A Playbook for Preventing Bias
To scale up the help it can provide to organizations working in health care, in June the initiative released the Algorithmic Bias Playbook, an action-oriented guide synthesizing many of the insights the initiative has drawn both from research and from experience in the field. The free playbook offers a framework for identifying, correcting, and preventing algorithmic bias following four steps:
- Creating an inventory of all the algorithms being used by a given organization
- Screening the algorithms for bias
- Retraining or suspending the use of biased algorithms
- Establishing organizational structures to prevent future bias
The playbook guides users through every step, breaking each down into discrete actions and offering practical advice and examples drawn from the CAAI’s work with health-care organizations. Although its specific focus is health care, “the lessons we’ve learned are
very general,” the authors write. “We have applied them in follow-on work in financial technology, criminal justice, and a range of other fields.”
Bembeneck, one of the authors of the playbook along with Mullainathan, Obermeyer, ideas42’s Rebecca Nissan, Michael Stern of the health-care startup Forward, and Stephanie Eaneff of Woebot Health, says that the playbook can serve as a blueprint not only for organizations hoping to improve their use of algorithms, but also for future regulation of algorithms. “We think one of the best ways to address algorithmic bias is better regulation,” she says.
To further encourage the implementation of best practices for algorithmic management in health care, the CAAI will cosponsor a conference with Booth’s Healthcare Initiative focused on helping those working in health care to take concrete steps toward eliminating algorithmic bias. Taking place in Chicago and online in early spring, the conference will bring together policymakers, health-care providers, payers, providers of A.I. software, and technical experts from outside the health-care industry—groups that may not be in regular contact with each other but could nonetheless benefit from an opportunity for dialogue.
Matthew J. Notowidigdo, professor of economics and codirector of the Healthcare Initiative, says that he’s eager to connect health-care experts with those who have backgrounds in machine learning. Health care poses some unique challenges when it comes to algorithms, he says—for instance, privacy restrictions may limit how data can be shared, including with the designers of algorithms—but “I’m of the belief that there’s a lot that health care can learn from other settings” where algorithms are used.
The conference will feature user stories from organizations that have put the Algorithmic Bias Playbook to use. Panels will focus on topics such as data sharing and building teams for algorithmic management. The conference’s organizers also emphasize the importance of allowing attendees to network, particularly given their diverse professional backgrounds, so they have the opportunity to share knowledge and build connections that can help them take action within their organizations.
The playbook and conference, as well as the ongoing support the CAAI offers through the Algorithmic Bias Initiative, reflect a rising concern within a portion of the health-care industry about how algorithms are used. Bembeneck says that her experience with the Algorithmic Bias Initiative has shown her that many health-care organizations—and, consequently, many of the vendors that supply algorithmic products—are acutely aware of the importance of equitable A.I. “Not only because they don’t want to be on the wrong side of the law,” she says, “but there’s a keen desire from everyone we’ve talked to that they want to give better health care.”