Fairness and Transparency in Human-Robot Interaction

Claure, Houston; Chang, Ming Che; Kim, Se-Yun; Omeiza, Daniel; Brandão, Martim; Lee, Min Kyung; Jung, Malte

doi:10.1109/hri53351.2022.9889421

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…That said, performance may vary across domains and developing a dedicated solver for policy-dependent fairness constraints may be interesting future work. In addition, we are currently looking to apply SCALES in more complex domains such as human-robot interaction [14,6].…”

Section: Discussionmentioning

confidence: 99%

SCALES: From Fairness Principles to Constrained Decision-Making

Balakrishnan

Soh

2022

Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

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This paper proposes SCALES, a general framework that translates well-established fairness principles into a common representation based on the Constraint Markov Decision Process (CMDP). With the help of causal language, our framework can place constraints on both the procedure of decision making (procedural fairness) as well as the outcomes resulting from decisions (outcome fairness). Specifically, we show that well-known fairness principles can be encoded either as a utility component, a non-causal component, or a causal component in a SCALES-CMDP. We illustrate SCALES using a set of case studies involving a simulated healthcare scenario and the real-world COMPAS dataset. Experiments demonstrate that our framework produces fair policies that embody alternative fairness principles in single-step and sequential decision-making scenarios.

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Section: Discussionmentioning

confidence: 99%

SCALES: From Fairness Principles to Constrained Decision-Making

Balakrishnan

Soh

2022

Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

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Objective metrics for ethical AI: a systematic literature review

Palumbo,

Carneiro,

Alves

2024

Int J Data Sci Anal

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The field of AI Ethics has recently gained considerable attention, yet much of the existing academic research lacks practical and objective contributions for the development of ethical AI systems. This systematic literature review aims to identify and map objective metrics documented in literature between January 2018 and June 2023, specifically focusing on the ethical principles outlined in the Ethics Guidelines for Trustworthy AI. The review was based on 66 articles retrieved from the Scopus and World of Science databases. The articles were categorized based on their alignment with seven ethical principles: Human Agency and Oversight, Technical Robustness and Safety, Privacy and Data Governance, Transparency, Diversity, Non-Discrimination and Fairness, Societal and Environmental Well-being, and Accountability. Of the identified articles, only a minority presented objective metrics to assess AI ethics, with the majority being purely theoretical works. Moreover, existing metrics are primarily concentrating on Diversity, Non-Discrimination and Fairness, with a clear under-representation of the remaining principles. This lack of practical contributions makes it difficult for Data Scientists to devise systems that can be deemed Ethical, or to monitor the alignment of existing systems with current guidelines and legislation. With this work, we lay out the current panorama concerning objective metrics to quantify AI Ethics in Data Science and highlight the areas in which future developments are needed to align Data Science projects with the human values widely posited in the literature.

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Fairness and Transparency in Human-Robot Interaction

Cited by 2 publications

References 31 publications

SCALES: From Fairness Principles to Constrained Decision-Making

SCALES: From Fairness Principles to Constrained Decision-Making

Objective metrics for ethical AI: a systematic literature review

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