Hiring Bias in Artificial Intelligence: The Impact on Small Businesses that Employ AI

Introduction

Nearly 90% of companies use Artificial Intelligence (AI) to optimize the recruitment process, shorten hiring timelines, and reduce manual labor.[1]  In an era of rapid technological progress, organizations are using AI for staffing and recruiting to review resumes, identify suitable candidates, and streamline conventional hiring procedures.[2] Although enterprises can harness this innovative recruiting tool to expedite operations across the board, numerous challenges associated with AI-driven hiring emerge, particularly for small businesses and their proprietors.[3]

A massive issue with this AI tool is the trend of hiring bias.[4] Hiring bias occurs when AI selectively chooses some candidates and eliminates others based on the data preferences built into its programming.[5]  Typically, AI tools use a database of employee data, and when candidates do not match the programmed criteria, the system excludes them from the job opportunity.[6]  This can manifest in different ways, such as eliminating resumes from candidates who attended certain schools or have gaps in their employment history, failing to advertise positions to candidates the AI does not favor, and other similar practices.[7]

With the recent advances in AI and new recruitment tools, many organizations are in favor of this approach.[8]  One commentator emphasized that AI tools for recruitment eliminate human bias, filter and rank top candidates, and follow ethical standards if properly monitored.[9] However, many organizations adopt AI hiring tools without evaluating how they function in practice, assuming fairness as something that is embedded in implementation.[10] This increases risk that biased outcomes go unnoticed over time.[11]

Federal Regulations

Since AI is an emerging technology, there are not many clear regulations and laws that outline how these tools should be used.[12] However, the Federal Trade Commission (FTC) offered federal guidance on the emerging presence of AI.[13] The Federal Trade Commission Act grants the FTC broad authority to combat unfair methods of competition and unfair or deceptive acts in commerce.[14] The statute declares “[u]nfair methods of competition in or affecting commerce, and unfair or deceptive acts or practices in or affecting commerce” to be unlawful.[15] This commerce and trade statute empowers the FTC to prevent unfair conduct through administrative proceedings, civil penalties, and injunctive relief.[16]

The statute is broadly framed, and thus AI hiring practices likely will fall within its scope.[17]

However, federal courts have not yet meaningfully tested the application of this law to AI hiring practices.[18] The FTC has posited that racially biased algorithms and discriminatory AI hiring tools constitute an unfair method of competition under the code.[19] This allows for regulatory coverage that may extend beyond traditional employment discrimination statutes.[20] So, while the laws, regulations, and technology are new and unfamiliar, there are still avenues for legal relief to remedy the harms they can cause.[21]

Legal Harms

As mentioned, the FTC’s federal regulations provide a resolution and remedy for bias in AI hiring.[22] A common theme across AI hiring tools is that screening and promotion algorithms can produce discriminatory outcomes due to embedded biases in their design and data.[23]

A.   Hiring Discrimination

AI tools used in hiring tend to learn from existing data, which often reflects biases caused by exclusion and inequality.[24] An article from MIT Sloan School of Management highlights how poor-quality data contributes to these issues.[25] The article points out that some AI systems unfairly downgrade resumes from graduates of HBCUs and women’s colleges, as these institutions have traditionally been underrepresented in white-collar roles.[26] Additionally, certain AI tools penalize applicants with employment gaps, which often disadvantages parents, single parents, and mothers who have taken time off for family obligations.[27] Using outdated data causes these employment algorithms to perpetuate old prejudices and stereotypes, resulting in significant hiring errors.[28]

B.    Poor Promotion Algorithms

Advances in recruitment technology are being used by both employers and candidates.[29] One commentor stated that AI training models select candidates who are predicted to impress interviewers or secure promotions.[30] However, that does not mean it is identifying the best person for the job; it can simply mean that AI is favoring other users who use AI, rather than a fair and accurate assessment of all individuals.[31] As mentioned, AI heavily relies on past data to make modern decisions.[32] When the data is lacking on which employees should see advertisements or job promotions, AI remains focused on “predicting visibility rather than performance.”[33] The effects of AI on both sides of the hiring process leave individuals overwhelmed, fooled, and distrustful.[34]

Suggestions for Small Business Owners to Combat Bias in AI Hiring

AI and its development are not going anywhere.[35] In a world rapidly adopting technological advances, individuals are expected to adapt and conform to their use.[36] Many large corporations in a variety of fields, such as Chipotle, Amazon, Goldman Sachs, Nvidia, and ZipRecruiter, use these AI tools to find employees.[37]

