On AI ethics and accountability

I attended a BCS seminar on AI Ethics, presented by Dr Kitty Hung. The lecture and slides will be shared on the BCS Video Channel. She identified a series of risks and posed some classes of solution. I was curious to see what others were saying and so asked ChatGPT.

By Chris Yang via unsplash

I reproduce the results below,

“AI ethics is a rapidly evolving field with multiple perspectives on how to develop and deploy AI responsibly. Key areas of focus include:

1. Bias and Fairness — AI systems often inherit biases from training data, leading to concerns about discrimination in hiring, lending, policing, and healthcare. Researchers emphasize fairness-aware algorithms, diverse datasets, and bias audits.
2. Transparency and Explainability — AI models, especially deep learning, function as “black boxes.” Ethical AI advocates push for explainable AI (XAI) to make decisions understandable to users and regulators.
3. Privacy and Surveillance — AI-driven data collection raises privacy concerns. Regulations like the EU’s GDPR and emerging U.S. laws aim to limit misuse. However, AI-powered surveillance remains controversial, especially in authoritarian contexts.
4. Accountability and Regulation — Governments and organizations are debating how to hold AI systems and their creators accountable. The EU AI Act and…