Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature

Starke, Christopher; Baleis, Janine; Keller, Birte; Marcinkowski, Frank

doi:10.48550/arxiv.2103.12016

Cited by 9 publications

(15 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This demonstrates the need for more diversification in terms of the investigated domains and tasks related to algorithmic fairness [107].…”

Section: Fairness In Computer Sciencementioning

confidence: 89%

“…61,105]. Research of this kind tends to focus on particular application domains and scenarios, such as pretrial risk assessment and hiring decisions [107]. For instance, Grgic-Hlaca et al have explored how users perceive and reason about fairness in algorithmic decision making using scenario-based surveys [55].…”

Section: Fairness In Computer Sciencementioning

confidence: 99%

“…However, perceptions of fairness and discrimination are multi-dimensional and context-dependent. There is a need for more diversification in terms of investigated domains and tasks [107], and to take demographic differences into account [96].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intersectional Experiences of Unfair Treatment Caused by Automated Computational Systems

Nuenen

Such

Coté

2022

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

This paper reports on empirical work conducted to study perceptions of unfair treatment caused by automated computational systems. While the pervasiveness of algorithmic bias has been widely acknowledged, and perceptions of fairness are commonly studied in Human Computer Interaction, there is a lack of research on how unfair treatment by automated computational systems is experienced by users from disadvantaged and marginalised backgrounds. There is a need for more diversification in terms of the investigated users, domains, and tasks, and regarding the strategies that users employ to reduce harm. To unpack these issues, we ran a prescreened survey of 663 participants, oversampling those with at-risk characteristics. We collected occurrences and types of conflicts regarding unfair and discriminatory treatment and systems, as well as the actions taken towards resolving these situations. Drawing on intersectional research, we combine qualitative and quantitative approaches in order to highlight the nuances around power and privilege in the perceptions of automated computational systems. Among our participants, we discuss experiences of computational essentialism, attribute-based exclusion, and expected harm. We derive suggestions to address these perceptions of unfairness as they occur.

show abstract

“…This demonstrates the need for more diversification in terms of the investigated domains and tasks related to algorithmic fairness [107].…”

Section: Fairness In Computer Sciencementioning

confidence: 89%

Section: Fairness In Computer Sciencementioning

confidence: 99%

See 1 more Smart Citation

Intersectional Experiences of Unfair Treatment Caused by Automated Computational Systems

Nuenen

Such

Coté

2022

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

show abstract

“…Especially the revelation of sensitive features (e.g., gender or race) being used in the process appears to have significant effects [21,25,38]. We further know that there are several human-specific predictors of fairness perceptions [10,37], which we subsume under Personal Fairness Notion. This may include, e.g., individuals' stance towards affirmative action [22], but may also vary across demographics [20,31].…”

Section: Hypothesismentioning

confidence: 99%

On the Relationship Between Explanations, Fairness Perceptions, and Decisions

Schoeffer¹,

De‐Arteaga²,

Kuehl³

2022

Preprint

View full text Add to dashboard Cite

It is known that recommendations of AI-based systems can be incorrect or unfair. Hence, it is often proposed that a human be the final decision-maker. Prior work has argued that explanations are an essential pathway to help human decision-makers enhance decision quality and mitigate bias, i.e., facilitate human-AI complementarity. For these benefits to materialize, explanations should enable humans to appropriately rely on AI recommendations and override the algorithmic recommendation when necessary to increase distributive fairness of decisions. The literature, however, does not provide conclusive empirical evidence as to whether explanations enable such complementarity in practice. In this work, we (a) provide a conceptual framework to articulate the relationships between explanations, fairness perceptions, reliance, and distributive fairness, (b) apply it to understand (seemingly) contradictory research findings at the intersection of explanations and fairness, and (c) derive cohesive implications for the formulation of research questions and the design of experiments.

show abstract

“…Fairness is a core concern of data-driven decision support systems, next to security/privacy and explainability concerns (Choraś et al, 2020). Currently, fairness protection has gained importance in developing sociotechnical algorithmic systems (Starke et al, 2021). For sociotechnical systems see (Bargh & Troxler, 2020).…”

Section: Fairnessmentioning

confidence: 99%

Towards an Integrated Approach for Preserving Data Utility, Privacy and Fairness

Bargh¹,

Choenni²

2022

MyRES

View full text Add to dashboard Cite

Data reusability has become a distinct characteristic of scientific, commercial, and administrative practices nowadays. However, an unlimited and careless reuse of data may lead to privacy breaches and unfair impacts on individuals and vulnerable groups. Data content adaption is a key aspect of preserving data privacy and fairness. Often, such content adaption affects data utility adversely. Further, the interaction between privacy protection and fairness protection can be subject to making trade-offs because mitigating privacy risks may adversely affect detecting unfairness and vice versa. Therefore, there is a need for research on understanding the interactions between data utility, privacy and fairness. To this end, in this contribution, we use concepts from causal reasoning and argue for adopting an integrated view on data content adaption for data driven decision support systems. This asks for considering the operation context wholistically. By means of two cases, we illustrate that, in some situations, local data content adaption may lead to low data quality and utility. An integrated wholistic approach, however, may result in reuse of the original data (i.e., without content adaption, thus in higher data utilization) without adversely affecting privacy and fairness. We discuss some implications of this approach and sketch a few directions for future research.

show abstract

Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature

Cited by 9 publications

References 0 publications

Intersectional Experiences of Unfair Treatment Caused by Automated Computational Systems

Intersectional Experiences of Unfair Treatment Caused by Automated Computational Systems

On the Relationship Between Explanations, Fairness Perceptions, and Decisions

Towards an Integrated Approach for Preserving Data Utility, Privacy and Fairness

Contact Info

Product

Resources

About