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Equity should be fundamental to the emergence of innovation.

Jack GallifantLuis Filipe NakayamaJudy Wawira GichoyaRobin PierceLeo Anthony Celi
Published in: PLOS digital health (2023)
The ability of artificial intelligence to perpetuate bias at scale is increasingly recognized. Recently, proposals for implementing regulation that safeguards such discrimination have come under pressure due to the potential of such restrictions stifling innovation within the field. In this formal comment, we highlight the potential dangers of such views and explore key examples that define this relationship between health equity and innovation. We propose that health equity is a vital component of healthcare and should not be compromised to expedite the advancement of results for the few at the expense of vulnerable populations. A data-centered future that works for all will require funding bodies to incentivize equity-focused AI, and organizations must be held accountable for the differential impact of such algorithms post-deployment.
Keyphrases
  • artificial intelligence
  • healthcare
  • machine learning
  • big data
  • deep learning
  • global health
  • public health
  • human health
  • mental health
  • health information
  • risk assessment
  • health promotion
  • data analysis