AI Ethics, Bias, and Fairness

AI models are not inherently objective; they are mirrors of the data they consume. Algorithmic Bias typically enters the system through two primary channels: Historical Bias and Representation Bias. Historical bias occurs when the training data reflects existing societal prejudices (e.g., a hiring tool trained on 20 years of male-dominated resumes). Representation bias happens when certain groups are under-sampled, leading the model to perform poorly on those populations. For instance, if a facial recognition system is trained on a dataset that is $80\%$ Caucasian, its error rate for other ethnicities will be significantly higher because the model has not learned the necessary feature variances.

In high-stakes fields like Healthcare or Criminal Justice, we face the 'Black Box' problem. Deep Learning models, particularly those with millions of parameters, are often uninterpretable. If a model denies a patient a specific treatment, doctors must know why. Without Explainable AI (XAI), we risk 'Automation Bias'—the human tendency to trust automated systems even when they are wrong. Ethically, we must balance Accuracy vs. Interpretability. A model with $99\%$ accuracy that cannot be explained may be less desirable in a courtroom than a $90\%$ accurate model that provides a clear reasoning path.