"Bias in AI" has long been a critical area of research and concern in machine learning circles and has grown in awareness among general consumer audiences over the past couple of years as knowledge of AI has grown. It's a term that describes situations where ML-based data analytics systems show bias against certain groups of people. These biases usually reflect widespread societal biases about race, gender, biological sex, age, and culture. There are two types of bias in AI. One is algorithmic AI bias or "data bias," where algorithms are trained using biased data.

"Bias in AI" has long been a critical area of research and concern in machine learning circles and has grown in awareness among general consumer audiences over the past couple of years as knowledge of AI has grown. It's a term that describes situations where ML-based data analytics systems show Bias against certain groups of people. These biases usually reflect widespread societal biases about race, gender, biological sex, age, and culture. There are two types of bias in AI. One is algorithmic AI bias or "data bias," where algorithms are trained using biased data.

"Bias in AI" refers to situations where machine learning-based data analytics systems discriminate against particular groups of people. This discrimination usually follows our own societal biases regarding race, gender, biological sex, nationality, or age (more on this later). Just this past week, for example, researchers showed that Google's AI-based hate speech detector is biased against black people. In this article, I'll explain two types of bias in artificial intelligence and machine learning: algorithmic/data bias and societal bias. I'll explain how they occur, highlight some examples of AI bias in the news, and show how you can fight back by becoming more aware.