FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -Bias in AI is when the machine gives consistently different outputs for one group of people compared to another, as outlined in the blood oxygenation example above. Typically these bias outputs follow classical societal biases like race, gender, biological sex, nationality or age.

Detailed explanation-2: -Machine learning bias generally stems from problems introduced by the individuals who design and/or train the machine learning systems. These individuals could either create algorithms that reflect unintended cognitive biases or real-life prejudices.

Detailed explanation-3: -Algorithms have been criticized as a method for obscuring racial prejudices in decision-making. Because of how certain races and ethnic groups were treated in the past, data can often contain hidden biases. For example, black people are likely to receive longer sentences than white people who committed the same crime.

Detailed explanation-4: -For example, a facial recognition algorithm could be trained to recognize a white person more easily than a black person because this type of data has been used in training more often. This can negatively affect people from minority groups, as discrimination hinders equal opportunity and perpetuates oppression.