However, without proper oversight, they also risk reinforcing existing biases—making bias audits essential to maintaining fairness in talent assessments.
Key Takeaways
- AI can boost efficiency and accuracy in talent assessments but must be monitored for bias.
- Bias audits ensure fairness and transparency in AI-driven hiring.
- Best practices include regular testing, human oversight, and compliance with regulations.
How Can AI Improve Talent Assessments?
AI’s role in talent assessments is best understood through its impact on what I like to call the “3 Es” Efficiency, Efficacy, and Experience.
- Efficiency: AI can significantly streamline the talent assessment process, automating tasks like candidate screening and skill matching, which traditionally require considerable time and resources. This efficiency allows HR teams to focus on more strategic activities while improving the speed and scale of talent decisions.
- Efficacy: When implemented thoughtfully, AI can enhance the accuracy and fairness of talent assessments by reducing human error and bias. For instance, AI-driven assessments can minimize biases, poor heuristics, and errors that often influence traditional hiring processes, leading to both more diverse and higher quality of hires. However, the success of AI in achieving this depends heavily on how well these systems are designed, trained, and monitored to avoid amplifying biases.
- Experience: AI can also elevate the experience of candidates and employees throughout the assessment process. From faster response time, to more engaging assessments, to using natural language processing to provide interview feedback, AI has the potential to make talent assessments more engaging and supportive for all stakeholders.
While the opportunities are significant, it's crucial to balance them with ongoing bias audits and ethical governance to ensure that AI-powered talent assessments are both effective and equitable. Without these safeguards, even the most advanced tools can unintentionally perpetuate biases present in the data or algorithms.
Why Bias Audits Matter in Talent Assessments
The use of AI in recruitment and talent assessments can provide major benefits, such as increased efficiency, reduced time-to-hire, and better quality of hires. However, it can also inadvertently introduce or reinforce biases if not carefully monitored. Because AI systems rely on vast amounts of historical data to make decisions, they can replicate patterns of discrimination that existed in the past. For instance, if past hiring data favored certain demographics over others, AI systems trained on that data could learn to make similar biased decisions.
Governments and regulatory bodies are increasingly scrutinizing these technologies. In fact, the European Union’s Artificial Intelligence Act classifies AI systems used in employment, recruitment, and workforce management as high-risk. These systems have the power to shape career trajectories and livelihoods, making the need for unbiased decisions critical.
To illustrate the severity, a recent study found that certain AI models gave biased job recommendations based on the nationality of candidates. Mexican subjects, for example, were more likely to be recommended for lower-paying roles than candidates from Sweden. This is just one example of the harm that can arise when bias creeps into AI-driven talent assessments.
Given these risks, bias audits are a vital step for any organization using AI in talent management.
The Role of Bias Audits
Bias audits are systematic reviews that test AI systems for unfair treatment or disparities across different groups of people. These audits help ensure that AI-driven tools are operating fairly and in compliance with ethical guidelines. In the context of talent assessments, bias audits can focus on analyzing how different demographic groups (such as gender, race, or age) are treated in recruitment processes, promotions, and performance evaluations.
Performing regular bias audits is crucial because AI systems change over time, especially as they interact with new data or adjust based on user behavior. A system that was initially unbiased can become biased over time if not properly monitored. Regular audits can detect such shifts and allow organizations to correct them before they become problematic.
- Outcome Testing: Perform adverse impact analysis - or other types of bias testing - based on model output to identify group differences in outcomes. Generally, additional explainability analyses are often required to fully ascertain whether outcomes are biased or there are plausible non-discriminatory explanations.
- Process Governance: Audit process - are there clear guardrails, controls, and human accountability in the development and deployment of the model?
As regulations evolve, regular audits are becoming mandatory. For instance, New York City's Local Law 144 now requires companies to perform bias audits on automated employment decision tools (AEDTs) to ensure they don't perpetuate discrimination.
Best Practices for Bias Audits in Talent Assessments
To maintain fairness and transparency in talent assessments, organizations should adopt several best practices when conducting bias audits:
Regular Testing and Monitoring: Implement a consistent schedule for bias audits. AI systems must be monitored continually to catch potential biases early. Regular testing ensures that any disparities are caught before they have a large-scale impact on recruitment or promotions.
Human Oversight and Accountability: While AI can enhance decision-making, it’s essential to keep humans in the loop. AI-driven assessments should always have human oversight, especially in high-stakes situations like hiring and promotions. This ensures that biased or problematic outputs are caught and addressed.
Transparency and Documentation: Organizations should be transparent about the data they use and how their AI systems make decisions. This transparency builds trust among stakeholders and ensures accountability. Furthermore, maintaining thorough documentation of all bias audits helps demonstrate compliance with regulatory standards.
Compliance with Regulations: As mentioned earlier, regulations around AI in employment are rapidly evolving. Organizations must stay up-to-date with laws such as the EU AI Act and NYC Local Law 144 to ensure compliance. Aligning internal practices with these regulations is key to reducing risk.
Leveraging AI Governance Platforms for Bias Audits
Given the complexity of AI systems, many organizations are turning to AI governance platforms to help manage bias audits and ensure compliance. These platforms can automate the process of testing for bias, provide real-time monitoring, and offer tools for reporting and transparency.
Plum’s Commitment to Fair Talent Assessments
Plum, a leader in talent assessment solutions, has made bias audits a core part of its process to ensure that its tools are fair and equitable. As part of their commitment to ethical AI, they conducted a comprehensive bias audit to ensure their talent assessment tools were free from unintentional bias.
"At Plum, we recognize that bias in talent assessments not only undermines fairness but also diminishes the true potential of our workforces. That's why we are steadfast in our commitment to rigorous bias audits. These audits are not just about compliance—they are a core part of our mission to ensure that everyone is assessed based on their abilities and potential, not prejudiced by background or circumstance. We are dedicated to continuously refining our methods to deliver the most equitable and predictive talent insights in the industry," said Caitlin MacGregor, CEO and Co-founder of Plum.
Final Thoughts
As AI continues to transform the way companies assess talent, ensuring fairness and transparency in these systems is essential.
Bias audits offer a practical and effective way to detect and mitigate bias. By adopting best practices for bias audits and staying informed about regulatory developments, companies can build trust in their AI-driven talent assessments and make strides toward a more equitable future for all candidates.
Whether you’re just starting to use AI in talent assessments or are well on your way, making bias audits a priority will ensure that your organization’s decisions are fair, transparent, and in line with emerging regulatory standards.
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Guru Sethupathy, PhD, is the founder and CEO of FairNow, the leading platform for AI governance. With nearly two decades of experience in AI risk management and strategy, he has advised Fortune 100 executives on leveraging analytics and AI to drive business value. Prior to founding FairNow, Guru led the People Analytics, Technology, and Strategy function at Capital One, and served as a trusted advisor at McKinsey. He holds a BS in Computer Science from Stanford University and a PhD in Economics from Columbia University.