# Can AI developers avoid bias in public health applications?

## TL;DR

This paper explores how AI developers can address bias in personalized public health treatments to ensure equitable outcomes for all subpopulations.

## Contribution

The paper introduces practical challenges faced by AI developers in mitigating bias and discusses implications for assigning responsibility.

## Key findings

- Bias mitigation strategies often overlook practical challenges faced by AI developers.
- Developers play a key role in ensuring equitable AI-driven public health treatments.
- Acknowledging these limitations is crucial for assigning responsibility in bias mitigation.

## Abstract

Developments in the field of engineering biology and artificial intelligence have made it increasingly possible to deliver personalised treatments which are tailored to the individual and can help prevent illnesses before they occur. While such advancements have important implications for public health, the use of AI-enabled personalised treatments comes with potential downsides, not least of which is the potential for bias which may cause harm to certain subpopulations. As one of the key actors in the AI development pipeline, developers are ideally placed to ensure that treatments are designed in an equitable manner. However, existing bias mitigation strategies often fail to consider the practical challenges faced by developers which can significantly impact their abilities to detect and remove bias from any treatments which they help to design. In this paper, we highlight some of the practical challenges that developers face in mitigating bias. We also consider the implications of acknowledging such limitations for attributing responsibility related to bias mitigation.