AI Should Replace Human Admissions Officers to Restore Fairness and Trust
Source: "HUGH HEWITT: The fairest admissions officer may not be human at all | Fox News." September 22, 2026. www.foxnews.com
The Gist
Hugh Hewitt argues that AI should take over college admissions decisions because it can process applications faster, more objectively, and more transparently than human admissions staff, who introduce bias and political controversy into the process. He believes AI can be programmed to legally exclude factors like race while still weighing legitimate merit criteria, which would restore public trust in a system many see as broken.
Conclusion
Colleges and universities should replace human admissions staff with AI-driven admissions processes, as this would produce fairer, more transparent, and more trustworthy results than the current system.
Premises
- The admissions process is fundamentally data-driven (test scores, GPAs, essays, resumes, recommendations), which is exactly the kind of task AI excels at processing quickly and at scale.
- AI can assess essays for originality and detect outside assistance, and can sort recommendations for truthfulness and quality more efficiently than human staff.
- AI can be explicitly programmed to weight legitimate merit factors (income, geographic diversity, life circumstances, athletic/artistic talent, first-generation status) while being instructed not to weigh prohibited factors like race, ethnicity, or religion, ensuring legal compliance.
- Human admissions officers introduce bias and politicization into the process, particularly around controversial factors like race, undermining trust in the system.
- An AI-driven process would be transparent to outside evaluators (donors, courts, accreditors), allowing schools to demonstrate their admissions criteria openly and defend against legal challenges.
- The current admissions process suffers from a trust deficit due to suspected politicization, and AI could restore objectivity and public confidence.
- AI can better model long-term outcomes (student success, reputation effects, financial contributions) than young admissions officers currently can.
Assumptions
- AI models can be reliably trained and audited to exclude prohibited factors (race, ethnicity, religion) in practice, not just in theory.
- Transparency of an AI model's stated weights equates to actual fairness in outcomes.
- Human bias in admissions is a greater problem than potential algorithmic bias embedded in training data or design choices.
- AI systems will not develop proxy discrimination (e.g., using zip codes or school names as proxies for race).
- The values and priorities encoded into the AI (e.g., what counts as 'merit' or 'diversity') are themselves neutral or uncontroversial.
- Job displacement of ~40,000 admissions employees is an acceptable trade-off for the stated benefits.
- Outside evaluators and courts would trust AI-generated transparency reports as genuine accountability rather than a new form of obfuscation.