Why is Explainable AI Important for HR ?
Companies across the world are looking for ways to incorporate or expand their use of AI in HR processes, either by purchasing HR technology with AI built in, or by recruiting data scientists to join or support their people analytics team to develop in-house solutions.
There are many uses cases for AI within the HR space. This article aims to address why ensuring such solutions come with explainable AI is key to building them into a company’s HR processes.
What is Explainable AI?
Explainable AI (XAI) refers to the concept of designing and developing artificial intelligence (AI) systems and machine learning models in a way that allows humans to understand and interpret their decisions, behaviors, and predictions. The goal of XAI is to make AI systems more transparent and accountable, addressing the "black box" problem that often plagues complex machine learning models.
When a machine learning algorithm is trained, it looks for patterns in the data it’s been provided (i.e. the training data) to then make a prediction on new data based on those patterns. When you build a machine learning algorithm into an HR solution, that prediction turns into a recommendation (to hire, to promote, etc.). Explainable AI is a method that is used to understand and interpret the predictions made by the algorithm.
Why is Explainable AI Important Within HR?
In addition to the ethical and legal considerations of using explainable AI within an HR context, there are very practical reasons for ensuring AI solutions developed for HR are explainable.
Transparency and Trust
When users can understand why a recommendation is being made and are allowed to decide whether they agree with the recommendation, they are more likely to trust and adopt the AI solution (1).
Mitigate Bias
Explainable AI reveals the primary drivers of a given recommendation. Reviewing the drivers can help to uncover biases that are built into the model based on historical patterns, at which point the model can be adjusted to address those biases. Users can then use AI to inform their decisions without relying on their own judgment, which is often influenced by biases (at least partially). HR decisions, such as recruitment, hiring, and promotion, can have a significant impact on individuals' lives and careers. XAI can help identify and mitigate bias in AI-driven HR processes, ensuring that decisions are fair and do not discriminate against specific demographic groups.
Enable Data Driven Decision Making
When a user has information about why a recommendation was made, the information can be used to make data driven decisions and effective interventions (2). For example, knowing why someone is high risk of leaving allows the manager to take targeted actions to retain them. Knowing why someone was recommended for a role or for a promotion allows the manager to better measure them against other candidates. While AI can inform HR decisions, humans should make the final decision, and explainable AI helps them to make better decisions.
Continuous Improvement
As users observe and learn from how AI models make decisions through XAI, they can improve their decision making process over time to include more data, with less reliance on subjective factors that often inform important people decisions.
In summary, ensuring AI solutions in HR supports adoption, helps to mitigate bias, and enables data driven decision making. In a future article, I will offer a few examples of building explainable AI into HR processes.
(1) Explainable AI – how humans can trust AI, Ericsson
2 Why businesses need explainable AI—and how to deliver it, McKinsey
ABOUT THE AUTHOR
Martha Curioni is a seasoned HR professional with data science experience. She’s worked for large, global organizations such as Deloitte and IQVIA, as well as an HR Tech Startup where she developed an algorithm to match employees to opportunities. She is passionate about designing HR programs and processes that are centered around data driven insights or AI solutions.
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