Algorithmic Injustices: Towards a Relational Ethics
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Abstract (arXiv)
It has become trivial to point out how decision-making processes in various social, political and economical sphere are assisted by automated systems. Improved efficiency, the hallmark of these systems, drives the mass scale integration of automated systems into daily life. However, as a robust body of research in the area of algorithmic injustice shows, algorithmic tools embed and perpetuate societal and historical biases and injustice. In particular, a persistent recurring trend within the literature indicates that society's most vulnerable are disproportionally impacted. When algorithmic injustice and bias is brought to the fore, most of the solutions on offer 1) revolve around technical solutions and 2) do not focus centre disproportionally impacted groups. This paper zooms out and draws the bigger picture. It 1) argues that concerns surrounding algorithmic decision making and algorithmic injustice require fundamental rethinking above and beyond technical solutions, and 2) outlines a way forward in a manner that centres vulnerable groups through the lens of relational ethics.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Dec 2019.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 1; 2024: 2; 2025: 1.
11 papers.
- 2021Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Paperscited by 49
- 2023Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairnesscited by 26
- 2022A Human Rights-Based Approach to Responsible AIcited by 20
- 2021From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigationcited by 11
- 2021Black Feminist Musings on Algorithmic Oppressioncited by 11
- 2022Metaethical Perspectives on 'Benchmarking' AI Ethicscited by 11
- 2024Toxicity of the Commons: Curating Open-Source Pre-Training Datacited by 7
- 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Accesscited by 6
- 2022Measuring Trustworthiness or Automating Physiognomy? A Comment on Safra, Chevallier, Gr`ezes, and Baumard (2020)cited by 1
- 2025"I Hadn't Thought About That": Creators of Human-like AI Weigh in on Ethics And Neurodivergencecited by 1
- 2021Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoderscited by 0
- 2025"I Hadn't Thought About That": Creators of Human-like AI Weigh in on Ethics And Neurodivergence12 Jun 2025
- 2024Toxicity of the Commons: Curating Open-Source Pre-Training Data29 Oct 2024
- 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Access19 May 2024
- 2023Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness16 Mar 2023
- 2022A Human Rights-Based Approach to Responsible AI6 Oct 2022
- 2022Metaethical Perspectives on 'Benchmarking' AI Ethics11 Apr 2022
- 2022Measuring Trustworthiness or Automating Physiognomy? A Comment on Safra, Chevallier, Gr`ezes, and Baumard (2020)17 Feb 2022
- 2021Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers6 Aug 2021
- 2021Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoders13 Apr 2021
- 2021Black Feminist Musings on Algorithmic Oppression25 Jan 2021
- 2021From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation7 Jan 2021