IELTS vocabulary: Artificial Intelligence Ethics

Artificial intelligence has become one of the most frequently set Task 2 and Part 3 topics in the last few years, and it has a specific vague-vocabulary failure mode: candidates either describe capability ("AI can do many things now") or jump straight to a single worry ("people might lose their jobs") without engaging with the actual ethical question a prompt is usually asking about — who is responsible, who gets harmed, and what oversight exists. Because AI is genuinely moving fast, examiners are also more likely to notice when a candidate's vocabulary sounds a year or two out of date, or when "artificial intelligence" is used as a single undifferentiated blob covering everything from a spam filter to a self-driving car. This article separates capability, concern, and governance vocabulary so an essay can name which layer of the debate it's actually addressing.

On capability, precision starts with not treating every AI term as interchangeable. "Artificial intelligence" is the broad field concerned with building systems that perform tasks normally requiring human intelligence, while "machine learning" is one specific technique within that field, in which a system improves its performance through exposure to data rather than through explicit, hand-written rules — not all AI is machine learning, though most headline-making modern systems are. "Generative AI" narrows further still, referring to systems that produce new content — text, images, audio — rather than simply classifying or predicting from existing data, and is worth naming specifically in any essay about AI-generated writing, art, or misinformation, since the ethical questions it raises (authorship, plagiarism, deception) differ from those raised by, say, a hiring algorithm. "Algorithmic decision-making" is the more general phrase for any system making or informing decisions in areas like hiring, lending, or medical diagnosis, and "autonomous system" describes one that acts (a self-driving car, a drone) with limited or no real-time human control — a genuinely distinct category from a system that merely recommends.

Concerns vocabulary is where most essays need the biggest upgrade, since "job displacement" alone can't carry an entire body paragraph. "Algorithmic bias" is systematic, directional unfairness in a system's output, typically inherited from patterns in its training data rather than deliberately programmed — worth distinguishing from ordinary "error," which is random and non-directional; a biased hiring algorithm might be highly "accurate" on average while still consistently disadvantaging one specific group, which is precisely what makes bias more dangerous than simple inaccuracy. The "black box problem" describes the difficulty of understanding exactly why a complex system produced a particular output, even for the engineers who built it, and "explainability" (sometimes "interpretability") is the property, or lack of it, that determines whether a human can get a genuinely understandable reason for a specific decision — a system can be technically "transparent" about its code and training process while still not being explainable for any individual output, and conflating the two understates how hard this problem actually is. "Job displacement" refers specifically to workers losing employment as tasks are automated, distinct from "automation" itself, which is the broader technical process and doesn't always result in net job loss. "Deepfake" (fabricated audio or video depicting a real person doing or saying something they didn't) and "misinformation" round out a genuinely current concerns vocabulary that a decade-old AI essay template wouldn't include.