Bias in Artificial Intelligence

Every day we see human bias in our society. Whether we face gender bias, advertising bias, corporate bias, or more, bias is prevalent in our lives, whether we realize it or not. However, this bias can also creep into our artificial intelligence systems. Bias in artificial intelligence (AI), also known as bias in machine learning, is when an algorithm produces prejudiced results due to assumptions made in the machine learning process.

Bias in AI can occur due to two situations: assumptions made while developing the algorithm or prejudiced training data. While producing an AI model, designers might let their personal feelings about certain groups enter the artificial intelligence they are constructing. Most of the time, a bias that derives from the developer is unintentional. Next, bias in AI can occur because of faults in the training data. Training data is a set of initial data fed into the artificial intelligence, so the program can understand and learn how to produce the intended results. Training data can most commonly be incomplete. If a set of training data doesn’t encompass all demographics, the AI will likely demonstrate bias. Additionally, if discrimination is evident in part of the training data, the AI will reflect that bias when used.

Allowing bias in our AI has numerous consequences. An example of this would be Amazon’s biased recruiting tool. In 2014, Amazon began reviewing job applicants’ resumes and rating applicants by using AI-powered algorithms so that recruiters don’t spend time on manual tasks. Although, they soon realized that the tool they were using was biased. Amazon’s tool used historical data from the last ten years to train their AI. This training data contained biases against women because the tech industry was male-dominated, and at the time, men composed 60% of Amazon’s employees. Therefore, Amazon’s recruiting AI incorrectly learned that males were preferable. Even though Amazon stopped using the algorithm, women still faced many consequences, including being denied the opportunity at a job. Similar to this situation, AI bias is also seen in healthcare systems due to race, advertising, and many more circumstances leading to penalizing consequences.

Nonetheless, we can still make AI unbiased. By collecting complete data, cleaning your training data of unconscious biases, and constantly checking your personal biases, it is possible to create AI that will make fair decisions. However, humans are not perfect, so we should not expect perfect AI. Artificial intelligence will only be as good as the person making it. When making AI, we must be aware of the risk of bias in AI, establish processes to test and remove bias, engage in conversation on bias in AI, and invest in diversifying the AI field to build the best AI possible.

Picture Source: towardsdatascience.com