Code and Conscience: Algorithmic Bias

Artificial Intelligence doesn't 'think'; it identifies patterns in data. This leads to the principle of Garbage In, Garbage Out (GIGO). If a training dataset contains historical prejudices—such as a bank's past record of denying loans to specific neighborhoods—the algorithm will perceive those prejudices as 'rules' for success. This creates a feedback loop where the AI automates and scales past discrimination. Even if you remove explicit labels like 'race' or 'gender,' the AI often finds proxy variables (like zip codes or school names) that correlate with those protected categories, effectively smuggling bias back into the system.

Many modern algorithms, especially Deep Learning models, are considered 'Black Boxes.' This means that while we can see the input and the output, the internal logic—the millions of weighted connections—is too complex for humans to interpret. In predictive policing or judicial sentencing (like the COMPAS tool), this lack of explainability is a major ethical crisis. If a judge uses an algorithm to determine a defendant's 'risk score,' but cannot explain why the score is high, it violates the legal principle of due process and the right to contest evidence.

To combat bias, ethicists propose Algorithmic Impact Assessments (AIAs) and 'Human-in-the-loop' systems. Transparency requires companies to disclose what data was used for training. Accountability ensures there is a clear human party responsible for the AI's 'errors.' We also use Fairness Metrics, where we mathematically test if the error rate is equal across different demographic groups. For example, if a facial recognition system has a $0.1\%$ error rate for white men but a $35\%$ error rate for dark-skinned women, it fails the 'Equal Opportunity' constraint.