List of AI Bias 👇👇👇

Here are the Top 7 Types of AI Bias to Be Aware Of: 1. Data Bias: The most common form of AI bias, it occurs when the data used to train an AI system isn't representative of the population the system will serve. For instance, if a facial recognition system is trained mostly on images of light-skinned people, it may perform poorly on darker-skinned individuals. 2. Confirmation Bias: AI systems can amplify existing biases if they're trained on biased data. This is because the AI learns from and perpetuates the biases present in the training data, leading to skewed outcomes. 3. Measurement Bias: This happens when the methods used to collect or measure data are skewed. For example, if a voice recognition system is trained using high-quality microphones, it may perform poorly when used with lower-quality microphones. 4. Algorithmic Bias: This occurs when the algorithms used to process data and make predictions are biased. It could be due to the design of the algorithm or the way it learns patterns from the data. 5. Sampling Bias: If the data used to train an AI isn't a good sample of the population, it can lead to sampling bias. For example, an AI trained to recognize pictures of dogs, but only using images of poodles, might not recognize other breeds effectively. 6. Prejudice Bias: This occurs when societal prejudices are reflected in the AI's output. For instance, an AI trained on past hiring decisions might discriminate against certain groups if those decisions were discriminatory. 7. Exclusion Bias: This type of bias happens when important variables or options are left out during data collection or algorithm development, leading to flawed conclusions or decisions 🚀 WriterZen will go live soon: https://www.producthunt.com/products/writerzen-1