ASGE Masterclass: Artificial Intelligence (On-Demand) | June 2021-ASGE BYRNE ethics and bias in AI

ASGE BYRNE ethics and bias in AI

Pdf Summary

This document discusses the ethical issues surrounding artificial intelligence (AI) in healthcare. The author highlights various concerns, including privacy and data management, quality of care and patient safety, responsibility and accountability, and potential inequalities in patient care. The author emphasizes the importance of responsible AI, governed by ethical guidelines and practices. This includes ensuring automation is applied to efficient operations, respecting privacy, avoiding bias and discrimination in AI systems, reducing risks, maintaining algorithmic accountability and transparency, and maintaining meaningful human control.<br /><br />The document also provides examples of bias and discrimination in AI systems. It discusses how face recognition software and algorithms used in courts have amplified existing biases and resulted in unfair outcomes. Additionally, the document highlights a study that found racial bias in an algorithm used to manage population health, which led to disparities in healthcare outcomes. The author points out that biases in data, annotations, and interpretation can perpetuate and amplify biases in AI systems.<br /><br />The document emphasizes the importance of understanding and addressing biases in AI systems. It suggests strategies such as understanding data, using multiple sources, testing with diverse datasets, and seeking input from experts to mitigate biases. The author concludes by emphasizing the significance of data in AI and thanking contributors and acknowledgments.<br /><br />Overall, this document discusses the ethical concerns and biases associated with AI in healthcare and emphasizes the need for responsible AI practices.

Keywords

artificial intelligence

healthcare

ethical issues

privacy

data management

quality of care

patient safety

responsibility

bias