What is Algorithmic Discrimination in Hiring and How Can Employers Prevent It?

Algorithmic discrimination occurs when an AI system disadvantages job candidates or employees based on protected characteristics like race, age, or sex. With new laws emerging, such as Colorado's AI legislation effective in 2026, employers must proactively audit their AI hiring tools to mitigate this risk. The key is to use artificial intelligence as a supplement to human judgment, not a replacement, while rigorously tracking key diversity metrics.

What is Algorithmic Discrimination in the Hiring Process?

Algorithmic discrimination is defined as a situation where an automated system contributes to unfavorable treatment of an individual or group based on their actual or perceived protected characteristics. For example, if a resume-screening AI consistently awards lower scores to graduates of Historically Black Colleges and Universities (HBCUs) or to candidates over the age of 40, it is likely engaging in algorithmic discrimination based on race and age, respectively. This bias is often baked into the AI's model unintentionally, through biased historical data or flawed programming, but the discriminatory outcome is the same.

This issue is gaining significant legal attention. Following New York City's 2023 law requiring bias audits for automated employment tools, Colorado will soon mandate that employers using AI for "consequential decisions" take steps to prevent algorithmic discrimination. Similar legislation is pending in California, New Jersey, and other states, creating a complex compliance landscape for multi-state employers.

How Can You Conduct Due Diligence on an AI Recruitment Tool?

Before implementing any AI tool, thorough due diligence is non-negotiable. Since the underlying algorithms are often proprietary "black boxes," you must rely on rigorous questioning of the vendor. Remember, based on our assessment experience, an employer is ultimately liable for discriminatory outcomes, and claiming "the AI made me do it" is not a viable legal defense.

Key questions to ask vendors include:

• Has this tool undergone a bias audit—a formal assessment to detect discriminatory patterns—and what were the results?
• What specific criteria and data points does the tool use to evaluate candidates?
• How does the provider safeguard applicant data and ensure privacy?
• Can the vendor explain, in understandable terms, how the model was trained and tested?

What Key Metrics Should You Track to Monitor for Bias?

Tracking key diversity metrics before and after implementing an AI tool is critical for spotting unintended bias. This involves comparing the demographics of candidates who advance in your hiring process. For instance, if you introduce an AI-powered resume screener, analyze the percentage of women or people of color selected for phone screens or interviews compared to the period before implementation.

A noticeable shift—either positive or negative—along demographic lines should prompt a deeper investigation. While one data point doesn't establish causation, a consistent pattern is a major red flag. Consider using a simple tracking table to visualize the impact:

Should You Use AI Video Interviewing and Facial Recognition Technology?

Employers should exercise extreme caution with AI video analysis and facial recognition technology. These tools carry heightened risks of algorithmic discrimination and privacy invasion. For example, an Illinois law requires specific compliance steps for AI analysis of video interviews, and Maryland prohibits using facial recognition during interviews without explicit candidate consent. With the rise of deepfake technology, the potential for misuse and inherent bias in these tools is significant. It is often best to avoid them unless absolutely necessary and with robust, transparent safeguards in place.

How Can You Integrate AI with Human Judgment Effectively?

The most effective strategy is to use AI alongside human judgment, not as a substitute for it. Both humans and algorithms can be biased, but each can help check the other's blind spots. Use AI to augment human decision-making, not automate it entirely. Practical applications include:

• Using an AI tool to scan human-written job descriptions for biased language before posting.
• Having a human recruiter review the top candidates flagged by an AI screener, paying close attention to the system's reasoning.
• Employing AI analytics to identify potential patterns of bias in performance evaluations conducted by managers.

To mitigate the risk of algorithmic discrimination, employers should: 1) conduct thorough vendor due diligence and bias audits, 2) continuously track diversity metrics, 3) use AI as a supplement to human judgment, and 4) actively monitor the evolving legal landscape at the federal, state, and local levels. Proactive management of AI tools is essential for fair hiring and legal compliance.