Artificial Intelligence (AI) has reshaped HR processes, streamlining tasks like resume scanning, interviews, and even hiring decisions, promising efficiency and cost-effectiveness. Yet, this advancement encounters substantial legal and ethical challenges. In the current HR landscape, companies like L’Oréal leverage AI-driven chatbots for preliminary candidate interactions, while Hilton Hotels achieved a remarkable reduction in hiring time by conducting multiple interviews at once through AI-enabled digital tools.
However, the application of AI in HR has faced setbacks. Amazon’s AI-driven hiring tool exhibited gender bias, prompting the company to limit its role in decision-making. Another tool extensively scours over 20 social media platforms for candidate data, raising ethical dilemmas about privacy and fairness in evaluations.
While US laws like Title VII and the Americans with Disabilities Act cover AI-related discrimination, they emphasize that employers are accountable for the AI tools they choose, underlining the need for fair and unbiased practices. Several states, like New York City, enforce laws mandating bias audits before employing AI tools in hiring processes. In the EU, the forthcoming Artificial Intelligence Act introduces stringent regulations for AI’s role in recruitment and performance evaluation.
The growing legal concerns are apparent, demonstrated by the settlement between the EEOC and iTutorGroup, highlighting age discrimination due to AI-based practices. Similarly, Workday faces litigation over alleged systemic discrimination attributed to its AI technology. To avert the pitfalls associated with AI implementation, HR teams should consider crucial steps: comprehending how AI operates, scrutinizing claims of AI tools being bias-free, continuously monitoring and validating the outcomes generated by these tools, and ensuring human involvement in decision-making processes.
By adhering to these measures, HR teams can navigate through the complexities, choose AI tools responsibly, detect and address biased outcomes, and fortify their defences against potential legal challenges stemming from AI-related issues.
