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Using AI Data Analytics for Talent Decisions

Integrating Artificial Intelligence into HR operations is becoming more commonplace, creating more agile, human-centric workplaces. However, with AI usage also comes a range of challenges that need to be addressed. We look at 5 key concerns when using AI to help make talent decisions and recommendations to overcome them.

Can AI and Automation Be Trusted?

AI is revolutionizing HR by streamlining processes, enhancing decision-making, and optimizing employee experiences. Automated systems can handle much of the hiring process from screening candidates and conducting interviews to providing developmental feedback and helping onboarding. In theory, this frees HR professionals to focus on strategic initiatives, employee engagement and development. In addition, AI data analytics provide insights into workforce trends, enabling proactive measures for talent management, leadership development and succession planning.

As more organizations embrace the use of AI, more questions and concerns will be raised by those inside and outside of the organizations, making it critical to fully understand any tools being used in more detail. Would you know if the AI trusted? Is there built in bias with the decisions being made? Will candidates accept not having human interaction? Are processes still legally binding? We share how best to answer these key concerns.

5 Concerns of AI Usage for Talent Decision-Making:

1. Hidden Bias

AI systems learn from human data, which inherently can be biased, potentially leading to adverse impact in job recommendations, or undue emphasis on irrelevant aspects of a candidate’s responses.


Use diverse and representative data across different demographics to train the AI and mitigate bias. Human subject matter experts (SMEs) should be involved in the design and development of any AI based assessment to proactively remove or reduce bias that may be introduced.

2. A Mysterious Black Box

An AI system’s internal mechanisms are often not accessible or comprehensible to human users, making it difficult to understand and justify why the system makes specific decisions or predictions.


Adopt explainable AI (XAI) methodologies and techniques by providing interpretable and transparent AI models. With XAI, users can understand and share the reasoning behind AI-generated decisions, thus fostering trust, accountability, and the identification of potential biases or errors.


3. Unknown Candidate Reaction

AI brings in an extra dimension to the hiring process that could impact how the candidate views the organizations.


Ensure a candidate’s assessment experience is as pleasant and transparent as possible. Use human-like elements to explain how the AI will be used to empower candidates with the information they need to know to mitigate the perception of an unjust AI.


4. Accuracy in Performance Prediction

AI and AI-based assessments may be limited in understanding the context and nuances that humans can resolve.


Hold AI-based assessments to the same standards as traditional assessments. Use validation studies to ensure assessments are reliable, valid, and predictive of future performance, as detailed in the Society of Industrial and Organizational Psychology principles.


5. Legal Concerns

The use of AI in the workplace might result in questionable outcomes during the hiring process that can lead to related legal challenges. As legislation and rules regarding AI in the workplace increase, organizations may struggle to stay up to date with their implications.


Laws and legislations are made to protect you, your candidates, and ensure a fair assessment process. Any AI tools being used should be subject to rigorous evaluation to ensure vendors can communicate any concerns with legal compliance and direct you to specialists in AI ethics and laws.


To read more about our research into evolving talent trends, key insights and future strategies based on our survey to over 1,600 HR professionals, check out our Global Talent Trends Resources page.

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Karim Badr

Karim Badr is a Research Scientist in SHL’s Science team. He is a psychometrician with a keen understanding of data science and measurement, specializing in the development of innovative assessment products that utilize cutting-edge technology. His work involves harnessing the power of psychometrics and artificial intelligence to create innovative assessment solutions that provide valuable insights into human behavior and capabilities.

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