Artificial Intelligence to Eliminate Hiring Bias

Eliminating Bias from Hiring Using Artificial Intelligence

The use of artificial intelligence (AI) allows technology to automate the hiring process. Recent progress in the space, such as advances in big data and machine learning, allow AI to recruit, background check, and even predict the compatibility and success rate of applicants. However, employers have to consider whether the technology is reliable enough to improve upon the current methods. Some users are hesitant, fearing it will reinforce bias and profiling while rejecting quality applicants. This report explores how using AI in hiring can reduce bias and recruit a wide variety of qualified candidates. It also explains the importance of a full and diverse dataset for training AI models. 

What is Bias in Hiring?

Unconscious bias occurs intuitively before we are even aware of it. It is a snap judgment that happens automatically and often unintentionally, formed from our surroundings, experiences, and upbringings. It affects our opinions and attitudes toward others. Unconscious bias in humans can make hiring practices discriminatory.    

The most common way to review applicants before an interview is for a recruiter to view their résumé. Research shows that this typical hiring process often leads to unconscious biases toward minorities, women, and older workers. Unfortunately, these quick judgments about an applicant can ruin their chances of getting the job, even if they are perfectly qualified for the position. For example, information such as an applicant’s picture, name, or hometown can subconsciously influence a recruiter’s decision. 

In fact, it is common for businesses to conduct assessments using biased hiring tools. According to the U.S. Equal Employment Opportunity Commission, assessments such as cognitive tests, physical fitness assessments, personality tests, credit checks, and medical examinations can disproportionately exclude applicants based on age, race, physical disability, nationality, or religion. These tests can be used by employers to intentionally or unintentionally discriminate against specific classes of applicants who may otherwise be well qualified for employment. Although federal regulations bar the deliberately discriminatory use of such assessments, these unfair hiring practices often pass under the radar. 

How AI Can Reduce Bias and Improve the Hiring Process

In 2019, a Gartner survey found that roughly 37% of businesses have used AI when hiring and 34% of recruiters feel AI will be crucial in shaping the future of recruitment processes, as reported by a London School of Economics team. 

Today, many businesses struggle to hire a diverse workforce. A well-trained, data-centric model can effectively eliminate human bias. Although AI mimics and potentially amplifies human prejudice, when used correctly, it can help to eliminate unconscious bias and make data-driven decisions. AI tools use data points to source and evaluate candidates. The models can predict the best candidates without using any bias or assumptions. AI eliminates these biases, helping to ensure that candidates are evaluated purely on merit.  

Time-constrained human recruiters with biased algorithms inevitably shrink the pipeline from the start. AI can initially evaluate the full pool of applicants and lead to greater success. It is common for only a small percentage out of millions of applicants to actually get reviewed. Truly automated recruiting is the only way to narrow the field without bias and access a wider variety of candidates.   

Other benefits of using AI in hiring include cost reductions, more effective job postings, and improved candidate sourcing, screening, digital interviews, background checks, and reference follow-ups. However, a factor against the use of AI is the lack of human emotion and personal touch. It is important to note that AI should not completely replace human recruiters; instead, it should be used to streamline the early stages of the hiring process. 

Best Practices for Unbiased Data

AI simulates human behavior; therefore, the datasets for an AI model must be unbiased from the beginning. If the training data is filled with past biases, the model will reflect that and bias will continue.    

Training an AI solution solely on a company’s historical data can be problematic. In 2014, Amazon developers created a hiring algorithm that used AI to identify top talent. However, after testing it was found that the solution discriminated against women applying for technical jobs due to pre-existing bias from the training data.  

The solution isn’t simple. Identifying gaps in a dataset can be challenging, and sourcing difficult data can be a daunting task. This is especially true with respect to minority candidates, who have already been adversely affected by this technology. Companies like Innodata approach this problem by building genuine community relationships, incorporating minorities in every step of the process, and explaining how they’re working to eliminate the bias within human datasets.     

Current AI hiring tools may be flawed, but these flaws can be addressed. Recently, AI developers such as OpenAI Charter and Future of Life created ethical and fair AI design standards that benefit everyone. These standards include solutions for research, ethics and values, and long-term AI issues. One of their key principles requires an AI model to allow auditing and the removal of any bias found in the data. It’s important to address bias before it corrupts your model.

Recruiters should consider scalability, consistency, and quality data when looking for an AI hiring solution. AI should save money, time, and resources. It must be a long-term solution that constantly grows and learns as data progresses. To build a diverse team, it is critical to look at recruitment AI solutions that create and integrate new, objective and predictive data.

Identify and Correct Bias

If we cannot correct implicit human bias, we should strive to identify and correct bias in hiring using AI. While removing bias in AI can be difficult, it is much more achievable than eliminating bias in humans. The goal is not to have AI replace human recruiters entirely, but to make the hiring process easier, more efficient, and more equitable. Ultimately, AI has the potential to create a high-quality, diverse workforce. It is up to businesses to harness this power effectively and use it responsibly. 

To learn more, see our post – Best Practices for Training a Machine Learning Model

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(NASDAQ: INOD) Innodata is a global data engineering company delivering the promise of AI to many of the world’s most prestigious companies. We provide AI-enabled software platforms and managed services for AI data collection/annotation, AI digital transformation, and industry-specific business processes. Our low-code Innodata AI technology platform is at the core of our offerings. In every relationship, we honor our 30+ year legacy delivering the highest quality data and outstanding service to our customers.