AI in Workforce Decisions: Navigating Legal Risks

Published Date : 17/06/2025 

The use of artificial intelligence (AI) and algorithmic software in workforce decisions has surged, offering businesses increased productivity and efficiency. However, it also exposes them to significant legal risks under anti-discrimination laws. 

The use of algorithmic software and automated decision systems (ADS) to make workforce decisions, including the most sophisticated type, artificial intelligence (AI), has surged in recent years. HR technology’s promise of increased productivity and efficiency, data-driven insights, and cost reduction is undeniably appealing to businesses striving to streamline operations such as hiring, promotions, performance evaluations, compensation reviews, or employment terminations. However, as companies increasingly rely on AI, algorithms, and automated decision-making tools (ADTs) to make high-stakes workforce decisions, they may unknowingly expose themselves to serious legal risks, particularly under Title VII of the Civil Rights Act of 1964, the Age Discrimination in Employment Act (ADEA), the Americans with Disabilities Act (ADA), and numerous other federal, state, and local laws.

Using automated technology to make workforce decisions presents significant legal risks under existing anti-discrimination laws, such as Title VII, the ADEA, and the ADA, because bias in algorithms can lead to allegations of discrimination. Algorithmic HR software is uniquely risky because, unlike human judgment, it amplifies the scale of potential harm. A single biased algorithm can impact thousands of candidates or employees, exponentially increasing the liability risk compared to biased individual human decisions.

Proactive, privileged software audits are critical for mitigating legal risks and monitoring the effectiveness of AI in making workforce decisions.

In the employment context, algorithmic or automated HR tools refer to software systems that utilize predefined rules to run data through algorithms to assist with various human resources functions. These tools can range from simple rule-based formula systems to more advanced generative AI-powered technologies. Unlike traditional algorithms, which operate based on fixed, explicit instructions to process data and make decisions, generative AI systems differ in that they can learn from data, adapt over time, and make autonomous adjustments without being limited to predefined rules.

Employers use these tools in numerous ways to automate and enhance HR functions. A few examples:

- Applicant Tracking Systems (ATS) often use algorithms to score applicants compared to the position description or rank resumes by comparing the skills of the applicants to one another.

- Skills-based search engines rely on algorithms to match job seekers with open positions based on their qualifications, experience, and keywords in their resumes.

- AI-powered interview platforms assess candidate responses in video interviews, evaluating facial expressions, tone, and language to predict things like skills, fit, or likelihood of success.

- Automated performance evaluation systems can analyze employee data such as productivity metrics and feedback to provide ratings of individual performance.

- AI systems can listen in on phone calls to score employee and customer interactions, a feature often used in the customer service and sales industries.

- AI systems can analyze background check information as part of the hiring process.

- Automated technology can be incorporated into compensation processes to predict salaries, assess market fairness, or evaluate pay equity.

- Automated systems can be utilized by employers or candidates in the hiring process for scheduling, note-taking, or other logistics.

- AI models can analyze historical hiring and employee data to predict which candidates are most likely to succeed in a role or which new hires may be at risk of early turnover.

AI-driven workforce decisions are covered by a variety of employment laws, and employers are facing an increasing number of agency investigations and lawsuits related to their use of AI in employment. Some of the key legal frameworks include:

1. Title VII: Title VII prohibits discrimination on the basis of race, color, religion, sex, or national origin in employment practices. Under Title VII, employers can be held liable for facially neutral practices that have a disproportionate, adverse impact on members of a protected class. This includes decisions made by AI systems. Even if an AI system is designed to be neutral, if it has a discriminatory effect on a protected class, an employer can be held liable under the disparate impact theory.

2. The ADA: If AI systems screen out individuals with disabilities, they may violate the Americans with Disabilities Act (ADA). It is also critical that AI-based systems are accessible and that employers provide reasonable accommodations as appropriate to avoid discrimination against individuals with disabilities.

3. The ADEA: The Age Discrimination in Employment Act (ADEA) prohibits discrimination against applicants and employees ages forty or older.

4. The Equal Pay Act: AI tools that factor in compensation and salary data can be prone to replicating past pay disparities. Employers using AI must ensure that their systems are not creating or perpetuating sex-based pay inequities, or they risk violating the Equal Pay Act.

5. The EU AI Act: This comprehensive legislation is designed to ensure the safe and ethical use of artificial intelligence across the European Union. It treats employers’ use of AI in the workplace as potentially high-risk and imposes obligations for continued use, as well as potential penalties for violations.

6. State and Local Laws: There is no federal AI legislation yet, but a number of states and localities have passed or proposed AI legislation and regulations, covering topics like video interviews, facial recognition software, bias audits of automated employment decision-making tools (AEDTs), and robust notice and disclosure requirements.

7. Data Privacy Laws: AI also implicates a number of other types of laws, including international, state, and local laws governing data privacy, which creates another potential risk area for employers.

One of the most significant challenges with the use of AI in workforce decisions is the lack of transparency in how algorithms make decisions. Unlike human decision-makers who can explain their reasoning, generative AI systems operate as “black boxes,” making it difficult, if not impossible, for employers to understand—or defend—how decisions are reached. This opacity creates significant legal risks. Without a clear understanding of how an algorithm reaches its conclusions, it may be difficult to defend against discrimination claims. If a company cannot provide a clear rationale for why an AI system made a particular decision, it could face regulatory action or legal liability.

Algorithmic systems generally apply the same formula against all candidates, creating relative consistency in the comparisons. For generative AI systems, there is greater complexity because the judgments and standards change over time as the system absorbs more information. As a result, the decision-making applied to one candidate or employee will vary from the decisions made at a different point in time.

Mitigating the legal risks involves conducting AI audits, workforce analytics, and bias detection. These steps can help ensure that AI systems are fair, transparent, and compliant with existing laws. By taking a proactive approach, employers can harness the benefits of AI while minimizing the potential for legal liabilities. 

Frequently Asked Questions (FAQS): 

Q: What are the main legal risks associated with using AI in workforce decisions?

A: The main legal risks include potential violations of anti-discrimination laws such as Title VII, the ADA, and the ADEA. AI systems can inadvertently perpetuate biases, leading to disparate impact or treatment, and may also violate data privacy laws.

Q: How can employers mitigate these legal risks?

A: Employers can mitigate legal risks by conducting AI audits, implementing workforce analytics, and performing bias detection. These steps help ensure that AI systems are fair, transparent, and compliant with existing laws.

Q: What is the difference between traditional algorithms and generative AI systems?

A: Traditional algorithms operate based on fixed, explicit instructions to process data and make decisions. Generative AI systems, on the other hand, can learn from data, adapt over time, and make autonomous adjustments without being limited to predefined rules.

Q: How do state and local laws impact the use of AI in employment?

A: State and local laws can impose additional regulations and requirements on the use of AI in employment, such as bias audits, notice and disclosure requirements, and specific rules for video interviews and facial recognition software.

Q: What is the role of transparency in AI decision-making?

A: Transparency is crucial in AI decision-making because it allows employers to understand and defend how decisions are reached. Without transparency, it can be difficult to defend against discrimination claims and regulatory actions. 

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