What Is AI Bias & How To Prevent It

Many believe that artificial intelligence, better known as AI, is critically important to the the future of tech.

AI integrations have picked up over the past few years and soon will have significant leverage on some of the most vital services and solutions we use daily.

This is why it’s crucial your business becomes aware of the subliminal human bias currently being passed onto AI-driven tech and implements effective strategies to end AI bias before it begins.

What is AI bias and how did it begin?

According to TechTarget, “Machine learning bias, also sometimes called algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process.”

AI biases can manifest themselves in many forms: from gender prejudice to age discrimination to even racial bias.

Machine learning is indicative of the humans who create and develop AI technology.

If these algorithms and software are amazing at crunching numbers and analyzing data are asked to mimic and replace humans while uncovering patterns and the needs-based on outcomes, it only makes sense that they’d inadvertently adopt the unconscious biases of their creators.

So far, several studies show that Tech companies must be mindful and catch these biases before integrating AI and other ML-powered devices because of the potential prejudicial consequences.

Unfortunately, AI bias, if not controlled, will only add to the widespread diversity issues the entire Tech industry is facing.

AI Bias Examples

1. Microsoft AI Bias example

In 2016, Microsoft learned firsthand why it’s essential to check for bias before opening AI services to the public. Their AI chatbot, Tay, proved that it was capable of mimicking human behavior but was not yet able to determine what is acceptable behavior to copy.

Tay ended up sending out inappropriate tweets, and after 16 hours the bot was shut down.

2. Google AI Bias example

In a failed 2015 Google AI experiment, their image recognition software embedded within their popular Google Photos service began to automatically classify black people in photos as “gorillas.”

Google apologized and said they were “appalled” by the mistake and subsequently blocked it’s image recognition software from identifying gorillas altogether.

3. Facebook AI Bias example

Facebook was forced to apologize in 2021 after it’s video labeling AI began labeling black men as ‘primates’ in a video shared by a news outlet. The prompt, asked users if they would like to “keep seeing videos about Primates.”

How to keep human bias out of AI

AI bias may seem like a problem that only affects those currently developing AI, however, AI technologies will fully permeate into mainstream software and programs faster than you think — which is why now is the best time for your Tech company to find strategies to eliminate AI bias.

Though eliminating these prejudices is no easy task, here are a few strategies that can help your business protect your consumer appeal, increase revenue, and gain a competitive advantage — all while doing your civil duty to foster equity in the Tech world:

Understand who is harmed by AI bias

Understanding the impact AI will have on your Tech business ensures you know the consequences that come with biased technology.

AI bias is not something to overlook considering this technology will interact with not only your employees but consumers as well, especially regarding digital marketing.

Forward-thinking Tech companies must seek to understand the influence these machines will have on their current and future digital marketing strategies.

The awareness of the potential influence AI biases could have on elements like social media marketing will incentivize companies like yours to create a moral framework during the AI development stage, while also continuing to retain the trust of their audience by integrating socially-appropriate and bias-free chatbots and other consumer-facing AI solutions.

Diversity in tech is important

When you choose to increase the diversity of your Tech talent, you’ll reap benefits like increased revenue and better innovations.

A diverse staff is also key to removing and avoiding bias. A diverse business will have more awareness of issues like race, gender, and sexuality, which will decrease the chance of AI bias as early as the development stage.

Ultimately a company lacking diversity will encounter data bias, which will likely be amplified by the AI technology that is directly interacting with consumers unless programmed differently. These machines utilize biased information, and an unintentional stereotype is created; this stereotype is likely to cause a decrease in customer retention.

Meanwhile, the more diverse your staff is, is the more likely you are to catch this biased information before it becomes embedded in your AI-based tools or released to the public in the form of a stereotype-driven social media campaign or an inappropriate chatbot.

Improve AI technician training

Adam Kalai, Co-author of “Man is to Computer as Woman is to Homemaker? Debiasing Word Embeddings” says teaching AI the difference between good and bad associations is essential when looking to avoid AI biases.

Kalai goes on to compare AI to human babies, and he’s right in the sense that AI, much like children, will mirror the input you’ve given it, no matter positive or negative. Your Tech business must reevaluate how you train those developing and working on these technologies to ensure these machine learning devices have a socially-conscious methodology.

Reconfiguring your training methods will help dismantle hidden biases, leaving little room for those stereotypes to reach the AI. 

To craft the best experience for your internal staff and consumers, your Tech business must secure ML experts who are well-versed in AI development and know how to avoid introducing subliminal biases to AI-driven technologies.

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