While an artificial intelligence (AI) does not have its own personality, per se, that does not mean they are not affected by bias. Deep learning algorithms are designed to identify patterns and use them to make recommendations, come to decisions, or render conclusions. If any part of the learning process promotes bias, then the AI ultimately develops one. And, if an AI bias occurs, it can be incredibly hard to fix.
The Origins of AI Bias
AI bias can happen for a variety of reasons. While the most obvious source is the data used by the system, other issues can also result in bias.
For example, an AI is usually designed to help answer a specific question. If that question contains a subjective component, or a concept that is open to interpretation, the company creating the AI puts their own definition on the concept. If their viewpoint is biased (even if it is unintentional) or even just poorly defined, the AI could produce unintended outputs, creating a lack of fairness or other observable bias.
When data is collected, bias can show up in one of two ways. First, if the data collection method results in an inaccurate depiction of reality, that can create bias. Second, if the data reflects existing biases that are present in society, the AI then has them as well.
Finally, when data is prepared, bias can also creep in there, even if the source data was unbiased. For instance, the attributes selected for the AI to review could create a prejudice.
Why Eliminating AI Bias is So Challenging
Dealing with AI bias is actually incredibly difficult. In some cases, the introduction of bias is not very apparent, so the designer may not realize there is a problem until they begin reviewing outputs. When this occurs, retroactively finding the source of the issue is a daunting task.
Similarly, the subjective nature of some core questions can make it difficult to determine what an unbiased outcome looks like. Along the same line, defining fairness itself is not easy, particularly since it has to be examined in mathematical terms when designing an AI. Since social context can impact the definition of fairness, and that can vary dramatically from one location to the next, the challenge is even greater.
Dealing with AI Bias in the Future
While the AI bias problem could be considered vast, researchers are working diligently to find a solution to the problem. This includes developing new algorithms that detect potential issues, including hidden biases, and processes that hold organizations accountable for unfair practices.
Dealing with AI bias will take time. However, even if it will not be solved easily, a solution is in the works.
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