Towards A Unified Utilitarian Ethics Framework for Healthcare Artificial Intelligence

TL;DR

This paper proposes a utilitarian ethics framework for healthcare AI, addressing key ethical principles like justice and privacy to improve trust and utility in clinical decision support systems.

Contribution

It introduces a novel utilitarian ethics-based theoretical framework for designing ethical AI in healthcare, based on thematic analysis of expert opinions and ethical principles.

Findings

Justice, privacy, bias, risks, and interpretability are key ethical principles.

Analysis of 36 AI experts' opinions identified top ethical concerns.

Proposed framework aims to guide ethical AI development in healthcare.

Abstract

Artificial Intelligence (AI) aims to elevate healthcare to a pinnacle by aiding clinical decision support. Overcoming the challenges related to the design of ethical AI will enable clinicians, physicians, healthcare professionals, and other stakeholders to use and trust AI in healthcare settings. This study attempts to identify the major ethical principles influencing the utility performance of AI at different technological levels such as data access, algorithms, and systems through a thematic analysis. We observed that justice, privacy, bias, lack of regulations, risks, and interpretability are the most important principles to consider for ethical AI. This data-driven study has analyzed secondary survey data from the Pew Research Center (2020) of 36 AI experts to categorize the top ethical principles of AI design. To resolve the ethical issues identified by the meta-analysis and domain experts, we propose a new utilitarian ethics-based theoretical framework for designing ethical AI for the healthcare domain.