When faculty discuss artificial intelligence, conversations often focus on the ethics of using AI in research, writing, instruction, and administration. But these discussions must increasingly explore the legal implications of AI use, especially with regard to federal discrimination laws. The US Department of Education has suggested that discriminatory use of AI is a potential area of concern for higher education. Likewise, the White House (under President Biden), Congress, and the Equal Employment Opportunity Commission have signaled an intent to combat so-called algorithmic discrimination in employment and other areas. According to the Biden White House’s “Blueprint for an AI Bill of Rights,” algorithmic discrimination arises “when automated systems contribute to unjustified different treatment or impacts disfavoring people based on their race, color, ethnicity, sex (including pregnancy, childbirth, and related medical conditions, gender identity, intersex status, and sexual orientation), religion, age, national origin, disability, veteran status, genetic information, or any other [protected] classification.”

Reciprocity over Hierarchy: Designing and Implementing a Student-Led Reverse Mentoring Program
Many academic leaders, including administrators and faculty members, participate in traditional mentoring programs that prioritize hierarchy. But these programs typically don’t foster engagement and connection between students and faculty or


