Artificial intelligence (AI) was once a far-fetched idea created by the brightest science fiction minds. Now, anyone can access and use it for personal or professional purposes. A Workvivo found that 98% of HR professionals felt burnt out at work. Meanwhile, almost three-quarters (73%) said they don\u2019t have the tools they need to perform their job effectively. By automatically searching for keywords, identifying patterns, and consolidating data, AI can help companies improve job satisfaction and cut costs. But are the benefits worth the risks? Can HR managers promote fairness over bias when using AI?<\/p>\n\n\n\n
Programs that use AI are incredibly powerful, and they can improve many HR systems and processes. AI technology provides insights from HR data that many people would not consider. These insights are based on trends that are often difficult to pinpoint, such as cross-functional relationships or hidden costs. <\/p>\n\n\n\n
One area AI has been leveraged to support HR tasks is the recruitment process. AI can help HR professionals save time and cut recruiting costs by predicting hiring needs, creating custom job postings, and screening and scoring candidates. With AI, HR managers no longer need to spend endless hours manually sifting through job applications. Instead, AI-based tools can automatically screen candidates for the programmed desired attributes.<\/p>\n\n\n\n
If not used carefully, AI can be problematic for several reasons. Without proper consideration, planning, and review, HR professionals may introduce bias to AI programming. When this happens, bias and other ethical issues can become ingrained in AI operations and their results. For example, if the person using AI to review job applications is biased, their bias will likely affect how the AI reviews those applications. Engrained, unconscious bias can rule out viable candidates and inadvertently damage diversity, equity, and inclusion (DEI) efforts<\/a> across the organization. <\/p>\n\n\n\n AI is dependent on the parameters a person assigns it. To address bias in AI recruitment, instruct the AI tool to consider all applicants without looking at age, name, gender, or address. This information is commonly found on resumes and applications, but these details relate to protected characteristics under legislation<\/a>. No decision should be based on any protected characteristics. <\/p>\n\n\n\n When programmed and used correctly, AI can help organizations improve equity and inclusion in HR processes. HR professionals should first identify existing biases in the organization. They can do this by using surveys to assess organizational culture. AI can then be used to support next steps, like reviewing, diagnosing, and revealing elements of DEI. Answers to questions like why cultural norms exist, why some interactions and relationships work, and others do not, and what underlying criteria are attributed to various decisions can all be drawn out using AI tools. <\/p>\n\n\n\n By identifying which negative outcomes are rooted in bias, employers can use AI to counter the effects of any unconscious bias. For example, you can use AI to analyse new hires and consider what made them successful in the hiring process. If the shared characteristics among successful candidates are not wholly based on performance and behaviour, there is likely bias in the system. Dig deeper to address the bias to improve HR practices and build trust with employees<\/a>. <\/p>\n\n\n\n Whether you\u2019re using it yourself or wondering how it will affect the future of work, AI is on everyone\u2019s minds. When it comes to trending HR practices, just remember: you don\u2019t have to subscribe if it\u2019s not best for your organization<\/a>.<\/p>\n\n\n\n Better leverage the real value of AI tools in HR by supporting the frameworks they generate with guaranteed compliance advice and content for every jurisdiction. See our expert-reviewed content, Canadian-made compliance tools, and expert support services live!<\/p>\n\n\n\n Access\u00a0an extensive HR and safety content library<\/a> featuring thousands of compliant policies and templates.<\/p>\n\n\n\nWhat are the ways to address bias? <\/strong><\/h2>\n\n\n\n
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