# A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics

arXiv: https://arxiv.org/abs/1807.00553 · HTML: https://mlsanity.com/paper/1807.00553/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2018.

## Built on by (most cited first, 10 of 10)

- [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021)
- [How Different Groups Prioritize Ethical Values for Responsible AI](https://mlsanity.com/paper/2205.07722/) (2022)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Hard Choices in Artificial Intelligence](https://mlsanity.com/paper/2106.11022/) (2021)
- [System Safety and Artificial Intelligence](https://mlsanity.com/paper/2202.09292/) (2022)
- [A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems](https://mlsanity.com/paper/2305.06055/) (2023)
- [Towards the Formalization of a Trustworthy AI for Mining Interpretable Models explOiting Sophisticated Algorithms](https://mlsanity.com/paper/2510.20621/) (2025)
- [On Measuring the Diversity of Organizational Networks](https://mlsanity.com/paper/2105.06929/) (2021)
- [Fiduciary Responsibility: Facilitating Public Trust in Automated Decision Making](https://mlsanity.com/paper/2301.10001/) (2023)
- [Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders](https://mlsanity.com/paper/2304.06031/) (2023)

## Abstract (arXiv)

Machine learning (ML) is increasingly deployed in real world contexts, supplying actionable insights and forming the basis of automated decision-making systems. While issues resulting from biases pre-existing in training data have been at the center of the fairness debate, these systems are also affected by technical and emergent biases, which often arise as context-specific artifacts of implementation. This position paper interprets technical bias as an epistemological problem and emergent bias as a dynamical feedback phenomenon. In order to stimulate debate on how to change machine learning practice to effectively address these issues, we explore this broader view on bias, stress the need to reflect on epistemology, and point to value-sensitive design methodologies to revisit the design and implementation process of automated decision-making systems.