Addressing Bias in AI: How to Ensure Your Artificial Intelligence Tools Produce Unbiased Outputs

3 min read

Here’s what you need to know about bias in artificial intelligence.

Artificial intelligence tools are capable of some truly amazing things. They can analyze complex data sets, make decisions, and even create predictions and hypotheses around potential future events. But despite AI’s meteoric rise in recent years, risks and flaws remain. AI systems are built by humans, after all, and humans are fallible. Perhaps one of the most significant risks involved in overreliance on AI is the risk of bias.

According to Levity AI, an email AI company, AI bias “refers to the tendency of algorithms to reflect human biases.”

AI bias arises when artificial intelligence systems are trained on or used in conjunction with biased data. One prominent example of bias in AI is Amazon’s problematic hiring algorithm for software development and other technical jobs. For a short period of time, Amazon used artificial intelligence to rate job applicants from one to five stars. However, Amazon’s algorithm relied on data gathered over the past decade, which showed that most software development resumes at the company were from men. Therefore, the algorithm concluded that male applicants are preferred for software development roles, and it began penalizing female applicants. This AI-assisted hiring effort was disbanded in 2017 after Amazon’s management lost faith in the project.

So how can you avoid Amazon’s missteps and ensure your AI outputs aren’t reflecting human biases? Here’s what you need to know about using AI in an unbiased way.

Diversify Your Datasets

When you’re using an AI tool to make decisions, it’s important that you share all of your available data with that tool. AI tools make decisions based on the data you provide; if you’re giving your AI tools incomplete or biased datasets, then you’ll get incomplete or biased outputs in return.

If you’re using an AI tool to help sort job candidates, for instance, you’ll want to be mindful of the data you train the AI on. Look for opportunities to introduce diversity into your training data and AI inputs. If you have visible minority employees, make sure they’re represented in your training data; your AI hiring tool will then be able to make less biased decisions.

Monitor Your AI Tools for Bias

When leveraging artificial intelligence tools, it’s important that you keep a vigilant eye on inputs and outputs. Quality assurance matters, especially when you’re just starting to use an AI tool. By continually reviewing your AI’s inputs and outputs, you can identify bias when it happens and address the problem head-on.

Empower Users to Correct Your AI

Sometimes, the best way to train an AI is to manually intervene when it makes a mistake or runs into a wall. If your artificial intelligence systems continue making biased decisions, it could be a sign that you need your employees to manually tell the AI that it’s being biased. This Human-in-the-Loop methodology provides the AI system with continuous feedback that enables the AI to improve. Some artificial intelligence tools can learn over time, which means you and your staff can train them to be less biased.

Bias in AI is an emerging and complex problem with a number of implications not only for businesses, but also for society as a whole. While AI systems are increasingly capable of analyzing complex datasets and making accurate decisions in short amounts of time, they still run the risk of disadvantaging minority groups. By keeping a mindful eye on your datasets and correcting your AI systems when they make biased decisions, you can empower your artificial intelligence tools to learn over time and become less and less biased every day.

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