Gartner Research

Algorithms Are Biased — Here's How to Overcome This Inherent Data Problem

Published: 24 February 2016

ID: G00296971

Analyst(s): Frank Buytendijk , Helen Poitevin , Alan D. Duncan , Alexander Linden


The notion that data and analytics are unbiased is a fallacy. In order to mitigate the potential effects of bias, data and analytics leaders must make deliberate design and implementation choices. They should start by adopting these best practices.

Table Of Contents
  • Key Challenges



  • Use an Ethics-Based Approach to Overcome Design Biases in Your Algorithms
    • Case Study: Recruiting Algorithm
  • Test Your Algorithms to Address Undesirable Consequences of Bias
    • Case Study: Autonomous Vehicles in Mining
  • Apply Analytics Governance to Establish Accountability and Mitigate Amplification Risks
    • Case Study: "The London Whale," 2012

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