Algorithmic Mediation in Group Decisions: Fairness Perceptions of Algorithmically Mediated vs. Discussion-Based Social Division

By Min Kyung Lee. 2017

200 citations recorded by the source corpus.

Subfields: Computer Science Applications, Safety Research, Human-Computer Interaction

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Users’ perceptions and assessments of performance may also signiﬁcantly diverge from that suggested by statistical metrics (Lee and Baykal, 2017; Olteanu et al., 2017a).”
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 et al., 2017; Lee, 2018; Binns et al., 2018; Woodruff et al., 2018).”
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? (2018, background): “For example, HCI researchers have empirically studied users’ expectations and perceptions related to fairness in algorithmic systems, finding that these do not always align with existing statistical definitions [16, 71, 72, 108].”
Human Trust in Artificial Intelligence: Review of Empirical Research (2020, methodology): “Following the reviewed empirical studies and based on the theoretical developments discussed (Lee et al., 2006a), we propose an AI embodiment framework that can help guide the integration of multidisciplinary knowledge on human – AI relations and facilitate future research.”
Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management (2018, background): “Lee and Baykal (2017) examined people’s actual experiences with algorithmic versus human decisions.”
A Qualitative Exploration of Perceptions of Algorithmic Fairness (2018, background): “…of given scenarios), individual factors (which appears to resonate with variation reported in [69,86]), different stakeholder perspectives (as discussed for example in [51,52]), and different framings of fairness (for example, an emphasis on fair division as in [51,52] versus social justice).”
In AI we trust? Perceptions about automated decision-making by artificial intelligence (2020, unknown): “However, when it comes to fairness in algorithmic decisions involving groups, the higher the level of computer programming knowledge, the less that the algorithmic-mediated decision was perceived to be fair (Lee and Baykal 2017).”
Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders (2019, background): “We adapted these questions from [33].”
Co-Designing a Real-Time Classroom Orchestration Tool to Support Teacher-AI Complementarity (2019, background): “Given this enhanced awareness, teachers reported frustration over their relative lack of control (cf. Lee & Baykal, 2017).”
Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development (2020, methodology): “Another application of algorithmic evaluation and widely used by companies is video and telephone analyses (Lee and Baykal 2017).”
This Thing Called Fairness (2019, background): “Lee and Baykal observe that “optimally fair” allocations determined mathematically based on expressions of utility may not be preferable to negotiated allocations for various reasons [94].”