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AI Biases Examples
AI bias is a problem that plagues AI systems, especially those that use deep learning. It's the result of algorithms being trained on datasets with inherent biases, which can lead to discrimination and unfairness in decision-making processes.
In this blog post, we'll discuss some AI biases examples, what AI bias is and how to avoid it.
Businesses need to be aware of AI bias to avoid it when building and using their own AI systems.
What Is AI Bias? Defining Biased Data
AI bias is a problem in artificial intelligence systems that arises when an algorithm is trained on biased data, leading to biased decisions. For example, if a machine learning algorithm were trained on only male doctors and female nurses, it would be unlikely to be able to identify gender-neutral words such as doctor or nurse accurately.
This problem is exacerbated by the fact that many algorithms are designed by humans who may not know their own biases.
Further, only 24% of companies stated that unbiasedness, diversity, and global-ability as mission critical.
Several AI initiatives still need to make a genuine commitment to overcoming bias in AI—and the success of these efforts has become increasingly important.
What Are the Types of Bias? AI Biases Examples
In the case of AI, bias may take two forms:
Algorithmic bias or "data-based" biases,
and societal AI bias.
Algorithmic data-based biases occur when algorithms use biased training datasets to learn from values that are inaccurate representations of reality (for example, facial recognition software that isn't taught how human faces differ significantly between races).
Societal AI bias, on the other hand, occurs due to our assumptions and norms as a society causing us not to see certain aspects correctly—it's essentially an issue where we don't know what we need to know!
Algorithmic Bias or Data-based bias
Algorithmic bias is the result of a computer program making decisions based on data that is biased. It can occur when the data used to train the algorithm includes only certain types of information, such as white males and no women or minorities.
The resulting algorithm will then make decisions based on this limited information and may be unable to make accurate predictions in other situations.
This type of bias can also occur when the training data is skewed in some way, such as by excluding certain groups that would have otherwise been included.
An example of this might be if you were training an algorithm to detect faces in images. You used photos from a specific country where people only had dark skin tones. It could cause problems applying this same model elsewhere in the world where people have different skin colors.
AI is a tool, not a person.
It's designed to solve problems and make decisions. It has no inherent bias or prejudice—it only knows what you tell it. That means that the biases and prejudices that go into your AI system come from the people who built it or taught it to "think" in ways that mimic human behavior.
This is why we must be careful about how we train our AI systems.
For example, we need to be aware of how society is biased toward specific groups of people, so we can make sure to avoid those same biases when creating our programs.
When we build AI, we must ensure that it doesn't perpetuate existing biases. It can be done by training an AI system using data from a diverse set of people. If you're building a facial recognition system, you should use photos of people with different skin colors and ethnicities.
A good example of societal bias would be to assume that people from one ethnicity share a single skin tone. This also applies to other forms of bias.
Suppose you're building a system to make medical diagnoses, for example. In that case, it should use data from a diverse set of patients so that it doesn't associate specific symptoms with one group more than another.
Where Does AI Bias Originate?
AI bias is a complex issue, and it's essential to understand the different sources of bias.
Bias can come from the data that AI systems are trained on or how they were programmed. It can also be introduced by human factors, like the people who write code for AI systems.
Finally, there's also a bias inherent in how AI systems are designed—it's not just about what's in their memory banks but about how they perceive and interact with their environments.
Let's take a look at the portrait art generator, for example. This AI tool can be used to turn your selfies into Renaissance and Baroque-style portraits—almost as if masters themselves drew them!
However, since the data fed into the AI largely contained only white and caucasian portraits, it failed to account for varying skin tones in its results.
Essentially, this meant that if you tried feeding the AI African-American selfies, it would render portraits with inaccurate skin tones.
It is an example of how AI can be both biased and limited by its design. The same thing can happen when you feed different types of data into the system—it only knows what it's been taught, so if the data set isn't diverse enough, it will be biased in its output.
The solution to this problem is to ensure that when you train your AI, you provide it with various data sets. It will help prevent it from being biased and ensure that it can account for different skin tones and ethnicities in its output.
What are the Ways to Prevent Bias in AI From Creeping Into Your Models?
One of AI's biggest challenges is preventing bias from creeping into models. Bias can come from various sources, including the data you use to train your model and any human bias that may have crept in during the process. Here are some basic guidelines:
Define and Narrow Down the Goal of Your AI
To prevent bias in an artificial intelligence model, you must define and narrow down the goal of your AI. It means that you need to specify the exact problem you want to solve and then narrow it further by defining what exactly you want your model to do with that information.
