We’ve just talked about the difference between Human-in-the-Loop and Human-at-the-Heart, and explored the spectrum of automation in language workflows. I think this is exactly the right moment to bring in AI ethics.
Once we begin asking who leads, who decides, and who is affected—we need a framework to think clearly. To grasp AI ethics in a practical and structured way, it’s helpful to consider two key aspects:
◼ Dimensions (Fairness, responsibility, transparency, privacy)
◼ Timeline (Before and after deployment)
This framework helps move ethical thinking from the abstract to the operational—especially when applied to real use cases in the language industry.
It is also important to step outside the high-income-country framework. Once you look at “fairness” on a global scale, it becomes clear: the distribution of AI benefits is deeply uneven, and that’s an ethical concern too.
Most mainstream discussions focus on how AI is used, policy lines, and regulatory safeguards. But in many parts of the world, access itself is still the issue. The infrastructure isn’t there. The conversations haven’t even started.
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