You don’t have to be a prophet to foresee that artificial intelligence will also play an essential role in the field of human resource management. It will have a decisive impact on the way we connect people in the future.
Using human-machine partnerships to improve the process of connecting people to the right job is relatively new to how most organizations hire. While there are many favorable advancements and novel solutions that promote more inclusive hiring, there are several risks to consider. First and foremost, we must challenge the assumption that hiring managers know what constitutes an ideal employee. The fact may be that many organizations don’t know what type of person with which mix of skills excels in their environment. To generate large, robust models, we will need more data not only about potential hires but also data about how past hires have performed. The data, generally considered proprietary, will improve the machine-learning systems, and they will help expose biases and unseen hiring practices of organizations. For human-machine partnerships to enable more inclusive talent practices, we also need to do more to understand algorithmic bias. People seeking work must be able to know how their profiles are being interpreted by the machine-learning tools that employers will use to inform their hiring decisions.
The professors, Peter Cappelli, Prasanna Tambe, and Valery Yakubovich wrote in a 2018 white paper, “The outcomes of human resource decisions (such as who gets hired and fired) have such serious consequences for individuals and society that concerns about fairness – both procedural and distributive justice – are paramount.” (source)
Organizations will need to be steadfast in ensuring that their systems’ decision-making algorithms align with their values. Ethical practices, including full visibility to the factors informing algorithmic hiring models, will be essential to prevent biases and for these tools to promote a shift toward inclusive talent.
Further reading: Employment and Skills in the Age of AI