68% of recruiters believe AI will remove unintentional bias (Recruit & Hire Better)
Unconscious bias in recruitment has long posed challenges to creating diverse and inclusive workplaces. However, machine learning (ML) and artificial intelligence (AI) are emerging as powerful tools to help reduce these biases. By leveraging data-driven approaches, AI can foster fairer hiring practices, ensuring that decisions are based on objective criteria rather than subjective biases.
Machine learning algorithms enable a data-driven evaluation of candidates, analysing relevant information such as skills, qualifications, and experience to determine a candidate's suitability for a role.
Unbiased job adverts
AI can also help eliminate biased language and content from job advertisements. By reviewing past data and identifying patterns, AI algorithms can suggest inclusive language and highlight essential qualifications, skills, and experiences required for a role. This approach ensures that job ads are more appealing to a broader range of candidates, leading to increased diversity in applicants in the first instance.
Structured interviews
AI can assist in conducting structured interviews, where all candidates are asked the same set of predetermined questions. By standardising the interview process, AI minimises the risk of bias creeping into the evaluation. Additionally, AI algorithms can analyse interview responses objectively, focusing on the content rather than subjective initial impressions.
Diversity analytics
AI can analyse vast amounts of HR data to identify patterns and trends related to diversity and inclusion. By examining data on candidate demographics, hiring decisions, and promotion rates, AI can help HR professionals identify potential areas of bias and take proactive steps to address them.
In the 1970’s and 80’s a number of major US orchestras introduced blind auditions. This subsequently resulted in the percentage of female musicians in the five highest-ranked orchestras increasing from 6% in 1970 to 21% in 1993.
Predictive analytics
AI can now use predictive analytics to identify candidates who are most likely to succeed in a role based on historical data they have access to, simply by focusing on performance indicators rather than demographic factors.
Candidates selected by AI are 14% more likely to pass the interview stage and 18% more likely to accept a job offer (Carv - AI Purpose-Built for Recruiters), and companies using AI recruitment tools have reported a 20% increase in employee retention by hiring better-fit candidates.
What are the potential pitfalls?
One of the significant challenges with AI is that it is only as unbiased as the data it is trained on. If the training data contains biases, the AI will likely replicate those biases. An example of this was with Amazon. They faced criticism when their AI recruitment tool favoured male candidates for technical roles because the training data inputted was biased towards men.
To mitigate the risk of biased AI, continuous monitoring and adjustment of AI algorithms and human engagement and oversight is essential. Recruiters should use AI as a tool to assist in decision-making rather than relying on it entirely. Combining AI with human judgment helps ensure a more holistic and fair evaluation of candidates.
