Use bias, opacity, explainability, consent accurately in context
Read and discuss a topic-specific article at C1 level
Practise speaking fluently on artificial intelligence and society
Complete written exercises with vocabulary in context
Teaching Notes
Warm-up: allow 8-10 min, let personal answers develop
Article: read together or have students read silently first
Vocabulary match: good for pair work
Speaking: encourage full sentences, not one-word answers
Exit questions: 5-min closer, no prep needed
Timing Guide
Warm-up: 8 min
Article + comprehension: 12 min
Vocabulary + match: 10 min
Exercises: 10 min
Speaking + discussion: 15 min
Exit + recap: 5 min
Teacher Question Bank
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C1 · Lesson 18 · Artificial Intelligence and Society
The Ethics of AI
Artificial Intelligence and Societybiasopacityexplainability
Getting started
Warm-Up Questions
Click the button to get your first question
Read & Understand
Article
The Ethics of AI
Artificial intelligence presents humanity with a set of ethical challenges that do not fit neatly into existing legal or moral frameworks. The opacity of large AI systems means that even their creators cannot fully explain how they arrive at particular outputs — raising profound questions about accountability and liability. Training data reflects the biases of the world from which it is drawn, meaning AI systems can perpetuate and amplify discrimination at scale. Meanwhile, the proliferation of generative AI is reshaping creative industries, educational assessment, and political communication in ways that societies are only beginning to process. Governance frameworks are developing, but technology is moving faster. The question is not whether AI will transform society — it already has. The question is who will shape the values embedded within it, and through what democratic process.
💡 Did you know? In 2023, a lawyer in New York submitted a legal brief citing cases that turned out to be entirely fabricated by ChatGPT. The case was a landmark warning about AI reliability in high-stakes contexts.
Topic: Artificial Intelligence and Society
Key words
Vocabulary
01
bias
systematic error in AI outputs reflecting inequities in training data
02
opacity
the quality of being difficult to understand or see into
03
explainability
the degree to which an AI's decision-making process can be understood
04
consent
voluntary agreement to something, especially the use of one's data
05
liability
legal responsibility for something
06
proliferation
rapid increase and spread
07
governance
the system by which something is controlled and regulated
08
sentience
the capacity to have subjective experiences and feelings
09
deterministic
producing fixed, predictable outputs from given inputs
010
alignment
ensuring AI systems pursue goals consistent with human values
Match the Words
Click a word on the left, then click its definition on the right.
bias
opacity
explainability
consent
liability
proliferation
governance
sentience
deterministic
alignment
the capacity to have subjective experiences and feelings
voluntary agreement to something, especially the use of one's data
the degree to which an AI's decision-making process can be understood
producing fixed, predictable outputs from given inputs
rapid increase and spread
the system by which something is controlled and regulated
ensuring AI systems pursue goals consistent with human values
legal responsibility for something
systematic error in AI outputs reflecting inequities in training data
the quality of being difficult to understand or see into
Say it right
Pronunciation
bias
BIAS
opacity
OPA-city
explainability
EXPL-aina-bility
consent
CON-sent
liability
LIA-bil-ity
proliferation
PROL-ifer-ation
Read & Discuss
Short Dialogue
A:
I've been thinking a lot about bias recently.
B:
Really? What's your take on it?
A:
I think the issue of opacity is often misunderstood.
B:
I agree. Most people don't consider the impact of explainability.
A:
Exactly. And when you add consent into the mix, it gets complicated.
B:
So what do you think the solution is?
A:
Honestly? It requires both individual action and systemic change.
B:
That's a fair point. It's never just one or the other.
Comprehension
What topic are they discussing?
What does person B agree with?
What does person A say the solution requires?
Practice
Exercises
Gap Fill
Complete each sentence using vocabulary from today's lesson.
1. Facial recognition systems have shown significant racial .
2. The of large language models makes accountability difficult.
3. Regulators are demanding greater from AI systems.
4. Training data is often used without explicit user .
5. Who bears when an AI medical system makes an error?
Error Correction
Find and correct the mistake in each sentence.
The bias of data has raise serious concerns.
Despite of the challenges, they succeeded.
The report, that was published last year, is relevant.
She suggested to review the consent more carefully.
Speaking practice
Speaking Prompts
Discuss with your partner
Should AI systems used in hiring, healthcare, or law be required to be explainable? What are the trade-offs?
Who should bear liability when an AI system causes harm — the developer, the user, or no one?
Can AI ever be truly ethical, or does ethics require consciousness?
Summarise today's topic in 3 sentences using vocabulary from this lesson.
Grammar focus: Advanced hedging in academic and ethical writing: It could be argued that... / T... — can you give an example?
Open discussion
Discussion Generator
More Questions
Use with pairs or whole class · Encourage full answers
Write a position paper (12-15 sentences) on one specific AI ethics issue of your choice. Use hedging language appropriately and engage with counterarguments.
