AI Neutrality in Title IX Investigations and Decision-Making within Higher Education

Algorithmic tools are being increasingly used to support administrative and quasi-judicial decision-making processes. As such, higher education institutions are beginning to explore algorithmic tools to organize case materials, conduct interviews, analyze interviews and evidence, and assist investigators in Title IX processes. Proponents of AI usage argue that these tools can improve efficiency, consistency, and reduce bias. However, its use raises questions about whether AI tools affect the neutrality of Title IX investigations and decisions. This theoretical paper examines the concept of neutrality in Title IX investigations as a theoretical construct. Using an interdisciplinary synthesis of procedural justice, institutional legitimacy, administrative decision-making, and AI ethics, this paper clarifies the conditions under which AI use supports neutrality. It also examines when AI risks undermining procedural fairness and institutional trust. Rather than treating neutrality as an outcome of investigative consistency, this analysis suggests that neutrality while using AI tools cannot currently be assessed solely by examining whether investigations and outcomes appear to be neutral. Instead, neutrality can only be measured when evaluated in relation to how human discretion, transparency, and accountability interact with algorithmic tools. While AI tools may support investigative and decision-making functions, over-relying on them without proper oversight risks undermining trust and institutional legitimacy. The paper concludes that neutrality in Title IX investigations is dependent on how AI tools are integrated and disclosed within the Title IX investigations.