Solving too many scenarios can mean you need thousands of labels across an unmanageable number of classes. Narrowly defining a problem, to start, will ensure that your model is performing well on the exact task it was designed for.
For example, if you're trying to predict whether an image contains a cat or not, it's essential to define what exactly constitutes a "cat."
If your image is of a white cat on a black background, does that count as a cat? What about a black and white tabby? Would your model be able to tell the difference between these two scenarios?
Collect Data in a Way That Allows for Differing Opinions
When collecting data, it's essential to ensure you're getting the complete picture. One of the best ways to do this is by collecting data in a way that allows for varying opinions.
When building an AI model, multiple ways exist to define or label a particular event. Accounting for these differences and incorporating them into your model will enable you to capture more variations in the real world's behavior.
For example, let's say your model is designed to predict whether or not a particular person will buy something. You might ask your model to classify people based on their age, income level, and occupation.
But what if other factors can be used to predict this outcome? For example, you could also include the length of time someone has been in the market for a new car or how many times they've visited the dealership before.
This is where the importance of feature engineering comes in. Feature engineering is the process of extracting features from your data so that you can use them for training your model. It would be best if you were very careful when selecting which features to include because they can significantly impact how well your model performs.
Understand Your Data
Managing bias in datasets is a widely discussed issue. The more you know about your data, the less likely it will be that objectionable labels slip through unnoticed into your algorithm.
When building an AI model, it's essential to understand the data that you're using. If you don't know where it came from, who collected it, or what information was included or excluded, then there's no way for you to know if there is bias in your dataset.
For example, suppose a dataset includes only white men as customers and doesn't include any women or people of color. In that case, you can't be sure that it will accurately predict results for anyone other than white men.
The same is true for data that's been collected in an unethical way. For example, if your data includes information about customers' race and gender but doesn't include their age or income, then your model may be biased towards young white men with higher incomes.
This is why it's crucial to examine your data closely. You should be able to identify any possible bias or limitations in the dataset and then decide if there's a way for you to correct them. If not, you may want to re-think whether this information is helpful.
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Gather a Machine Learning Team That Asks Diverse Questions
To prevent AI bias in your model, you must ensure that your team is diverse. A diverse group raises questions and makes observations you wouldn't have made otherwise, helping you anticipate problems before your model is in production.
You want people from different backgrounds so that you can get more varied perspectives on the problem at hand. This also helps with communication and collaboration between team members because there are other ways of thinking about issues and coming up with solutions.
Think of it this way: if everyone thinks the same way, then they will all come up with the same answer. But suppose each person has a different perspective. In that case, they might come up with multiple solutions or variations of responses leading to a better overall solution (That is, getting more information).
The best way to find diversity is to go outside your immediate circle. Seek out people from different backgrounds, cultures, and industries. If you work at a tech company, consider hiring someone from a non-tech background and someone who has worked in various sectors such as banking or retail.
Also, consider gender diversity if you have men on your team primarily.
I hope you enjoyed some some AI biases examples in this blog post.
As AI continues to grow and evolve, we will see more and more instances where technology is being used to perpetuate bias. The best way to fight this is through education, training, and awareness.
The first step is for companies to acknowledge their biases and consider them when making decisions about their AI systems. As humans, we all have biases that affect how we see the world around us. However, when it comes to computers and algorithms, it's easy for us to forget that computers do not have a sense of morality or ethics, so they will continue using the same data sets over and over again without realizing that they're biased.
Next, companies should be transparent about their data sets so that they can be analyzed by others outside of their own organization who may notice trends or patterns that may not have been noticed before because they weren't part of the original team or company culture.
Finally and most importantly: We need to educate ourselves on how our biases affect our day-to-day lives and how these biases might affect AI systems over time if left unchecked.
🤔 Want to see how Graphite Note works for your AI use case? Book a demo with our product specialist!
This blog post provides insights based on the current research and understanding of AI, machine learning and predictive analytics applications for companies. Businesses should use this information as a guide and seek professional advice when developing and implementing new strategies.
At Graphite Note, we are committed to providing our readers with accurate and up-to-date information. Our content is regularly reviewed and updated to reflect the latest advancements in the field of predictive analytics and AI.
Hrvoje Smolic, is the accomplished Founder and CEO of Graphite Note. He holds a Master's degree in Physics from the University of Zagreb. In 2010 Hrvoje founded Qualia, a company that created BusinessQ, an innovative SaaS data visualization software utilized by over 15,000 companies worldwide. Continuing his entrepreneurial journey, Hrvoje founded Graphite Note in 2020, a visionary company that seeks to redefine the business intelligence landscape by seamlessly integrating data analytics, predictive analytics algorithms, and effective human communication.
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