Gartner Research

Seek Diversity of People, Data and Algorithms to Keep AI Honest

Published: 27 April 2017

ID: G00320704

Analyst(s): Svetlana Sicular


Data and analytics leaders should seek diversity in artificial intelligence teams, data and algorithms to counteract bias-rooted errors in AI. Diversity in people, data sources and problem selection will be key aspects of your AI success.

Table Of Contents
  • Impacts


  • Diversity Counteracts Bias

Impacts and Recommendations

  • AI teams tend to develop conforming, self-perpetuating approaches, hindering the team's ability to innovate and spot incorrect outputs
  • Data sources for AI teams often contain incomplete or unintentionally biased information — this is the main cause of erroneous AI outcomes
  • No single algorithm or a rigid set of algorithms can meet the complexity of AI projects

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