Is There Hidden Bias in AI-Driven Hiring? Here’s the Full Answer

Why Is AI Used in Hiring?

AI is becoming increasingly popular and employers are looking for ways to implement it in their hiring processes.

According to LinkedIn’s annual survey, 62% of recruiters are optimistic about how AI can revolutionize recruitment. So, it’s not surprising that the use of AI in recruiting is expected to spike – eight in ten companies report that they plan to implement it in their hiring processes.

At the same time, job seekers are skeptical about the use of AI in hiring. Forbes found that 49% of surveyed adults in the US believe that AI is more biased than human recruiters.

But is there any truth to fearing AI in the job market or is it all based on misconceptions?

First, let’s talk about why AI is so popular in hiring.

Employers and recruiters are always looking for ways to streamline the hiring process. Statistically, a single corporate job opening gets over 250 applications. Hiring teams are swarmed by all the resumes they need to sort through.

Here’s what AI brings to the hiring process:

• Efficiency. Virtually any AI tool can review thousands of resumes in minutes, which speeds up the hiring process. This means hiring managers can process applications, reach out to job seekers, and fill vacant positions much faster.
• Data analysis. AI is perfect for identifying patterns in large datasets. It can spot qualified candidates who might be easily overlooked by manual screening methods and automatically reject candidates who don’t meet the basic requirements for the role.
• Bias reduction. Ironically, AI can minimize hiring biases, so long as it’s used correctly. If it’s programmed to focus solely on relevant qualifications and work experience, other factors that might influence recruiters can be ignored. (E.g.: name, age, gender, etc.)

However, AI isn't perfect. If it’s trained on biased data or with flawed algorithms, it can perpetuate existing biases, so we can’t blame job seekers for being concerned.

AI will keep affecting talent acquisition, so it’s crucial to understand what it can and can’t do if you want to navigate the job market.

What Is an ATS?

When we talk about AI in hiring, the first thing that comes to mind is applicant tracking systems (ATS) – a type of specialized software that's a staple in modern recruitment. We’re not exaggerating either; over 98% of Fortune 500 companies use some form of ATS.

But most ATS aren't actually AI.

Traditional ATS work on a simple principle: they store, sort, and analyze data from job applications. They can search for keywords, rank candidates based on predefined criteria, and help manage the hiring workflow. As far as automation goes, some HR professionals say that it's an analog process.

But the hiring landscape is changing. AI-powered ATS systems are gaining traction and blending the organizational aspects of traditional ATS with the advanced capabilities of AI.

Here are a few ways these new systems are shaking things up:

• Smart screening. AI can go beyond simple keyword matching. It can understand context, identify relevant experience even if it's phrased differently, overcome any ATS formatting mistakes the candidate might have made, and evaluate the overall quality of a candidate's background.
• Predictive analytics. Analyzing successful hires can enable AI to predict which candidates are likely to perform well in a role.
• Chatbots. Similar to how they’re used in customer service, AI-powered chatbots can interact with candidates, answer questions, and even conduct initial screenings.
• Bias detection. Advanced AI can help flag potentially biased language in job descriptions and highlight diverse candidates who might otherwise be overlooked.

While traditional ATS are still the norm, the shift towards AI-enhanced systems is picking up speed. As these tools become more sophisticated, they're likely to play an increasingly significant role in how companies find and evaluate talent.

Why Does Bias Appear in AI-Driven Hiring?

Even though it’s supposed to be objective, AI-driven hiring isn't immune to bias. 

AI systems learn from the data we feed them. If that data reflects historical hiring patterns that favor certain demographics, the AI might stick to these preferences unintentionally.

At the same time, AI can amplify existing biases. If the AI notices a slight correlation between a certain characteristic and job performance, even if it's a coincidence, it might overemphasize this in future hiring decisions.

For example, if a tech company's past software engineer hires were predominantly young men from top-tier universities, an AI trained on this data might prioritize similar candidates, even if that wasn't the hiring team’s intention.

The teams that are developing these AI tools also play a crucial role. The tech industry, particularly in AI engineering, isn’t always the most diverse. Teams that work on AI are typically made up of the same ethnic, racial, age, or gender demographic, like white men between 24 and 34 years old. Having such a homogenous team can lead to blind spots since developers might not consider things outside their own experiences, which unintentionally adds their biases to the system.

Moreover, the choices developers make when designing AI hiring systems can introduce bias. Deciding which factors the AI should consider, how to weigh different qualifications, or what constitutes a "good" candidate all involve human judgment and require nuance. No matter how well-intentioned they might be, the AI can reflect their biases or personal preferences.

Let’s say the AI system is designed by a team of developers with no employment gaps. In turn, the AI might automatically downgrade candidates with employment gaps without considering the nuance. A candidate who took two years off to earn an advanced degree or a stay-at-home parent who briefly stepped back to care for their family might be unfairly penalized, despite being great candidates for the role they’ve applied for.

Can You Encounter Bias Before Applying?

