Artificial intelligence (AI) has become an essential resource in numerous industries. Recruiters use it to assist with resume screening, candidate testing, video interviews, and more. While it can improve workflows to meet faster deadlines, AI can also screen neurodiverse candidates out and enforce existing workplace inequalities if left unchecked. Tech experts and recruiters can address the issue by learning more about it and its multiple solutions.

How Bias Can Affect AI Recruiting Tools

Neurodiverse applicants could have a harder time getting jobs in companies that rely on AI recruiting programs. If recruiters and tech experts do not know what to look for, the tools can present multiple challenges to equitable hiring efforts.

Video Analysis May Misinterpret Communication Skills

AI-powered video interview platforms look for specific things in each applicant. The algorithms evaluate tone of voice, pauses in each answer, and facial expressions to determine a person’s social skills. Neurodivergent candidates may have atypical speech patterns or less eye contact, depending on how each person comfortably communicates.

If the AI’s training data didn’t include those differences, it may screen candidates out by automatically giving them lower scores. Differing communication skills do not prevent people from being excellent employees. However, computer programs will not understand that without people adjusting how AI platforms evaluate candidates.

Bias Might Appear During Resume Screenings

Experts know that diverse workplaces have improved retention rates and better internal cultures, so recruitment teams may use AI tools to achieve more diverse hiring practices. However, the resources could unknowingly reinforce the same workplace demographics if no one monitors them.

Researchers found that AI resume screening tools favor white-male associated names 85.1% of the time when reviewing applications. Other factors related to neurodiverse candidates — like resume gaps or nontraditional career paths — could reinforce existing biases if the people using the tools do not identify the issues.

Personality Tests Could Reject Different Cognitive Styles

Some AI recruiting platforms include personality tests for each applicant. The algorithm uses the answers to identify personality traits or cognitive styles that fit the role, team, or company culture. Research shows that neurotypical participants rate their test experience more positively than neurodiverse candidates, who may feel pressured to mask their autistic traits to reach a human recruiter successfully. Answers that differ from neurotypical cognitive styles could filter neurodiverse candidates out of potential in-person interviews.

Why Neurodiversity Is Important in Workplaces

Companies hiring one of the 5.5 million American adults with autism could miss out on the strengths their neurodiversity brings to the table.

People with autism can have superior focus and memory compared to neurotypical individuals. Their productivity may be higher because they can focus longer or spot patterns that current employees do not. They could also point out other lapses in inclusion, like employee training opportunities formatted for people without learning differences.

Ways to Prevent Bias in AI Recruiting Programs

People interested in fixing AI recruiting tools or developing them with more inclusive programming can remove bias in numerous ways. Implementing simple strategies may improve the hiring process for neurodiverse people, who have much to contribute to growing organizations.

1. Double-Check the Training Data

Recruiters should check the data their programs are trained on. They may need to contact the manufacturers for more details. The datasets should have included a wide range of fictional applicants with experiences, backgrounds, and neurodiverse profiles that differed from each other. If the algorithm did not train on a broad dataset, it may unfairly evaluate candidates with different abilities or communication styles.

2. Choose Transparent AI Models

Transparent AI models are crucial for ensuring diverse hiring practices. Tech experts can prioritize algorithms that show their decision-making processes so future users can pinpoint things that need to change. Transparency is key to eliminating anti-neurodiversity issues because everyone — from coders to people new to AI platforms — can understand how the algorithm evaluated candidates.

3. Conduct Recurring Bias Audits

Teams can check their AI systems for bias with recurring audits. They should test the tools and evaluate the outcomes for prejudice or discrimination. Technical audits during development or after application are equally helpful. If people routinely evaluate the AI’s fairness, underrepresented applicants have a better chance of undergoing equal hiring practices.

4. Create Programs With Neurodiverse Programmers

Leadership teams making AI recruiting software should set goals to hire neurodiverse employees. Deloitte experts agree that people with different cognitive abilities can spot ideas or opportunities that neurotypical minds might miss. If they are working on AI programming, they may recognize red flags that would generate biased recommendations before they can affect workplaces across industries.

5. Integrate Human Supervision Throughout the Hiring Process

AI programs have strict processing standards. They do not have the essential interpersonal experiences that recruiters do. Someone with programming skills can still fail an assessment test due to nervousness or barriers that work against neurodiverse people. An AI tool might rule that individual out based on their test score, but a recruiter could recognize the applicant’s experience and move them onto the next step in the hiring process.

They can also double-check the AI’s work with their workflow standards. If a hiring team is trying to recruit people who do not fit previous candidate molds, they may not remember to update their AI’s evaluation methods unless they review the results in real time.

AI Can Support Diverse Recruiting Efforts

Inclusive AI recruiting is possible if people view it as a supportive tool rather than a replacement for traditional recruiting methods. If programmers and hiring experts monitor how the algorithms make recommendations, they can quickly spot issues that might prevent neurodiverse candidates from having a fair evaluation.