Towards FATE in AI for Social Media and Healthcare: A Systematic Review

TL;DR

This systematic review examines how fairness, accountability, transparency, and ethics (FATE) are addressed in AI systems within social media and healthcare, highlighting current solutions and future research needs.

Contribution

It provides a comprehensive overview of FATE concepts in AI for social media and healthcare, identifying benefits, limitations, and future directions for research.

Findings

Statistical and intersectional fairness support healthcare fairness on social media

Transparency is crucial for AI accountability

Current solutions include simulation, data analytics, and automated systems

Abstract

As artificial intelligence (AI) systems become more prevalent, ensuring fairness in their design becomes increasingly important. This survey focuses on the subdomains of social media and healthcare, examining the concepts of fairness, accountability, transparency, and ethics (FATE) within the context of AI. We explore existing research on FATE in AI, highlighting the benefits and limitations of current solutions, and provide future research directions. We found that statistical and intersectional fairness can support fairness in healthcare on social media platforms, and transparency in AI is essential for accountability. While solutions like simulation, data analytics, and automated systems are widely used, their effectiveness can vary, and keeping up-to-date with the latest research is crucial.