A Pew Research Center study shows that Americans remain wary about the increased use of artificial intelligence (AI) in hiring and evaluating workers. Out of the 11,004 US adults surveyed in December 2021, 32% believe that AI will do more harm than good to workers over the next 20 years, while 71% were against using it to make hiring and firing decisions. Respondents feared that AI-driven recruitment might impact evaluations and result in job losses, and that systems would infringe on their privacy by collecting too much personal information. However, 40% still felt that AI could provide benefits such as reducing human errors, speeding up hiring processes and eliminating potential biases. Respondents also highlighted the potential of AI-driven performance evaluations to provide a more objective and consistent assessment of workers’ skills and productivity. Some other concerns encompassed the hiring process to resume screening, applicant evaluation to performance monitoring and personnel decisions. The majority of respondents were concerned that AI systems would infringe on their privacy by collecting too much personal information, such as browsing history or social media activity.
As AI continues to make inroads into the workforce, tech industry leaders are calling for policymakers, businesses and developers to address these concerns. In the European Union, regulators have called for transparency in AI systems, education and training for workers to adapt to a rapidly changing job landscape, and limits to AI training to tackle these concerns before they become too severe. Meanwhile, regulators have already begun paying attention to how AI models are trained and how they could affect citizens’ rights.
AI’s growing role in the workplace presents both benefits and concerns such as privacy, fairness and discrimination. By taking a proactive approach to policy, transparency, and education, politicians are ensuring that AI serves as a force for good. However, there is still work to be done to ensure that potential risks are mitigated, and concerns around job loss, bias, discrimination and privacy are addressed before artificial intelligence models are deployed more widely.
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