Navigating AI in HR Management (by Tyler Serrano)

Artificial intelligence has made massive improvements over the years. Recently, AI has reshaped the landscape of human resources. This is especially true in large hotel companies and hospitality brands where hundreds, if not thousands, of applications may come in for a single role. Tools such as automated resume screening, predictive analytics, and structured digital interviews can reduce the administrative burden on HR teams. However, as these systems become more prevalent in recruitment, hospitality leaders must confront the unintended discrimination emerging from the data and design of AI tools.

The basic structure of AI is rooted in efficiency, but the very data that powers these systems can also be embedded with human bias. Decisions made by AI can unintentionally exclude qualified applicants, overlook diverse talent pools, and create new barriers, particularly for neurodivergent candidates navigating video-based assessments. Understanding these risks is essential for hospitality companies that pride themselves on service culture and global workforce diversity.

Hidden Bias in Resume-Screening

AI tools used in resume screening rely heavily on datasets derived from past hiring decisions. This becomes a problem, especially when the historical data reflects exclusion issues. An example of this occurred at Amazon, where internal testing of an AI recruiting tool revealed that the model was downgrading resumes containing terms associated with women’s colleges or women’s affinity groups. Since the system was trained on resumes submitted to the company over a ten-year period, a time when male applicants were the majority, the algorithm learned to favor those who resembled these candidates. This situation highlights a critical principle for hospitality companies embracing AI: algorithms will try to replicate the past unless HR intervenes.

In hospitality, there are different expectations than traditional jobs, especially with the large presence of the expected customer-facing appearance. Additionally, specific communication styles or uninterrupted career paths have historically influenced hiring decisions. An AI trained on legacy data can unintentionally penalize those who meet the following criteria:

• Those who are changing careers and transitioning into the hospitality environment.
• Neurodivergent individuals whose resumes may reflect different patterns of work or skill acquisition
• Women or minority candidates who are underrepresented in previous hiring cohorts

The danger here does not rely on deliberate discrimination, but on the reinforcement of outdated norms. As hospitality brands continue pursuing diverse and inclusive candidates, ensuring that AI models are regularly audited and trained on representative datasets becomes a business necessity.

AI Interviews and Neurodivergent Applicants

Going beyond resume screening, many businesses are beginning to use AI-powered video interviews, in which applicants record their responses to questions. At the same time, the system evaluates them using facial recognition, tone analysis, and behavioral scoring. While this can create consistency and scalability, it also introduces the risk that AI may misinterpret the communication styles of neurodivergent candidates.

For example, HireVue, which is one of the largest providers of AI interview technology, faced public scrutiny when its system was reported to analyze micro-expressions, eye contact, vocal pitch, and body language as indicators of candidate quality. Although the company has since reduced reliance on facial analysis, the early controversy highlighted a fundamental issue with neurodivergent applicants. The applicants may not present communication cues that the algorithm interprets as “strong,” even if they are exceptionally capable.

Common neurodivergent behaviors that AI may misinterpret include:

• Avoiding direct eye contact with the camera
• Monotone speech or atypical vocal patterns
• Delayed response times
• Limited facial expressions
• Nervous or repetitive movements under pressure

In a more personal interview, managers can address these issues by rephrasing questions, focusing on capability rather than body language, and building rapport. AI systems, however, can inadvertently label these behaviors as indicators of poor communication, low confidence, or a lack of customer service aptitude. As a result, AI may filter out qualified neurodivergent candidates before they ever reach a human recruiter.

Creating Responsible AI for Use in Hospitality HR

To balance the benefits of AI with the need for unequivocal fairness, hospitality organizations must adopt a cautious but deliberate approach. To be able to adopt these ideals, HR could include the following:

1. 1. Regular Audits and Data Review --- AI tools should be tested against diverse applicant datasets, then adjusted to eliminate patterns of disparate impact. Vendors should also be required to disclose information about how their algorithms weigh these variables.
2. Human Oversight at Critical Stages --- AI systems should assist HR judgment but not entirely replace it. Final hiring decisions and candidate evaluations should always involve trained professionals who can recognize potential issues.
3. Alternative Assessments for Neurodivergent Candidates
4. Applicants should be able to request accommodations, such as:

• Human-led interviews 
• Extended time for video responses 
• Interviews without facial-recognition scoring

Conclusion

AI can play a meaningful role in HR within the hospitality sector, but a major challenge is embracing it without caution. This can unintentionally reproduce historical biases and disadvantage neurodivergent applicants who do not conform to AI expectations. For an industry built on diversity and exceptional service, responsible AI adoption requires a commitment to fairness, transparency, and human oversight. Used in the described ways, AI can enhance HR operations. When AI is used blindly, it risks undermining the values that define the hospitality profession.

Questions for Thoughts

1. AI is too useful not to use, but there is a chance the AI “bubble” will pop at some point (as the .com bubble did in the 2000s). What will these companies do after laying off/not hiring for HR positions if this happens?
2. In what ways can AI be trained to balance diversity and inclusion with traditional hiring practices?

About the Author

Tyler Serrano is a transfer student from Victor Valley Community College and is a senior graduating in Spring of 2026. Tyler has 5 years of professional experience in restaurants, including 3 years managing a casual sushi restaurant. Additionally, Tyler has 2 years of experience volunteering as a chef at a local church in Claremont. Tyler has a deep appreciation for the craft of food service and deeply enjoys making people happy.

Note: The picture was downloaded from CNBC. Tyler Serrano submitted this essay as an assignment to Dr. Linchi Kwok's HRT 3500 Management of Human Resources in Hospitality at Cal Poly Pomona.