Source: University of New Hampshire School of Law

The article examines how artificial intelligence is not necessarily creating new biases but rather replicating and reinforcing existing societal inequalities. AI models are trained on historical data, meaning any biases embedded in that data are carried over into automated decision-making processes.

One of the key concerns is how AI bias affects critical areas such as hiring, criminal justice, healthcare, and finance. Automated systems designed to improve efficiency may unintentionally discriminate against marginalized groups, leading to unfair outcomes.

Legal experts argue that AI should be subject to strict oversight to ensure fairness, transparency, and accountability. Some regulatory proposals advocate for mandatory AI audits to detect and correct biased decision-making.

The article also explores the ethical dimensions of AI bias, questioning whether AI can ever be truly neutral. Since AI systems reflect human-designed algorithms, biases are often deeply ingrained in their outputs. Addressing this issue requires not only better training data but also diverse development teams and regulatory frameworks.

As AI continues to influence key sectors of society, the legal and policy debate surrounding algorithmic bias is becoming increasingly urgent. The article suggests that AI regulation should focus on mitigating harm while still allowing innovation in the field.