AI In Recruitment: Balancing Innovation With Responsibility

Four ways to foster transparency and equity

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. 

Gartner 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. 

Yet, there are fears about the rise of AI in hiring. 

Lack of adequate safeguards in AI selection tools can perpetuate biases, leading to unjust or discriminatory outcomes for candidates, particularly those from marginalized communities. This undermines diversity and inclusion initiatives within organizations and contributes to diminished employee performance, increased turnover rates and lowered organizational effectiveness. 

There’s also concern about a lack of transparency about what AI algorithms are doing. When individuals don’t understand what is being assessed, trust in the process diminishes, regardless of the model’s effectiveness or quality. This can leave people feeling that hiring processes are unfair or discriminatory. 

Given these challenges, it may feel like there are few easy answers to implementing AI-based tools into your selection process. However, amid these complexities, there’s a growing body of best practice guidance to turn to. By adhering to established best practices and maintaining a commitment to continuous improvement in processes, organizations can harness the power of AI to drive fair and effective recruitment outcomes. 

Here are a few key considerations to make when implementing AI-based tools in your process:

1. Use of Diverse, Representative Data Across Demographics to Train AI and Mitigate Bias

When considering the adoption of AI-based tools for your business, it's important to recognize the prevalence of models trained on historical data due to their accessibility. Relying solely on such data poses significant challenges, as it inherently carries biases originating from human creators. To ensure that the AI tools you invest in deliver fair and effective outcomes, it's vital to prioritize solutions built with a data-centric approach. 

Therefore, when evaluating potential tools, it's imperative to verify that the designers have thoroughly audited and curated the dataset, emphasizing on inclusivity and fairness. By choosing solutions crafted with a data-centric mindset, you can be confident in the integrity and reliability of the AI tools integrated into your processes.

2. Adoption of Explainable AI Methodologies Providing Interpretable, 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 interpretability and provide clear and understandable explanations for their decisions. This should be documented in technical manuals or other user guides and be easily accessible to people in your organization.

Not all vendors in this space will have as much expertise in HR practices as they do in AI development. Do your own research to ensure your use of these tools will allow you to meet the best practices in guidance, such as the Society for Industrial and Organizational Psychology’s “Considerations and Recommendations for the Validation and Use of AI-Based Assessments for Employee Selection” and the U.S. Equal Employment Opportunity Commission’s document “Select Issues: Assessing Adverse Impact in Software, Algorithms, and Artificial Intelligence Used in Employment Selection Procedures” under Title VII of the Civil Rights Act of 1964.

Be aware of potential legal, ethical and reputational risks associated with opaque AI systems. Seek out solutions that prioritize fairness, accountability and regulatory compliance. 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 untrustworthy highlights the importance of accountability.

3. Ensure Candidates’ Assessment Experiences Are Pleasant and Use of AI Is Clear and Evident

The experiences you provide to potential hires can have a substantial impact on the quality of candidates you can bring into your organization. Poor experiences related to recruiters and interviewers could result in your organization missing out on 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 openings, as Drake Star’s Q1 2024 report notes.

Transparency and communication are key in fostering trust and maintaining a positive candidate experience. Provide clear information to candidates about the use of AI tools, including how their data will be used and what impact it will have on the hiring decision. Strive to make the assessment process intuitive, user-friendly, engaging, incorporating features that enhance interaction and provide valuable feedback to candidates. By prioritizing transparency and candidate experience, organizations can enhance their reputation and attract and retain top talent.

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 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.

Additionally, prioritizing education and training for individuals within your organization is crucial. Equipping your team with the necessary knowledge and skills enables them to harness AI responsibly and ethically, driving efficiency and effectiveness across your processes. This investment in education not only helps your organization stay ahead of the curve, but also ensures compliance with legislation related to AI-based tools in hiring. Regular audits and updates will help mitigate any potential risks associated with non-compliance, ensuring your organization operates ethically and responsibly while using 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 and representative data to train AI models, ensuring your vendors provide clear explanations for AI predictions, and investing in education and training, 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. 

Sara Gutierrez is Chief Science Officer at SHL, where she leads a talented team of people scientists in the rigorous design and validation of innovative assessment products that solve the organization’s people questions.