Browse all content tagged with Bias
Explore how Artificial Intelligence impacts human rights, balancing benefits like improved access to services with risks such as privacy violations and bias. Learn about international frameworks, regulatory challenges, and the importance of responsible AI deployment to protect fundamental rights.
Explore bias in AI: understand its sources, impact on machine learning, real-world examples, and strategies for mitigation to build fair and reliable AI systems.
Discrimination in AI refers to the unfair or unequal treatment of individuals or groups based on protected characteristics such as race, gender, age, or disability. This often results from biases embedded in AI systems during data collection, algorithm development, or deployment, and can significantly impact social and economic equality.
Garbage In, Garbage Out (GIGO) highlights how the quality of output from AI and other systems is directly dependent on input quality. Learn about its implications in AI, the importance of data quality, and strategies to mitigate GIGO for more accurate, fair, and reliable outcomes.
Underfitting occurs when a machine learning model is too simplistic to capture the underlying trends of the data it is trained on. This leads to poor performance both on unseen and training data, often due to lack of model complexity, insufficient training, or inadequate feature selection.
Explore the basics of AI reasoning, including its types, importance, and real-world applications. Learn how AI mimics human thought, enhances decision-making, and the challenges of bias and fairness in advanced models like OpenAI’s o1.
