We should change the law to allow for a third method for plaintiffs to bring suit under the “discrimination per se” doctrine. As I describe in a paper, this new doctrine would allow for the burden of proof to be shifted to the employer. So when a plaintiff using a hiring platform encounters a problematic design feature — like platforms that check for gaps in employment — she should be able to bring a lawsuit on the basis of discrimination per se, and the employer would then be required to provide statistical proof from internal and external audits to show that its hiring platform is not unlawfully discriminating against certain groups. In another paper, I argue that we need a federal law that would mandate data retention for all applications (including applications that were not completed) on hiring platforms and that would require employers to conduct internal and external audits so that no groups of applicants are disproportionately excluded. The audits would also ensure that the criteria being used is actually related to job tasks. This idea has precedence in federal law: Occupational Safety and Health Administration audits are recommended to ensure safe working conditions for employees. Employers that subject their automated hiring platforms to external audits should also receive a certification mark, that would favorably distinguish those employers in the labor market. This type of auditing and certification system recognizes that job applicants should be able to make informed choices about which hiring platforms they will trust with their information. Unions can help to ensure that automated hiring platforms are fair. Through collective bargaining, unions can work with employers to determine what criteria are actually relevant for determining job fit. Unions can also make sure that applicant data retained by automated hiring platforms is protected, and that it is not sold or transferred to workers’ detriment. To be sure, human decision-making is clouded by bias. But so is automated decision-making, especially given that human biases can be introduced at any stage of the process, from the design of the hiring algorithm to how results are interpreted. We cannot rely on automated hiring platforms without adequate safeguards to prevent unlawful employment discrimination. We need new laws and mandates to achieve that goal. Ifeoma Ajunwa (@iajunwa), an assistant professor of employment and labor law at the ILR School at Cornell, is the author of the forthcoming “The Quantified Worker.” The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips. And here’s our email: letters@nytimes.com. Follow The New York Times Opinion section on Facebook, Twitter (@NYTopinion) and Instagram.
Using AI - powered hiring systems is misguided , and new laws are needed to protect applicants from bias , according to employment and labour discrimination specialist Ifeoma ##wa , who says it could worsen employment discrimination . Ajunwa said automated hiring could create a closed - loop system , in which algorithm - created ads attract certain applicants , those that are hired are evaluated and those evaluations are used to create future ads . The system has neither transparency nor accountability , and can eliminate age , race , gender or mental health discrimination . legislation to allow action against the bias of automated hiring system would be a first step in tackling the problem .