5 Algorithms that Demonstrate Artificial Intelligence Bias

Artificial intelligence (AI) is a powerful tool that can be used to solve many problems. However, AI algorithms can also be biased, which can lead to unfair or discriminatory outcomes. Here are 5 algorithms that demonstrate AI bias: Facial recognition algorithms: Facial recognition algorithms have been shown to be biased against people of color. For example, a study by the ACLU found that Amazon's facial recognition algorithm was more likely to misidentify people of color than white people. Credit scoring algorithms: Credit scoring algorithms are used to determine a person's creditworthiness. However, these algorithms have been shown to be biased against women and minorities. For example, a study by the Consumer Financial Protection Bureau found that black borrowers were more likely to be denied credit than white borrowers, even when they had similar credit histories. Recruitment algorithms: Recruitment algorithms are used to screen job applicants. However, these algorithms have been shown to be biased against women and minorities. For example, a study by Google found that its recruitment algorithm was more likely to recommend male candidates for technical roles than female candidates. Language models: Language models are used to generate text, translate languages, and answer questions. However, these models have been shown to be biased against women and minorities. For example, a study by Stanford University found that Google's language model was more likely to associate positive words with men and negative words with women. Self-driving cars: Self-driving cars use AI to make decisions about how to navigate the road. However, these algorithms have been shown to be biased against pedestrians and cyclists. For example, a study by the University of California, Berkeley found that self-driving cars were more likely to hit pedestrians and cyclists who were black than pedestrians and cyclists who were white. These are just a few examples of AI algorithms that demonstrate bias. It is important to be aware of these biases so that we can take steps to mitigate them.