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Algorithmic bias is everywhere. Our work with dozens of organizations—healthcare providers, insurers, technology companies, and regulators—has taught us that biased algorithms are deployed throughout the healthcare system, influencing clinical care, operational workflows, and policy. Beyond healthcare, we've seen algorithmic bias influence decisions in criminal justice, finance, and other fields. But our work has also shown us that there are solutions, and we are sharing resources to help leaders, practitioners, and policymakers address the problem and mitigate algorithmic bias wherever they find it.

The Playbook

Is your organization using biased algorithms? How would you know? What would you do if so? This playbook describes 4 steps your organization can take to answer these questions. It distills insights from our years of applied work helping others diagnose and mitigate bias in live algorithms.


Algorithmic Bias Playbook

Algorithmic Bias Playbook

This playbook will teach you how to define, measure, and mitigate racial bias in live algorithms. By working through concrete examples—cautionary tales—you’ll learn what bias looks like. You’ll also see reasons for optimism—success stories—that demonstrate how bias can be mitigated, transforming flawed algorithms into tools that fight injustice. 

  • C-suite leaders: This playbook will help you think strategically about how algorithms can go wrong, and help you lay out oversight structures to prevent bias.
  • Technical teams: The difference between biased and unbiased algorithms is often a matter of subtle technical choices. This playbook will help you make those choices better, whether you're a developer or consumer. 
  • Regulators: This playbook’s practical approach to bias can be used to craft prospective guidance for industry, or to guide retrospective civil investigations.

Download the Playbook

Algorithmic Bias Playbook

The Research

Recently, our researchers published an article in Science that showed significant racial bias in a widely deployed health-care algorithm that selects patients for a managed care plan. Were that bias to be eliminated, the number of African American patients selected would nearly double. Working with the developer of the algorithm, we found solutions to dramatically reduce the bias and improve the care of many patients.

In addition, a paper we published in Nature Medicine helps show how algorithms can help physicians more equitably treat diverse populations who experience chronic pain differently. By carefully selecting training data based on patient reporting, we showed that the algorithm was more accurate than the typical methodology clinicians use and was able to significantly reduce racial disparity at every level of pain measurement.

Now, we're working with organizations across the country to help their teams understand how bias arises in healthcare algorithms and what they can do to mitigate those problems with the goal of providing more equitable healthcare for everyone.

Independence Blue Cross
Harvard Pilgrim Health Care
SymphonyRM
Blue Cross Blue Shield - North Carolina
Office of IDEaS

Join Us

We are excited to be part of growing interest at all levels to counter healthcare inequity by finding ways to use data and AI to improve healthcare outcomes for all. If you would like to join us in this effort, please reach out. Thanks to our generous donors, we are happy to offer our expertise and support to organizations interested in identifying and mitigating bias in their systems.

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