But when thinking of large corporations, the question arises: What about smaller businesses? How can small businesses leverage AI without the extensive outreach and numerous individuals required to control and guide it?[38]  Below are a list of tactics for small business owners to keep in mind to comply with FTC regulations.[39]

1.     Identify AI Issues – Before utilizing AI for hiring decisions, there needs to be a careful review of the data and the assumptions within these symptoms.[40] The AI is not the problem itself, but rather the humans who have created it.[41] Look for systems that are fair at finding rewarding talent and algorithms that are diverse and accepting, rather than ones that are more standard.[42]

2.     Implement Human Oversight – AI performs best when it incorporates a structure that allows humans to interject and make revisions.[43] By allowing humans to edit for unfair biases ingrained in outdated data, AI can be more inclusive in the applications it reviews and the promotions it sends out.[44]

3.     Get Legal Advice When Needed – To prevent the issue, practices should utilize agency recommendations to mitigate legal risks.[45] However, if an issue persists, additional lawyers and firms are available to offer solutions that comply with federal regulations.[46] Legal solutions include “revisiting vendor agreements, updating training programs, and engaging with legal counsel to evaluate new obligations.”[47]

[1] See Elmira van den Broek, Anastasia V. Sergeeva & Marleen Huysman, New Research on AI and Fairness in Hiring, Harv. Bus. Rev. (Dec. 12, 2025), https://hbr.org/2025/12/new-research-on-ai-and-fairness-in-hiring.

[2] Id.

[3] Id.

[4] Id.

[5] Id.

[6] Id.

[7] Id.

[8] See Keith Ferrazzi, The AI Recruitment Takeover: Redefining Hiring in the Digital Age, Forbes (March 27, 2025, at 17:33 ET), https://www.forbes.com/sites/keithferrazzi/2025/03/27/the-ai-recruitment-takeover-redefining-hiring-in-the-digital-age/.

[9] Id.

[10] Van den Broek, Sergeeva & Huysman, supra note 1.

[11] Id.

[12] AI in the Workplace (US), Westlaw, https://us.practicallaw.thomsonreuters.com/w-018-7465 (last visited Feb. 16, 2026).

[13] 15 U.S.C. § 45.

[14] Id.

[15] Id.

[16] AI in the Workplace (US), supra note 12.

[17] Id.

[18] Id.

[19] Press Release, FTC Declares Racially Biased Algorithms in Artificial Intelligence Unfair and Deceptive, Prohibited by Law, Lawyers’ Committee for Civil Rights Under Law (April 20, 2021), https://www.lawyerscommittee.org/ftc-declares-racially-biased-algorithms-in-artificial-intelligence-unfair-and-deceptive-prohibited-by-law/.

[20] Id.

[21] AI in the Workplace (US), supra note 12.

[22] Id.

[23] Id.

[24] Emilio J. Castilla, AI is Reinventing Hiring—With the Same Old Biases. Here’s How to Avoid that Trap, Mass. Inst. Tech.: Sloan Sch. of Mgmt. (Dec. 15, 2025), https://mitsloan.mit.edu/ideas-made-to-matter/ai-reinventing-hiring-same-old-biases-heres-how-to-avoid-trap.

[25] Id.

[26] Id.

[27] Id.

[28] Id.

[29] Tomas Chamorro-Premuzic, AI Has Made Hiring Worse—But It Can Still Help, Harv. Bus. Rev. (Jan. 26, 2026), https://hbr.org/2026/01/ai-has-made-hiring-worse-but-it-can-still-help.

[30] Id.

[31] Id.

[32] Id.

[33] Id.

[34] Id.

[35] Id.

[36] Id.

[37] Id.

[38] Van den Broek, Sergeeva & Huysman, supra note 1.

[39] Id.

[40] Id.

[41] Id.

[42] Id.

[43] Chamorro-Premuzic, supra note 29.

[44] Id.

[45] Bradford J. Kelley & Andrew B. Rogers, The Sound and Fury of Regulating AI in the Workplace, 63 Harv. J. on Legis. Online 1 (2025), https://journals.law.harvard.edu/jol/2025/12/06/the-sound-and-fury-of-regulating-ai-in-the-workplace/.

[46] Id.

[47] Id.