# Algorithmic Injustices: Towards a Relational Ethics

arXiv: https://arxiv.org/abs/1912.07376 · HTML: https://mlsanity.com/paper/1912.07376/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers](https://mlsanity.com/paper/2108.02922/) (2021)
- [Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness](https://mlsanity.com/paper/2303.17555/) (2023)
- [A Human Rights-Based Approach to Responsible AI](https://mlsanity.com/paper/2210.02667/) (2022)
- [Black Feminist Musings on Algorithmic Oppression](https://mlsanity.com/paper/2101.09869/) (2021)
- [Metaethical Perspectives on 'Benchmarking' AI Ethics](https://mlsanity.com/paper/2204.05151/) (2022)
- [From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation](https://mlsanity.com/paper/2101.02647/) (2021)
- [Toxicity of the Commons: Curating Open-Source Pre-Training Data](https://mlsanity.com/paper/2410.22587/) (2024)
- [Sociotechnical Implications of Generative Artificial Intelligence for Information Access](https://mlsanity.com/paper/2405.11612/) (2024)
- [Measuring Trustworthiness or Automating Physiognomy? A Comment on Safra, Chevallier, Gr`ezes, and Baumard (2020)](https://mlsanity.com/paper/2202.08674/) (2022)
- ["I Hadn't Thought About That": Creators of Human-like AI Weigh in on Ethics And Neurodivergence](https://mlsanity.com/paper/2506.12098/) (2025)
- [Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoders](https://mlsanity.com/paper/2104.05928/) (2021)

## 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.