Yes, you might face AI bias before you start your job hunt. It all comes down to how job ads are distributed online.

Many job boards and social media platforms use algorithms to decide who sees which job postings. These algorithms, like the AI in hiring processes, can unintentionally reinforce stereotypes and biases.

Some examples include:

• Showing more customer-facing roles to women, such as receptionist and retail jobs, and more manual labor jobs to men. This can be based on historical data of who typically applies for these positions.
• Younger job seekers might see more entry-level positions, while older job seekers get fewer tech job ads, regardless of their skills.
• Your IP address might influence the type of job ads you see, with users from some areas receiving fewer advertisements for well-paying roles.

This means that depending on your demographic profile, you might not even see some job opportunities.

8 Types of Bias in AI-Driven Hiring

Now that we’ve covered how bias in AI-driven hiring works, let’s look at some concrete examples.

Types of bias can be based on:

1. Gender. AI can discriminate based on perceived gender. One famous example of this is Amazon's 2014 experimental hiring tool that showed clear bias against women, penalizing resumes that included words like "women's" and downgrading graduates of women's colleges.
2. Race or ethnicity. Based on historical data, AI could perpetuate racial or ethnic biases by favoring candidates from backgrounds similar to past hires. Unknowingly, this could create a loop of candidates from the same race or ethnicity for certain roles.
3. Socioeconomic status. Depending on how it’s programmed, AI might prefer candidates from prestigious universities or high-profile companies and disadvantage qualified applicants from less privileged backgrounds.
4. Age. Historical data bias can also lead to AI tools discriminating against older job seekers by preferring younger candidates or recent graduates.
5. Disability. If you’re navigating the job market with a disability, you might have employment gaps or an unconventional career path that AI might misinterpret.
6. Language. Similar to how hiring managers might be biased towards non-English names, AI can also rank these candidates lower despite strong qualifications.
7. Education. If the AI is taught to overemphasize traditional credentials, this can significantly disadvantage self-taught professionals or those with unconventional educational backgrounds.
8. Appearance. With video and phone interviews recently becoming popular, AI might show a preference based on background settings, lighting, or facial features.

Each of these biases in AI-driven hiring stems from flaws in the AI's training data or algorithm design. These are just reflecting, and potentially amplifying, biases that were already there.

Recognizing these potential biases is a crucial first step for employers and job seekers to ensure fairness in an increasingly AI-driven job market.

6 Ways You Can Use AI to Boost Your Job Application

By now, AI might look like it’s challenging your job search, but that’s not entirely the case.

With the right approach, you can use any AI tool to enhance your job application and increase your chances of landing that dream job!

Some examples of what AI can help you do include:

1. Create a standout resume. Start by inputting your information into a professional resume builder. Then, you can either rely on the intuitive software and AI-generated tips by Novoresume or use a generative AI tool like ChatGPT to help expand your resume’s content.
2. Showcase your achievements. Instead of just repeating the same day-to-day tasks, show the highlights of your career. You can use AI to help phrase your achievements in a way that connects them to the job you’re applying to and impresses the hiring manager.
3. Write a compelling cover letter. AI tools can help you draft a cover letter that you can easily expand on. Simply input key details about the job and your qualifications, and you’re good to go.
4. Tailor your application to each job. Your resume should always be tailored to the job. So, give your AI tool of choice the job description and your current resume, and it'll pinpoint exactly what you need to change. This makes it easy to tailor your resume when you’re applying for different positions.
5. Review your resume. AI can give your resume a fresh take if you haven’t updated it in a while. Upload your resume to an AI tool, and it'll suggest updates to make it more current and competitive.
6. Help you prepare for job interviews. You can use AI if you need to prepare for an interview, too. It can help you prepare for the most common questions, offer tips for managing interview stress, and even simulate mock interviews.

BONUS: Bias in AI-Driven Hiring Infographic

FAQs About Hidden Bias in AI-Driven Hiring

Are you still wondering something about the hidden bias in AI-driven hiring? Check out the answers to some of the most frequently asked questions below:

Key Takeaways

And that's a wrap on our guide to hidden bias in AI-driven hiring! You're now ready to tackle your job search with this knowledge under your belt.

But before you go, let's quickly recap the main points:

• AI is increasingly common in hiring, with 62% of recruiters optimistic about its potential, but 49% of job seekers worried about bias.
• While AI can streamline hiring processes, it can also perpetuate human biases if it’s not properly designed or implemented.
• Bias in AI hiring can stem from different sources, including gender, race, age, and even socioeconomic status. Recognizing these can help you perfect your job application strategy to get past them.
• You might encounter AI bias before you even apply since job ads can be distributed according to algorithms. Consider broadening your job search so you find more opportunities.
• Despite the potential biases, you can use AI to your advantage in creating resumes, writing cover letters, and preparing for job interviews.
• Using a professional resume builder and an ATS-ready resume template can increase your chances of making it past AI screening tools and into the hands of a hiring manager.