AI has the potential to help us make decisions in our daily lives, but it’s important to be aware of the potential for bias.
People build bias into AI for many reasons, and this often happens unintentionally. Some people may be unaware that they are biased in the first place. Others may be aware of their own biases but don’t realize that they are also shaping how AI is built. Here are some examples of how biases might come into play:
AI bias from data sources
Bias in data sources can lead to bias in the training data used to train AI systems. For example, if only male employees were included as trainers or reviewers for a facial recognition system, then the system would likely perform poorly for women and maybe even for men with darker skin tones. Another example involves data about crime rates: if only areas where white people live were used as training data, then an AI system might falsely accuse black people of being more likely to commit a crime than white people – even when there is no evidence to support that claim. Bias can also come from using biased labels – such as with images tagged by human workers – or from using biased search terms that inadvertently exclude or include certain groups of people from consideration during development and testing.
AI bias human-written rules
Bias can also come from human-written rules and definitions embedded within an AI system’s codebase, which was done in early days when very few developers had exposure to machine learning concepts or tools. It is common practice now for developers to use existing libraries and APIs instead of writing everything from scratch themselves. This means that developers may not have a good understanding of what they’re using when they use existing tools, which could result in unintended bias introduced into their systems via these libraries and APIs.
It is important to develop and test AI-powered systems for issues such as bias and discrimination before they are deployed onto public platforms like social media, transportation networks and healthcare systems.
But machine learning models can be difficult to debug because they are not explicit or transparent about how they make decisions. In fact, that's part of the reason why they're so popular - they function as black boxes that take in requests and output something incredible, whether it's art (like 1SecondPainting), music, or text.
In any case, understanding why bias can exist within machine learning algorithms is useful because this understanding allows us to take steps toward reducing bias before deploying machine-learning applications within safety-critical environments like autonomous vehicles or medicine where reducing bias could prevent unnecessary accidents and save lives.
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