Artificial Intelligence (AI) is transforming the Human Resources and Recruitment landscape, offering efficiency and data-driven insights. However, the potential for bias in AI algorithms is a growing concern. Organizations must prioritize fairness and transparency in their HR processes and systems to harness the benefits of AI Recruitment in Talent Acquisition while mitigating its risks. This blog explores the critical role that optimizing your existing technology and reporting play in mitigating AI bias and ensuring equitable outcomes.
Understanding Bias in AI Recruitment
AI vendors build their systems by feeding them training data. If that data reflects societal biases, the AI will perpetuate those biases, often in ways that are harder to detect and more difficult to eliminate. Additionally, the biases of the people developing the systems can influence their behaviour. This training bias can lead to discriminatory outcomes in hiring, promotions, and other HR decisions. An AI system trained on historical hiring data may inadvertently favor candidates from certain demographic groups, leading to a lack of diversity in the workforce, or to a perfect candidate being overlooked in favor of one that superficially resembles past hires. We see examples of this with AI screening tools that consider names as part of their algorithms; in the same way that some recruiters will unconsciously favor candidates with more common and familiar names, AI tools will do the same if those names are strongly represented in their training data.
ATS Optimization and Bias Mitigation
Applicant Tracking Systems (ATS) play a pivotal role in hiring, and many organizations are incorporating AI into that hiring process, either as part of the ATS or alongside it. To avoid bias, organizations must carefully configure their ATS in several ways: first, they should eliminate discriminatory questions and filters. This step may be obvious, but it’s essential, and discriminatory screening questions are often subtle (and unintentional), and we advise our clients to review their screening processes regularly. Secondly, blind resume screening can help reduce bias; by removing or obscuring candidate names, gender, or other protected attributes, an organization’s AI tools are forced only to consider relevant professional qualifications and skills, ensuring an unbiased process. As ATS experts, Velocity helps organizations design and optimize their ATS for this purpose, working with existing systems and assisting organizations to implement new ones.
Reporting drives fairness
Reporting plays a key role in ensuring your recruitment process remains bias-free; how you collect, extract, and process data is vital to identifying problem behaviours and eliminating them. By analyzing data on hiring rates, time-to-fill metrics, and employee turnover by demographic group, organizations can uncover these problematic behaviors and eventually prevent them. Velocity helps companies prepare this data by configuring the data entry points, advising on the best ways to extract and process the data, and helping to build the reports that analyze it.
Key Strategies for Addressing Bias in AI Recruitment
To sum up, ensuring that the data used to train an organization’s AI tools is inclusive and diverse and that the AI analyzing the ATS data is structured to minimize bias. At the same time, organizations must have a reporting process that offers insight into recruitment and hiring behaviour so that any biased behaviour is detected early and can be effectively corrected. Additionally, any AI tool deployed anywhere in the business must have human oversight, the hiring process still depends on qualified decision makers, and that everyone who interacts with or has responsibility for the AI is well-educated on how to identify bias and report it.
Velocity HCM helps organizations identify and eliminate bias and ensure fairness in the talent acquisition process, specifically AI recruitment; our configuration, reporting, and training experts can help your organization prepare for an effective AI Recruitment strategy.
