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

ASGE BYRNE ethics and bias in AI

Pdf Summary

In this document, Dr. Michael Byrne discusses the ethical issues and potential biases associated with artificial intelligence (AI) in healthcare. He highlights the importance of responsible AI practices, which include respecting privacy, avoiding bias and discrimination, reducing errors, and maintaining human control.<br /><br />The document also mentions the impact of AI on society and emphasizes the need to consider safety, effectiveness, training data, algorithmic context, and unconscious biases. It refers to various studies and articles that discuss the challenges and potential biases in AI-driven healthcare systems.<br /><br />Dr. Byrne discusses how automation can magnify efficiency in a business but can also magnify inefficiencies if applied to inefficient operations. He references articles that highlight biases in face recognition software and algorithms used in court systems, as well as racial biases in algorithms used in managing the health of populations.<br /><br />The document also points out that biases and interpretations can arise from biased data representation, biased labels, biases in interpretation, confirmation bias, and overgeneralization. It emphasizes the importance of understanding data, avoiding single training sets, combining inputs from multiple sources, and seeking expert advice to address biases in AI systems.<br /><br />The document concludes with Dr. Byrne's contact information and acknowledges individuals and organizations who contributed to the presentation.

Keywords

ethical issues

biases

artificial intelligence

healthcare

responsibility

privacy

automation

efficiency

racial biases

data representation