JUNE - 2023CIOAPPLICATIONS.COM9learning models are not representative of the broader subject population, then models can amplify and scale bias. Just like within broader HR analytics, identifying the right business problems to solve is key to ascertaining value from HR in AI. Fig. 2 The HR / AI EcosystemPotentially the most interesting and under-explored components of the HR AI ecosystem are the modeler, the user, and the subjects. These three can be thought of as the "decision engine" that translates AI into action. What's unique about the use of AI in HR is that, unlike consumer-facing technology, the buck doesn't stop with the user. In order to realize value from a predictive turnover model, the usermust leverage an additional intervention to influence an employee's behavior. This is where HR's consultative acumen and some behavioral science come into play. Some Underrated HR/AI Use CasesMost HR / AI hype is limited to a small number of use cases, like automated resume screening and predictive turnover models. Those use cases are powerful, but machine learning is an incredibly versatile technology that can be applied to a broader suite of problems. Here are a few lesser-known use cases for HR practitioners to take away:1.Creating markets ­ the talent marketplace is one of the most exciting recent innovations in HR. Most talent marketplace platforms are powered by (read: the AI is trained by) skills data, so it's crucial to have a strategy for the collection, validation, and governance of this data. Don't think of talent marketplace as being just an internal mobility platform ­ AI has the ability to power markets for gigs, mentors, or even the services of teams within an organization.2.Job families and workforce planning ­ many organizations have job titles and job descriptions that are a total mess. This makes doing an analysis on what talent the organization has and what talent it needs in the future a challenge. One type of machine learning, unsupervised learning, can be used to read data from job descriptions and cluster them together, creating data-driven job family taxonomies that can be used for workforce planning.3.Adding color to performance management­ a really cool paper by Andrew Speer from Wayne State University outlined a new method for measuring employee performance: Most organizations have a lot of data in the form of text in manager reviews as well as peer and self-reviews, but this is an underutilized data source. Speer demonstrated that numerical performance scores created from natural language processing (NLP) correlated highly with human ratings of performance. Machine learning systems, or models, have the potential to help us to make more accurate predictions or create smarter tools
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