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Using AI when hiring

Research shows AI better than humans at hiring staff

Using artificial intelligence when hiring staff can be efficient and lead to increasing diversity. But negative attitudes against AI can hold people back from benefiting from using the new technology, new research at London School of Economics shows. A recent US study shows a majority oppose AI use in making final hiring decisions.

A systematic review of hiring indicate that using artificial intelligence methods outperform humans with respect to making efficient hiring decisions, increasing diversity of recruits and leading to the best performance in new hires, the LSE researchers write in LSE Business Review.

Due to these results, the LSE  researchers recommend that AI be adopted in the contexts in which it has shown to outperform humans. 

“However, we also recognise that due to the findings on negative perceptions towards AI, this may represent a barrier to adoption. In cases where AI is performing better than humans, there may still be emotional factors holding people back from embracing these new technologies.”

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Referring to the present level of AI, the researchers write that when using AI, for example in hiring, the algorithm should be trained in the specific context such as job role and industry that it is used in.

“Although there are currently limits of AI prediction, there are also limits in humans’ ability to predict outcomes. For example, in our systematic review, when looking at predicting the future promotions of job candidates, despite its limited abilities, AI still performed better than humans.” 

“In any social decision-making scenario, we likely will not be able to make the perfect decision for a while, but this should not dissuade us from implementing technologies that are an improvement over previous methods.”

“We can’t conclude whether every type of AI should be adopted, but instead we offer a simple framework for how practitioners and researchers can determine decisions regarding the adoptability of their specific type of technology.” 

“The framework involves comparing the outcomes of human and AI decision-making in specific contexts and determining whether AI performs better, equal to, or worse than humans. In this way, we also believe that decisions on adoption should be context-specific to the scenario in which the technology is being employed and be monitored over time to ensure reliability.”

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The report is written by Grace Lordan, Associate Professor in the Department of Psychological and Behavioural Sciences at LSE, Paris Will, Lead Corporate Research Advisor at The Inclusion Initiative at LSE and behavioural scientist Dario Krpan. 

A recent study in the US by the Pew Research Centre shows that people are generally wary and uncertain of AI being used in hiring and assessing workers. 62% believe artificial intelligence will have a major impact on jobholders overall in the next 20 years, but far fewer think it will greatly affect them personally.

Three-in-ten told Pew Research the use of AI will even out – the help and the hurt will be equal.  

The Centre’s survey shows that a majority oppose AI use in making final hiring decisions by a 71%-7% margin, and a majority also opposes AI analysis being used in making firing decisions. 

“Pluralities oppose AI use in reviewing job applications and in determining whether a worker should be promoted. Beyond that, majorities do not support the idea of AI systems being used to track workers’ movements while they are at work or keeping track of when office workers are at their desks.”

Yet there are instances where people think AI in workplaces would do better than humans. Pew Research says. 47% think AI would do better than humans at evaluating all job applicants in the same way, while a much smaller share – 15% – believe AI would be worse than humans in doing that. 

And among those who believe that bias along racial and ethnic lines is a problem in performance evaluations generally, more believe that greater use of AI by employers would make things better rather than worse in the hiring and worker-evaluation process, the Pew survey shows.

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