The Hidden Bias in AI: Can Technology Discriminate Against You?

Many business owners trust AI tools to make fair and efficient decisions when it comes to screening job applicants, approving loans, writing copy for company websites, responding to customers’ questions, and picking which employees get a raise or promotion. But here’s the shocking truth—if you use an AI-powered assistant, it might actually be more prejudiced than a real person!

AI is often viewed as neutral and objective. After all, it’s not human. It doesn’t have feelings for or against any particular group of people. So how can a robot be prejudiced?

The truth is, bias in AI is a growing problem that can lead to serious real-world consequences, including discrimination in hiring, unfair financial decisions, skewed data, and even wrongful arrests.

But how exactly does AI become biased, and what can we do about it? Let’s find out…

What is AI bias?

As we learned in a previous article, algorithms based on data determine how an AI tool responds to a human’s prompt. AI bias occurs when an algorithm uses data that favors one group over another in ways that are unfair or discriminatory.

Here are three ways this can happen:

1. The data used to train the AI is biased.

AI learns from existing data, so if the data contains biased information, the AI will pick that up. For example, if past hiring records show a preference for white candidates, the AI will recommend white job candidates over applicants from other backgrounds.

2. The AI’s algorithms are flawed.

Let’s say Boris and Jason are each trying to get a business loan from a financial institution that uses an AI system to determine eligibility. This AI’s algorithms are designed to weigh certain financial factors (such as owning stocks) much more heavily than others (such as owning works of art). Imagine that Jason’s art collection is far more valuable than Boris’s stock portfolio. However, because the AI system favors stocks, Boris will get approved for a loan, while Jason will not.

3. The AI system’s testing was limited.

AI systems need to be tested across a wide range of people and scenarios in order to produce reliable, accurate, and consistent results. Let’s take healthcare as an example. Women who are having a heart attack are known to experience different symptoms than men. So if an AI diagnostic tool was tested primarily on male patients, it will fail when it is used on female patients.

Real-world consequences of AI bias

AI bias doesn’t just exist in theory. It is happening right now in ways that affect real people. Here are three cases where AI bias led to major issues:

1. AI bias in hiring

In 2014, Amazon started using an AI recruiting tool to automate its hiring process. The idea was simple—feed resumes into the system from past job applicants and let the AI learn which candidates were the best. But there was a problem—the AI started favoring male applicants over female ones.

How did this happen? The AI was trained on past hiring data from a male-dominated tech industry. It noticed that men were hired more often, so it assumed that being male was a factor in success. As a result, it downgraded resumes that included words like “women’s” (such as “women’s chess club”) and penalized graduates from all-women’s colleges. Amazon had to scrap the tool entirely.

2. AI bias in banking and finance

AI is widely used in finance to assess creditworthiness and approve loans. But studies have shown that AI-driven loan approval systems sometimes give lower credit scores to minorities, even when their jobs and financial histories are similar to those of white applicants.

3. AI bias in policing

Many law enforcement agencies are now using AI-driven facial recognition software to identify crime suspects. But these systems have higher error rates for people of color.

One shocking example occurred in 2020 when Robert Julian-Borchak Williams, a Black man from Detroit, was arrested after an AI-powered facial recognition system incorrectly matched his face to security footage of a shoplifter. He was detained for thirty hours before the police realized their mistake.

Is there a way to make AI fairer?

AI bias is a serious issue, but there are ways to reduce its impact:

1. Use diverse data sets

AI systems should be trained on data that includes people from many different racial/ethnic backgrounds, genders, ages, and income levels. More diverse data leads to fairer AI decisions.

2. Audit AI systems regularly

Business owners can’t just sit back and “let the robots do their thing”. Human employees need to test and review their AI systems on a regular basis, especially if they notice something doesn’t look right (e.g., all of the job candidates the AI recommends are in the same narrow age range). Testing AI systems means running simulations and analyzing the results to see if certain groups are being unfairly disadvantaged.

3. Increase transparency in AI decisions

AI decision-making should be easy to explain to the people who are affected by it. If an AI doesn’t recommend an employee for a promotion, that person should be able to understand the reasons why. Transparency means including options for businesses and individuals to challenge unfair outcomes.

4. Human oversight is key

AI is meant to assist human decision-making, not replace it. Having a real person review AI-generated decisions can help catch and correct bias before it causes harm.

AI can save you a lot of time and effort, but it isn’t perfect. Just like human employees, AI systems need oversight, training, and regular checks to ensure they are making fair decisions. If left unchecked, biased AI systems could reinforce discrimination rather than eliminate it.

As businesses and individuals, we must be aware of AI bias and demand fairness in AI-driven decisions—because that computerized “employee” might not be as objective as you think.