This section will have annotations for Academic articles.
| “AI hiring tools can yield racial bias and systemic rejection” | |||
|---|---|---|---|
| What it covers | Claims that AI is helping companies save more money explaining why AI is being utilized more and more in the recruitment process and its benefits.through the stanford studies they were able to find that many employers were using the same AI hiring vendor. They found racial disparities mostly affecting Black and Asian applicants and pinpoints algorithmic monocultures. This dependence on one vendor can systematically reject the same applicants across multiple employers. | ||
| Why it's valuable | This source is valuable because it has evidence of the discrimination in the algorithm seen from multiple companies from utilizing just one AI system. The only limitation is that it only focuses on one of those AI vendors and only on a specific time period. | ||
| Who would benefit | The people that would benefit from this are employers, HR professionals, policy makers and students studying this topic. | ||
| limitations | The article’s flaw is that it only really focuses on one sort of AI vendor and only a specific period of time. | ||
| Citation | Bommasani, R., Bana, S. H., Creel, K. A., Jurafsky, D., & Liang, P. (2026, May 26). AI hiring tools can yield racial bias and systemic rejection. Stanford Institute for Human-Centered Artificial Intelligence. https://hai.stanford.edu/news/ai-hiring-tools-can-yield-racial-bias-and-systemic-rejection | ||
| “Navigating the AI Employment Bias Maze” | |||
|---|---|---|---|
| What it covers | This source explains the legal risks of utilizing AI based hiring systems which includes discrimination of different groups, it brings relevant federal laws, EEOC guidance, state and local regulations and strategies companies can use to reduce bias. | ||
| Why it's valuable | Its value comes from explaining the policies to stop bias in AI hiring as well as legal consequences for having bias. | ||
| Who would benefit | This piece benefits employers, attorneys, recruiters and HR. | ||
| limitations | The limitation here would be that regulations and policies change pretty often so some of the information and laws could become outdated and so it should be checked frequently against current laws. | ||
| Citation | Kempe, L. (2024, April). Navigating the AI employment bias maze: Legal compliance guidelines and strategies. American Bar Association. https://www.americanbar.org/groups/business_law/resources/business-law-today/2024-april/navigating-ai-employment-bias-maze/ | ||
| “Gender, Race and Intersectional Bias in AI Resume Screening via Language Model Retrieval” | |||
|---|---|---|---|
| What it covers | The article examines whether language models used for resume screening produce gender, racial and intersectional disparities. The article studied more than 550 job descriptions and resumes with names associated with different racial and gender identities and found that there was significant discrimination particularly affecting black men. | ||
| Why it's valuable | The source is valuable because it shows how bias can happen through just regular resume screening processes and emphasizes people's identities. Such as race, gender and class which end up affecting many of these groups. | ||
| Who would benefit | This article is useful for researchers, policymakers and students studying the topic. | ||
| limitations | A limitation is that the study uses LLM- based research. This research filters text utilizing large language models to evaluate data through a specific criteria. The article does not look into hiring systems managed by a specific group or company. | ||
| Citation | Wilson, K., & Caliskan, A. (2025, April 25). Gender, race, and intersectional bias in AI resume screening via language model retrieval. Brookings Institution. https://www.brookings.edu/articles/gender-race-and-intersectional-bias-in-ai-resume-screening-via-language-model-retrieval/ | ||
| “People Mirror AI systems’ Hiring Biases, Study Finds” | |||
|---|---|---|---|
| What it covers | This article focuses on the University of Washington research which reported how human hiring is being affected by biased AI recommendations. They had an experiment involving 528 participants and 16 different occupations and from that experiment researchers were able to uncover that people tended to mirror racial preferences expressed by AI systems. | ||
| Why it's valuable | This source is valuable because it expands the discussion between humans and automated discussions. It goes beyond algorithmic bias itself and shows how data is connected to human bias. | ||
| Who would benefit | The article is useful for HR professionals, students researching the topic, organizational researchers and companies working with AI. | ||
| limitations | The limitations of the article is that the participants studied only interacted with simulated AI systems and AI generated resumes rather than an actual hiring platform. | ||
| Citation | Milne, S. (2025, November 10). People mirror AI systems’ hiring biases, study finds. UW News. https://www.washington.edu/news/2025/11/10/people-mirror-ai-systems-hiring-biases-study-finds/ | ||
| “AI & HR: Algorithmic Discrimination in the Workplace” | |||
|---|---|---|---|
| What it covers | This article goes over how AI is transforming HR and Hiring but its also creating potential problems with algorithmic discrimination. It gives examples from Amazon, Mobley v. Workday, Saas V. Major and Lindsey & Africa to show how often AI is being used in regular everyday life. AI systems can evaluate but also can be biased with the data it's been trained with. | ||
| Why it's valuable | This article is important because it connects concerns about how biased training data is to employment discrimination law and regulations. | ||
| Who would benefit | The people that benefit from this article are policy makers, researchers and HR. | ||
| limitations | Its limitation would be that the article is that it focuses on legal policies instead and there's more policies since AI is growing and advancing. | ||
| Citation | Mesriani, K. (2024, November 21). AI & HR: Algorithmic discrimination in the workplace. Cornell Journal of Law and Public Policy. https://publications.lawschool.cornell.edu/jlpp/2024/11/21/ai-hr-algorithmic-discrimination-in-the-workplace/ | ||