Automated Talent Assessments

Heads up for anyone who uses AI in assessments for applicants - research across 13,000+ people and 12 studies shows that job candidates respond to assessments differently when they believe AI will be analyzing their results. When people think AI will analyze their assessments, people tend to emphasize analytical traits and downplay empathy, creativity, and intuition - important human traits. This is systematic misrepresentation and it affects who moves through the hiring process. If you're using an assessment that job candidates that believe may be analyzed by AI, you may be screening out some of the people you want and could benefit from. What can you do about this if you're using AI in assessments? The researchers suggest that you: 1️⃣ be radically transparent about the use of AI and explain that the AI can and does value diverse human traits like creativity, intuitive problem solving, etc. 2️⃣ perform regular behavioral audits and look for behavioral adaptation or homogenization towards analytical responses. 3️⃣ take a hybrid approach - some major companies claim they have humans reviewing applications and assessments, including guaranteeing that ultimate assessment and decision making is made by humans. Please take a moment to read the full HBR article here: https://lnkd.in/ehxwCJZu

What do two I/O psychologists know about the future of hiring? Enough to fill up an entire podcast apparently. “The future of assessments is about customization at scale. AI allows us to generate and adapt assessments in real-time, making them more relevant to specific job roles.” –Ben Williams If you are into #AIbasedassessments you HAVE to listen to the latest epiosode of Psych Tech @ Work featuring Ben Williams founder and managing director of Sten10 as we discuss how AI is reshaping the field of psychometric assessments and hiring processes. Our conversation dives into the evolving landscape of AI-driven assessments and the important and ever changing role of ensuring there is a "human in the loop" Topics Covered: -The role of AI in automating and customizing assessments Emerging challenges in trust, fairness, and explainability in AI-powered hiring -The importance of designing job-specific psychometric tools that align with organizational needs -AI's potential in generating, scoring, and validating assessments -Future implications of AI on entry-level and senior hiring roles -Prompt engineering, AI literacy, and the limitations of AI-generated assessments. Real-world examples of AI based assessments, including how AI can generate coaching tips and personality profiles, as well as potential concerns regarding the over-reliance on AI outputs. Takeaways: -Custom psychometric assessments are the future: Moving beyond off-the-shelf tools, companies can develop highly specific and job-relevant assessments using AI. -Prompt engineering for assessments: Organizations can create better assessment tools by focusing on AI prompt development and optimization. -AI literacy is essential for hiring professionals: As AI becomes more embedded in hiring, HR professionals need to understand its benefits and limitations to apply it responsibly. Listen in for the details!

The AI Assessment Effect Candidates often tend to adjust their answers or behavior to match what they believe the “ideal candidate” profile looks like. A new study published earlier this month found that when candidates believe they’re being assessed by artificial intelligence, they emphasize analytical skills and downplay their intuitive and emotional skills. This so-called “AI assessment effect” stems from the widespread assumption that AI-based evaluations prioritize rational, data-driven attributes over human-centric abilities. Researchers warn that if job seekers tailor their behavior to what they think AI values, their true competencies and personalities may remain hidden, undermining the integrity of the recruitment process. In addition if most candidates assume AI favors analytical traits, the talent pipeline could become increasingly uniform, limiting diversity and reducing the variety of perspectives within organizations. The researchers recommend 1) Radical transparency: Don’t just disclose that AI is used in assessments—be explicit about what it evaluates. Clearly communicate that your AI values a range of traits, including creativity, emotional intelligence, and intuitive problem-solving. Share examples of successful candidates who excelled by showcasing these qualities. 2) Regular behavioral audits: Go beyond demographic bias checks. Look for patterns of behavioral adaptation: Are candidates’ responses becoming more homogeneous over time? Is there a noticeable shift toward analytical self-presentation at the expense of other valuable traits? 3) Hybrid assessment models: Combine AI and human judgment to ensure a more balanced and holistic evaluation of candidates. See research published in the June issue of the Proceedings of the National Academy of Arts and Sciences. https://lnkd.in/ebtD4HBd