What is AI bias in recruitment?
AI bias in recruitment is unfair discrimination that can occur when artificial intelligence systems help with hiring. This bias can come from different sources, such as AI training models or algorithms. Biases can affect AI-driven hiring decisions and potentially cause legal issues. When using AI in the hiring process, it can be beneficial to consider the several types of biases that might occur:
Sample data
Sample data happens when the data used to train AI doesn’t accurately reflect the real-world population. For instance, AI may over-represent or under-represent certain groups.
Algorithmic
This bias occurs because of the algorithm’s design, not the data. Factors like the neural network’s complexity or the algorithm’s prior information can introduce this bias.
Representation
Similar to sample bias, representation bias can occur during data collection. Data collectors may not evenly distribute data, fail to account for outliers or anomalies, or overlook the population’s diversity, leading to unequal representation of all demographics.
Measurement
This type of bias happens when there are errors in reporting conclusions or during the construction of the training dataset. These mistakes can result in biased outcomes against certain groups.
Causes of AI recruitment bias
Understanding how and why AI can be biased can help you find a solution. Below are a few reasons why AI hiring bias happens.
Distorted training data
AI models base their decisions on the data they learn from. If this training data has biases, the AI can make biased decisions. For example, if there are historical biases in the data collection or if the data lacks diversity, the AI will likely reflect those issues, resulting in biased hiring, such as favouring particular groups.
Algorithm design
An algorithm’s design can cause hiring bias when the algorithm influences how it processes information and makes decisions. When developers unintentionally include biased assumptions or parameters in the algorithm, these biases can amplify the AI’s decision-making process. For example, if an algorithm prioritizes specific keywords or experiences common to a particular demographic, it may favour candidates from that group while disadvantaging others. The algorithm’s complexity and the required data can introduce bias if developers don’t carefully manage these factors. Even with unbiased training data, the algorithm can still produce biased outcomes based on its design.
Human involvement
Human involvement in AI can add to bias in recruitment because humans naturally hold biases, influencing how they design and interact with AI systems. When developers and users input their assumptions, preferences, or subjective judgments into the AI, they inadvertently embed these biases into the system. AI often uses reinforcement learning and feedback loops, which means it learns and adapts based on human-provided feedback. If humans provide biased feedback, the AI unknowingly perpetuates and amplifies these biases over time.
For instance, if developers feed biased hiring decisions back into the system as positive outcomes, the AI continues to favour those biased decisions. This cycle can lead to ongoing and systemic bias in recruitment, making it essential to monitor and mitigate human influence on AI systems.
AI hiring bias examples
AI can be biased against certain groups based on race, gender, or disability. Here are some examples:
Sexism
When a company uses an AI tool to screen job applications for a tech role, developers typically train AI tools on previous hiring data from when the company historically favoured men for similar positions. As a result, the AI may prioritize keywords and qualifications that more often appear in male candidates’ resumes. For example, AI might rank applicants higher if their resumes include terms like “technical leadership” or “project management,” which are generally more common in male candidates’ resumes.
Consequently, the AI might overlook qualified female applicants with similar skills but different wording or less traditional experience. This scenario can lead to the company hiring fewer women for tech roles, reinforcing gender imbalances in the industry.
Racial discrimination
Just like AI can show bias based on gender, it can also be biased against people of different races. Due to unconscious biases in the data, AI tools can unintentionally favour certain racial groups over others. Since its introduction, people have criticized AI for racial bias. Some AI models make incorrect decisions or produce inaccurate results for members of underrepresented groups.
For example, AI might incorrectly assess the qualifications of people of underrepresented groups, leading to fewer job interviews for these candidates. This issue can happen if the AI relies on patterns from a predominantly white applicant pool, causing it to overlook other resumes.
Legal and ethical connections
AI hiring bias can cause issues for your company. Beyond the ethical issues, it can also cause problems with Canadian enforcement bodies like the Canadian Human Rights Commission (CHRC) or the Ontario Human Rights Commission (OHRC). With AI evolving quickly, keeping up with the laws can be challenging. HR departments might consider proactively managing the risks of AI hiring tools.
When using AI in hiring, your HR department might want to learn more about the legal and ethical implications of potential bias. Here are a few key points to think about:
- Relevant employment discrimination laws, such as those from the CHRC, OHRC, or the provincial or territorial regulator in your region
- Ethical guidelines for the AI tools they use
- Ensuring responsible AI use and maintaining accountability for any biases that may arise
Strategies to reduce AI bias in recruitment
Implementing the following strategies can help create fair and unbiased recruitment and ensure a more equitable hiring process:
Regularly reviewing and updating training data
Review your training data regularly to ensure it has a range of candidates and accurately represents different groups. Check that the data is free from details about people’s demographics and fix any imbalances favouring certain groups.
Diversifying your AI development team
Different perspectives can help identify and address potential biases that might not be obvious to a more homogenous group. A varied team can provide insights that lead to more equitable AI solutions.
Implementing bias detection and correction tools
Bias correction and detection tools can help you find biased patterns and adjust the AI’s behaviour to promote fairness.
Setting clear ethical guidelines and standards
Setting standards for how AI should handle different scenarios can help address issues related to bias and fairness and maintain consistent and equitable hiring.
Training HR teams on AI bias awareness
The more your HR teams know about AI bias and how it can affect hiring decisions, the better equipped they are to oversee AI tools and ensure they add to a fair hiring process.
Being transparent and open to appeals
Allow candidates to see how automated tools evaluate their applications. Being transparent about these tools and allowing candidates to appeal decisions can help protect against bias. This approach gives candidates a clearer understanding of your hiring process, allowing them to address any concerns or errors.
Adopting fairness-focused AI models
As AI becomes more common, some developers focus on fairness-aware algorithms to tackle biases. These algorithms examine data for issues like discrimination or unfair treatment. When using AI models, ensure you understand how they make decisions and their reasoning. Also, the AI applies rules to avoid biases related to gender or race, which can reduce the risk of unfair outcomes.
Incorporating human oversight
Avoid letting AI handle hiring decisions alone. If you integrate AI into your recruitment process, consider having a team supervise it to spot and address potential issues and prevent AI bias. A diverse team can review and audit the AI’s decision making.
AI offers many advantages, but it also comes with risks. In hiring, biased decisions by AI or humans can harm your company’s reputation and lead to legal issues. Reducing AI bias can promote fairness and help your hiring team make better decisions for your business.