Harvard's AI ethics framing is the one that treats ethics as strategy rather than as compliance

A lot of AI ethics writing treats the topic as a list of things not to do. Harvard's piece https://professional.dce.harvard.edu/blog/ethics-in-ai-why-it-matters/ is more useful because it frames ethical AI as both a social responsibility and a competitive and organisational advantage.

The issues covered, data privacy, bias, transparency, explainability, employment impact, governance and regulation, are the ones that actually affect AI product decisions at every level from individual tool use through to enterprise deployment.

The specific framing I find most useful: bias is not an edge case in AI systems, it is a structural feature of any system trained on historical data that reflects historical inequalities. Acknowledging that upfront changes how you evaluate AI tools and how you design around the limitations rather than assuming the model is neutral.

The employment impact section is the one that generates the most discussion and the most discomfort. Worth having honestly rather than avoiding.

For people working in or adjacent to AI products: which of the six ethical issues covered in the article do you think receives the least serious attention in practice relative to how much it deserves?