2022, methodology): “As past research has already confirmed, trust in AI can be built through embedding specific principles in the design and governance of AI, as suggested in recent publications using the fairness, accountability, transparency, explainability framework (Shin, 2020b; Shin & Park, 2019).”
Trustworthy Artificial Intelligence: A Review (2022, background): “It ensures that the different stakeholders using or impacted by the system clearly understand its performance and limitations [181].”