Ethics of AI — Bias, Deepfakes, and Responsibility

An AI can pass a medical licensing exam, but it can also be less accurate at identifying diseases on darker skin tones. This isn't a bug. It's a feature of the data it learned from. AI is a powerful tool, but it inherits our blind spots. The biggest questions about AI aren't about code, they're about fairness, responsibility, and what we value as humans. You are part of the generation that gets to write the rules.

Artificial intelligence is built on data. If that data reflects historical human biases, the AI will learn, reproduce, and even amplify those biases. This is called algorithmic bias, and it has real-world consequences.Biased by Design: An AI trained on historical hiring data might penalize resumes with female-associated names because it learned from a time when men dominated the field. Facial recognition systems have shown lower accuracy rates for women and people of color, a direct result of unrepresentative training datasets.The Trust Crisis: AI-generated deepfakes—hyper-realistic but completely fabricated videos or audio—are becoming indistinguishable from reality. This creates an 'epistemic crisis' where we can no longer automatically trust what we see or hear, threatening everything f...

Kwame, 19, noticed a strange pattern in his university class. Essays graded by a new AI tool consistently scored students from his cultural background lower than essays graded by the professor. He suspected bias but needed proof. He and a few classmates compared their AI-graded scores with their human-graded scores from other assignments and found a clear discrepancy. Kwame presented the data to his professor, not as an accusation, but as a concern. The professor investigated and discovered the AI was trained on a narrow set of essays, making it less effective at evaluating different writing styles. The university replaced the tool. Kwame realized that challenging an algorithm with good data wasn't just possible, it was necessary.