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AI Recruiting: 4 Key Considerations When Implementing AI-Based Tools in Your Hiring Process

Gartner¹ reports that 76% of HR leaders believe if their organization does not adopt and implement AI solutions like generative AI in the next 12 to 24 months, they will be laggards. Here are 4 things to consider when using AI in recruitment.

Fears Rise About AI Recruitment Tools

ChatGPT and other large language models catalyzed a rise in AI applications. More companies are now expected to invest in AI capabilities to streamline and optimize the hiring process by enabling recruiters to identify, attract and retain top talent more effectively and efficiently than ever before. Yet there are fears about the rise of AI in recruitment².

We look at the key areas that organizations should be aware of when looking to implement AI-based tools to aid their recruitment:

1. Use of diverse, representative data across demographics to train AI and mitigate bias

When considering the adoption of AI-based tools, relying solely on models trained on historical data poses significant challenges, as it inherently carries biases originating from human creators. By choosing solutions crafted with a data-centric mindset, you can be confident in the integrity and reliability of any AI recruitment tools integrated into your processes.

2. Adoption of explainable AI methodologies providing transparent AI models

Avoid black-box models that offer limited insights into their decision-making. When evaluating AI solutions, seek out solutions that prioritize transparency and provide clear explanations for their decisions.

Be aware of the potential legal, ethical, and reputational risks associated with opaque AI systems. A recent class-action lawsuit filed by a man in the U.S. who was rejected from a job because AI video software judged him to be untrustworthy highlights the importance of accountability³.

3. Ensure candidates’ assessment experiences are pleasant and use of AI is clear

The experiences that you provide to potential hires can have a substantial impact on the quality of candidates that you can bring into your organization. Provide clear information to candidates about the use of AI recruitment tools, including how their data will be used and what impact it will have on the hiring decision. By prioritizing transparency and candidate experience, organizations can enhance their reputation and attract and retain top talent.

SHL research shows that 42% of candidates declined job offers due to negative experiences during their reviews. That’s a real problem considering that more than 75% of companies are struggling to find the right people with the right skills to fill their job openings⁴.

4. Invest in education and training to empower people to learn about and adjust to AI

Embracing a forward-thinking mindset and staying informed about emerging AI trends are essential for ensuring your business remains competitive in the future. By educating yourself on the latest developments and gaining a deep understanding of AI tools and processes, you can position your organization for success in the evolving landscape of AI.

AI-powered selection tools don’t have to be scary, opaque, or black-boxed. By adopting best practices when implementing these tools, using diverse data to train AI models, ensuring your vendors provide clear explanations for AI predictions, and investing in education, you can set yourself on the path to mitigate bias, ensure equitable outcomes for all candidates, build trust with stakeholders, and decrease your legal, ethical, and reputational risk.

Learn more about how SHL helps organizations gather reusable talent intelligence critical to future-proof people decisions, starting with pre-hire into onboarding and throughout the employee lifecycle.




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Sara Gutierrez

As a Research Leader within the Research & Development team at SHL, Sara Gutierrez leads a talented group of research scientists in the design and development of SHL’s most innovative assessments, including multimedia simulations, computer adaptive ability tests, and design and development of assessments for mobile delivery. Most recently, her focus has been on the intersection between testing and technology and the opportunities this provides to drive innovation in assessment design. Her team shares the goal of creating assessment that provides interactive and engaging experiences that more accurately reflect real-world business scenarios.

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