Bias in AI
Bias in AI refers to systematic errors or unfair outcomes in machine learning models that arise from biased training data, flawed assumptions, or problematic design choices. Addressing AI bias is essential for building fair and equitable systems.
Understanding Bias in AI
Bias in AI refers to systematic patterns of unfairness that can emerge at every stage of the machine learning pipeline, from data collection and annotation through model training to deployment. Training data often reflects historical societal biases: hiring models trained on past decisions may discriminate against women, and facial recognition systems trained predominantly on lighter-skinned faces show higher error rates for darker-skinned individuals. Algorithmic bias can also arise from proxy variables, where seemingly neutral features like zip codes correlate with race or income. The consequences affect loan approvals, criminal sentencing recommendations, and healthcare resource allocation. Addressing bias requires diverse training data, fairness-aware algorithms, rigorous auditing with tools like the confusion matrix broken down by demographic groups, and ongoing monitoring for data drift. AI ethics frameworks and regulations increasingly mandate bias assessments before deploying AI systems in high-stakes contexts.
Category
AI Ethics & Safety
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Adversarial Attack
An adversarial attack is a technique that creates deliberately crafted inputs designed to fool a machine learning model into making incorrect predictions. These attacks reveal vulnerabilities in AI systems and are critical to AI safety research.
Adversarial Training
Adversarial training is a defense strategy that improves model robustness by including adversarial examples in the training data. The model learns to correctly classify both normal and adversarially perturbed inputs.
AI Alignment
AI alignment is the research field focused on ensuring that AI systems pursue goals and behaviors consistent with human values and intentions. Alignment is considered one of the most important challenges in AI safety.
AI Ethics
AI ethics is the branch of ethics that examines the moral implications of developing and deploying artificial intelligence systems. It addresses fairness, transparency, privacy, accountability, and the societal impact of AI technology.
AI Safety
AI safety is the interdisciplinary field focused on ensuring AI systems operate reliably, beneficially, and without causing unintended harm. It encompasses alignment, robustness, interpretability, and governance research.
Constitutional AI
Constitutional AI is an approach developed by Anthropic that trains AI systems to be helpful, harmless, and honest using a set of written principles. The model critiques and revises its own outputs based on these constitutional rules.
Deepfake
A deepfake is AI-generated synthetic media that convincingly replaces a person's likeness, voice, or actions in images, audio, or video. Deepfakes raise significant concerns about misinformation and identity fraud.
Explainable AI
Explainable AI (XAI) encompasses techniques that make AI system decisions understandable to humans. XAI is crucial for building trust, meeting regulatory requirements, and debugging model behavior.