How to Reduce AI Bias in Hiring

Artificial intelligence (AI) has significantly transformed the hiring process, providing recruiting teams with a streamlined approach to acquiring new talent. While AI can enhance decision-making and reduce bias in hiring, it is not immune to the same discriminations as its human creators. 

Therefore, companies face the challenge of addressing and minimizing AI bias in hiring to ensure fair and inclusive hiring practices. In this article from Aniday, we will explore the concept of AI bias, its various forms, and practical strategies to mitigate its effects. Join this blog with Aniday.

What is AI Bias?

Bias in the context of AI refers to the unfair or discriminatory treatment of individuals or groups based on certain characteristics such as race, gender, age, or ethnicity. 

In the hiring, management, and firing processes, numerous biases can manifest, often subtly or unconsciously. These biases may lead to hiring fewer women overall, premature termination of older employees, or hindering job opportunities for individuals belonging to certain protected classes.

While some companies have implemented AI in their talent acquisition functions to make decisions without considering these protected classes, AI itself does not work in isolation from biases. 

The effectiveness of AI depends on the data set used to train it, and any errors or inherent biases within the data will be reflected in the AI's output. These biases are not emotional but rather programming errors that result in unintended and unwanted outcomes.

Data May Reflect Hidden Societal Biases

One significant source of AI bias in hiring arises from the data used to train AI models. For instance, searching for the word "beautiful" on Google predominantly displays images of white women. This bias is not due to any racial preference embedded in the search engine's algorithms but rather a reflection of the training data, which contained an overrepresentation of these specific images created by people who themselves held biased preferences.

Algorithms Can Influence Their Own Data

Another aspect contributing to AI bias in hiring is the ability of algorithms to influence the data they receive. Positive feedback loops can occur when certain types of content rise to the forefront based on user interactions. This amplifies the visibility and prominence of specific data, further reinforcing the biases already present in the AI's training set. Consequently, AI systems can perpetuate and magnify their own biases.

People Can Manipulate Training Sets

Bad actors can intentionally corrupt training data, leading to biased outcomes. An infamous example is Microsoft's AI chatbot "Tay," released on Twitter in 2016. Within hours, people taught Tay to post inflammatory and offensive content, resulting in violent, racist, and sexist misinformation. 

To counteract this issue, open-source or publicly available AI models often require continuous monitoring and intervention to prevent intentional manipulation of training sets.

Unbalanced Data Affects the Output

A common saying among data scientists is "garbage in, garbage out," emphasizing that flawed input data yields flawed outputs. If programmers inadvertently train AI on information that does not accurately represent real-life distributions, the AI's predictions and decisions may be distorted. 

For instance, facial recognition software may struggle with recognizing faces of individuals with darker skin tones if the original training set primarily consisted of images of white people.

Additionally, unbalanced data sets can introduce unintentional associations between features and predictions or hidden categories. Suppose the training data does not include samples of female truck drivers. In that case, the AI may automatically link the male and truck driver categories due to the lack of explicit examples featuring women. 

As a result, the AI creates a bias against hiring women as truck drivers based on previous patterns, despite it being an erroneous conclusion.

Why AI Bias is a Challenge in Hiring

Talent acquisition teams are dedicated to ensuring fairness throughout the hiring process. However, the increasing workload and influx of job applications have propelled many teams to turn to AI and automation software for assistance in managing large volumes of resumes and applications. 

Prior to the COVID-19 pandemic, the average job opening received 250 applications, but today, some entry-level positions receive thousands of candidates. AI programs are often employed to aid in predicting job performance, assessing video interviews, and making hiring decisions.

Nevertheless, applicants have reported instances where AI software rejected their applications based on factors such as foreign-sounding names or specific words included in their resumes. Although names and word choices are not protected classes, they can serve as proxies for race, gender, or age. 

For example, Amazon had to discard a recruiting tool in 2018 that automatically penalized resumes containing the word "women's," inadvertently disadvantaged candidates with backgrounds related to women's studies. This incident is particularly striking considering companies in the top quartile for gender diversity are 25% more likely to generate above-average profits compared to those in the lowest quartile.

Reducing the Effects of AI Bias in Hiring

To address and mitigate AI bias in the hiring process, talent acquisition teams can adopt several best practices:

Double-check AI Predictions

It is crucial not to rely solely on AI predictions without verification. While algorithms strive to make accurate forecasts, they can still produce errors, including biases. Therefore, someone within the team should review AI suggestions and make informed decisions about accepting, vetoing, or further examining them. Taking human oversight into account helps ensure fair and unbiased judgment.

Report Biases Immediately

When recruiting teams identify biases in AI software, it is essential to report these issues promptly. Programmers can then work on patching the AI to correct the biases and improve its fairness. Timely reporting and collaboration between recruiters and programmers are vital in addressing and rectifying bias-related problems.

Seek Transparency

Programmers play a critical role in providing transparency regarding the algorithms used in AI systems. Users should have access to information about the types of data the software was trained on, even if interpreting complex AI models poses challenges due to hidden layers. Therefore, talent acquisition teams should prioritize selecting and implementing AI software that offers transparency regarding its training data and processes.

Get Different Perspectives

Including professionals with expertise in sociology or psychology on the team can significantly contribute to identifying biases present in training sets and offering valuable insights on correcting them. These experts are well-versed in recognizing societal biases and can provide guidance on ensuring fairness and inclusivity in the AI recruiting process.

Ask Questions

Before releasing new AI software to the public, programmers should perform thorough checks to verify the data's alignment with overall goals. They should consider whether the AI includes the right features, whether the sample size is sufficient, and whether any biases were inadvertently introduced during the training process. 

Although a standardized process for vetting AI software is yet to emerge, programmers must diligently double-check their work and address potential bias concerns.

Improve Diversity, Equity, and Inclusion

Nearly 50% of recruiters have observed an increase in job seekers' inquiries about diversity and inclusion. To create a fair hiring process, companies should strive to foster a culture of diversity, equity, and inclusion (DEI) beyond solely improving AI usage. 

For instance, eliminating discriminatory language from job listings and actively promoting diversity initiatives within the organization can help attract diverse talent and reduce bias in hiring practices.

Look to Create Balance

It is crucial to recognize that AI is merely a tool that operates based on its design and training. To minimize bias, recruiting teams should critically evaluate any software employed in the hiring process. Ultimately, human decision-makers should retain the final authority as they are better positioned to consider the broader context and individual circumstances. Treating candidates as human beings requires empathy and understanding, which AI alone cannot fully replicate.

In Summary

Artificial intelligence has transformed the recruitment process, streamlining and expediting talent acquisition. Nonetheless, the issue of AI bias poses a substantial challenge that requires attention to guarantee equitable and inclusive hiring practices. By implementing the strategies outlined in this article from Aniday, talent acquisition teams can reduce the effects of AI bias in hiring. 

From double-checking AI predictions to seeking transparency, diverse perspectives, and fostering a culture of diversity, equity, and inclusion, organizations can work towards creating an unbiased and equitable hiring environment. Remember, when striving to hire human beings, they should be treated as such. Aniday hopes this blog helpful for you.