Experts have identified 188 human biases. Unfortunately, many of these biases seep into technology, such as artificial intelligence. Today, we see AI bias reflecting many of society’s discriminations and judgments.
Awareness is the first step towards eliminating that bias to improve the effectiveness of AI technology.
Below, we explore AI bias and discuss how to identify and eliminate it in the AI you use in your industry.
- Artificial Intelligence bias reflects patterns of discrimination and stereotypes prevalent in society.
- You can reduce AI bias by ensuring your systems are working from complete and diverse data sets.
- Human intervention is vital for eliminating AI bias.
What Is Artificial Intelligence Bias?
A bias is a preconceived notion or prejudice against people, places, things, or ideas. Unfortunately, all data that experts collect will have some bias. For example, online surveys only include responses from people who have access to computers or mobile devices. Medical data is also biased, as it usually only includes those who have access to health facilities and insurance. Additionally, research is affected by the time of day, geographical location, and survey incentives that attract a specific persona.
AI Bias Definition
This biased data is what AI pulls from when automating tasks and analyzing patterns. The process might adopt the bias of the research groups, scientists, and information, causing results that potentially prefer specific races, genders, and appearances. Worse, this could also lead to AI functions making stereotypical connections or discriminatory remarks.
Bias in AI
How AI Develops a Bias (With Examples)
Below are three ways AI might develop a bias.
Biased Training Materials
The foundation of AI bias is its training materials. These are the documents, algorithms, user habits, and information it studies to identify data patterns. While some AI bias comes from developers intentionally using prejudiced materials, most cases occur when AI unintentionally copies habits from a society with deep-set preferences.
One illustration of this is AI word association, or work embeddings. Word embeddings are critical for AI to sound more natural. They occur when a system connects similar words like mom and dad versus mom and airplane. However, societal stereotypes and real-world patterns influence AI and can cause biased word embeddings. A rudimentary example would be an AI associating art, cooking, and shopping with females and science, technology, and automobiles with males.
A bias also occurs when AI has incomplete data from a small subset of people.
This happened in 2015 when Amazon used an automated system to sort resumes. The issue came when they found the program preferred male applicants. The system learned by studying previous resumes to identify common denominators. However, most of the earlier resumes were from men, since the tech industry at the time was predominantly male. This caused the system to filter out most resumes from women.
Unintentional Algorithm Mistakes
Despite all that artificial intelligence can do, it still has its limits – one of them being its power of human discernment. Most of AI’s tasks follow a set mathematical algorithm. However, when there are flaws in that algorithm, it can make biased or discriminatory decisions.
One famous example of AI bias was Twitter’s photo cropping algorithm that preferred slim, young, and light-skinned faces in images. This bias came from society’s increased use of filters, which impacted and skewed the algorithm’s beauty standards when it selected the focal point of each picture.
How to Identify and Reduce AI Bias
Thankfully, there are steps you can take to identify and eliminate AI bias in your business.
1. Understand AI Bias
Again, being aware of and acknowledging the issue are vital in order to fix it. You must first fully understand what AI bias is and how it creeps into your systems. Only then can you identify the sources of prejudices and work to change it.
2. Look for Patterns of Partiality
If you find your AI is showing partiality towards a specific gender, race, or other differentiating traits, you may have identified a bias. You can also look at other companies that use the same programs for an example of the system’s data and potential preferences it holds.
3. Create Complete Data Sets
You have some control over your AI platform as you sync it with your office’s data. Now that you understand where bias comes from, you are better prepared to gather and use complete, representative data sets for training AI for your organization.
4. Keep Your AI Updated
Tech creators are aware of AI’s past patterns of bias, which is good news for you. That means developers are working towards reducing this. You can do your part by keeping your AI software and systems updated with the latest bug fixes and tools.
5. Put Processes in Place for Examining and Correcting AI Bias
Your employees should be part of the AI process to ensure that all content and predictive analytics are bias-free and inclusive. Establish practices for checking and correcting AI performance. This will help reduce bias and catch inappropriate content before the public sees it.
Here are six other ways you can minimize AI bias based on experts’ recommendations:
Is AI Worth the Risk?
The answer is a resounding YES.
With the proper precautions, AI can save you countless hours and resources by processing and analyzing large amounts of data at lightning speed while also taking the guesswork out of your content strategy. However, keep in mind that it’s here to work alongside you, not to replace you.
With AI in the workplace, you have a coworker that does the mundane and heavy lifting. However, you’re still responsible for quality control and supervisory roles, like checking the content it produces for accuracy and unbiased data.
Work with Your Own AI Content Marketer
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Instead, we offer a platform that compliments your workplace while still including employees to ensure you always have quality, unbiased content.
Contact us to discover more about our automated platform and how we’re changing the face of content marketing.