Algorithms, Housing Discrimination, and the New Disparate Impact Rule - Foggo, Virginia; Villasenor, John (Science and Technology Law Review, 2021)
In the coming years, algorithms—often but not always powered by artificial intelligence—will experience increasing adoption in relation to home loan approvals, real estate marketing and sales, and zoning decisions. While algorithms offer many potential advantages, they also bring the risk of perpetuating or even amplifying longstanding patterns of housing-related discrimination. When that occurs, disparate impact litigation under the Fair Housing Act (FHA) will be a key mechanism for seeking redress. This Article aims to help ensure that FHA disparate impact claims can serve as an effective tool to combat housing discrimination in an era when an increasing number of decisions will be made by algorithms. This issue is particularly timely in light not only of the broader imperative to ensure that federal antidiscrimination frameworks remain effective as the technology used in the housing sector evolves, but also because the Department of Housing and Urban Development has recently published a final rule that, subject to a pending court challenge, will codify a set of explicit steps for litigants to follow in cases involving allegations of algorithm-based housing discrimination. Depending on its interpretation in the courts, the new rule risks erecting very high barriers to future FHA plaintiffs in light of the proprietary nature of the algorithms they will be challenging. To address this, the Article analyzes Supreme Court cases in relation to both FHA disparate impact litigation as well as pleading standards more generally and presents a roadmap which would allow plaintiffs to access the information necessary to address the pleading requirements of the proposed rule while simultaneously protecting the rights of defendants and avoiding overburdening courts.