AI and bias refer to the systematic prejudice that can emerge in artificial intelligence systems due to the data they are trained on or the algorithms that govern their behavior. This bias can manifest in various forms, including racial, gender, and socio-economic biases, leading to unfair outcomes in decision-making processes. Common use cases include facial recognition systems, hiring algorithms, and predictive policing, where biased AI can perpetuate existing inequalities. Addressing AI bias is crucial for developing fair and ethical AI technologies that serve all individuals equitably.
A/B testing compares two versions of a product to optimize performance and improve user engagement.
AI FundamentalsExplore the concept of accountability in AI, focusing on ethical responsibilities and transparency i...
AI FundamentalsAccuracy is a key metric for evaluating AI model performance, indicating the proportion of correct p...
AI FundamentalsAcoustic modeling is essential for speech recognition, representing audio signals and phonetic units...
AI Fundamentals