Artificial intelligence (AI) has captivated every aspect of our everyday lives and expanded into various industries, including HR, reimagining areas such as analytics, recruitment, and employee wellness. Driven mainly by the idea that it will help maximize the value of data, more and more HR leaders are implementing AI into their operations. However, AI can be very obscure, making it hard for humans to understand why a prediction is being made. This has led to a call for “Explainable AI.”
The demand for explainable AI has risen after a surge of criticism towards numerous critical failures such as biases and unexplainable decision-making. Defined, explainable AI is a collective term for a range of AI techniques intended to map how other AI systems come to their results, what reasoning they use, and which factors may influence them.
This makes it easier to follow the reasoning of an algorithm and to assess whether its outcomes are safe and valid. Mapping the factors that influence the outcome of an algorithm increases its reliability and maximizes human understanding. When entrusting AI in HR decisions, HR leaders have to be sure its reasoning is accountable and ethical as their decisions can have significant impact on individuals and their company as a whole.
Many AI models have presented HR leaders with a “black box” that relies on millions of complex, interwoven parameters in order to deliver outcomes that HR teams are expected to trust and act upon, even if they may not understand them. Data from black box solutions is often hidden due to its proprietary nature, making it difficult for humans to grasp complex internal workings, features, and data representations.
With explainable AI in HR, however, HR teams don’t have to be kept in the dark with algorithms that don’t disclose the inputs and reasoning behind predictions. Explainable AI will explain how and why a prediction is made so that you know where and how to influence change in the future.
While AI is meant to make the lives of HR teams easier, decision-making requires more than blindly trusting AI-informed decisions. If you’re given results that you can’t explain let alone understand yourself, it may be difficult to justify potentially business-altering choices. It’s imperative that AI’s powerful capabilities are understood by the users, to be sure that the technology serves the right purpose.
Explainable AI is there to not only make predictions, but explain why it made them. It allows HR to see how the model works, ask for insight, introspection into parameters, and look for motivation about how the model came up with the solution. Putting the decisions completely in the hands of AI is a risky business; AI and human minds working together will make decision-making the most successful.
Need an alternative to your black box solution or just starting your people analytics software journey? Consider ZeroedIn. Settling for large, big-ticket people analytics software like Visier puts you at risk of choosing a solution that will provide you with data, but no explanation. Explainable AI is built-in to ZeroedIn’s advanced people analytics model so that you can back your HR decisions with data-driven evidence. Contact our data scientists today for more information or to request a demo.