AI can sift through huge numbers of online profiles to identify passive candidates who may be open to new roles. However, it’s important to have a team of human recruiters overseeing this process to ensure the algorithms aren’t replicating or amplifying existing biases.
Unfortunately, the engineers behind the machine learning used by some recruitment tools can transmit their unconscious biases into the algorithm (Miasato and Silva, 2020). This results in discrimination.
AI in Recruitment
AI technology can reduce conscious and unconscious bias throughout the recruitment process. AI recruiting software can create unbiased job descriptions and flag exclusionary language, allowing teams to recruit more diverse candidates.
When an AI tool identifies patterns in resume data, it can highlight specific candidates that might be overlooked by recruiters. Some tools can also assess genuine interest, candidate motivations and predicted tenure to make recommendations that help to improve the hiring process.
However, human bias can still linger in some recruitment tools. For example, a facial recognition program that was used by Amazon was found to be biased against women and white people. This was due to a lack of diversity in the data set that was used for training.
It is important that all recruiters understand the role of AI and how it can affect their recruitment. This can be done by ensuring that all team members are trained to use the AI and reviewing the data output for any biases that might exist. In addition, a data protection strategy that is consistent with data protection regulations should be in place for all AI tools.
Bias Detection in Hiring
Unconscious biases are difficult to recognize in the hiring process and can lead to costly mistakes. Even when your company has diverse interview panels and uses standardized screening questions, unconscious bias can still play a role in deciding who gets hired.
Whether it’s the candidate’s name, age or address, each factor can trigger a hiring manager’s gut reaction and influence their decision-making. This can result in a bad hire that ends up costing the organization more than it would have had it gone with someone more qualified.
When it comes to using AI for recruitment, there are a few strategies you can use to help minimize bias. For example, you can use blind assessments that remove names during the initial screening stage and focus solely on qualifications such as work samples and skills tests. This helps to set an objective standard and reduce the impact of unconscious bias. You can also implement a structured interview process that allows candidates to meet with different managers from different parts of the business. This can reduce the impact of in-group bias and help to identify candidates with the best cultural fit.
Inclusive Hiring Practices
The interview process is where recruiting can be most impacted by unconscious bias. Following modern inclusive hiring best practices can help make your organization more welcoming and able to attract top talent from diverse backgrounds.
Inclusive hiring practices should start with transparent job descriptions that avoid coded language and focus on the skills required for the role rather than irrelevant criteria that could screen out candidates. It is also important to use structured interviews with consistent questions for each candidate and to remove identifying information like name and gender from resumes before assessment so that assessments are solely based on skill and experience. Finally, providing regular unconscious bias training for interviewers can help mitigate the impact of biases on their ability to assess and evaluate candidates.
Inclusive hiring is about more than quotas and policies; it requires a cultural shift in the way that your company views its employees. Changing your culture is a journey and takes time, but you can set a strong foundation with the right tools and resources. HRbrain has a comprehensive suite of AI solutions that can reinforce equity throughout the recruitment and selection process.
Automated Resume Screening
Many recruiters are overwhelmed by the number of applicants they receive for open roles. Automating resume screening can help recruiters manage this process more efficiently by identifying and evaluating candidates based on their experience, skills, and education. This can save time by removing the need to manually read and evaluate resumes, thereby reducing the chance of unconscious bias and speeding up the recruitment process.
However, automated resume assessment tools can have their limitations too. For example, if the tool prioritizes pedigree when assessing job applications, this may lead to it favoring candidates from top tertiary institutions over those from lower-income households, thus introducing bias.
It is important to ensure the inputs into your AI recruiting software are watertight so that it can assess a candidate objectively. It also helps to include in the job description the key criteria that you expect a candidate to meet, such as soft skills or a certain level of expertise. This can help the AI select and rank candidates based on their ability to perform the role effectively, eliminating potential biases during the initial assessment stage.
AI-Driven Job Descriptions
AI tools allow recruiters to streamline the creation of job descriptions, focusing on key inputs such as responsibilities and required skills. This reduces time spent on creating a compelling job post and ensures consistency across all listings. Modern AI job description generators also offer customizable features to enable recruiters to tailor the tone and length of a description to align with their brand’s voice and culture.
Additionally, AI tools help recruiters optimize job descriptions for search engine optimization (SEO) by identifying the most effective keywords for a particular role and industry. This helps boost visibility for a job posting and improves the likelihood of qualified candidates finding it through organic searches. Some AI recruitment tools also incorporate inclusivity checks to identify tuyen dung da nang problematic words that may discourage underrepresented groups from applying.
While AI can reduce biases in the initial screening process, final hiring decisions should still be based on human judgment. Additionally, relying too heavily on AI tools can make the hiring process seem impersonal and turn off candidates. A mix of AI for repetitive tasks and a personal touch from recruiters can keep the candidate experience friendly and engaging.
