# Equity Bias: An Ethical Framework for AI Design

arXiv: https://arxiv.org/abs/2604.21907 · HTML: https://mlsanity.com/paper/2604.21907/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Epistemic Injustice in Generative AI](https://mlsanity.com/paper/2408.11441/) (2024)
- [The Butterfly Effect in Artificial Intelligence Systems: Implications for AI Bias and Fairness](https://mlsanity.com/paper/2307.05842/) (2023)
- [Beyond "Fairness:" Structural (In)justice Lenses on AI for Education](https://mlsanity.com/paper/2105.08847/) (2021)
- [Lazy Data Practices Harm Fairness Research](https://mlsanity.com/paper/2404.17293/) (2024)
- [Are generative models fair? A study of racial bias in dermatological image generation](https://mlsanity.com/paper/2501.11752/) (2025)
- [Navigating Fairness Measures and Trade-Offs](https://mlsanity.com/paper/2307.08484/) (2023)
- [A survey of recent methods for addressing AI fairness and bias in biomedicine](https://mlsanity.com/paper/2402.08250/) (2024)

## Abstract (arXiv)

Equity Bias is a philosophical and practical framework for building smarter, more equitable AI systems. Grounded in hermeneutic philosophy and epistemic injustice theory, it treats bias not as an error to eliminate but as a reflection of whose knowledge is encoded into systems. While traditional approaches aim to reduce or remove bias, Equity Bias instead makes bias transparent and contestable. In doing so, it broadens whose perspectives shape AI and provides a lens for understanding AI systems as interpretive agents. The framework introduces a three-phase AI Life Cycle methodology: 'Equity Archaeology' (mapping knowledge and assumptions), 'Co-Creating Meaning' (participatory design), and 'Ongoing Accountability' (continuous evaluation). Equity Bias guides developers, researchers, and policymakers towards AI that is ethically accountable and capable of addressing complex real-world challenges.