Shadowing Practice: What is AI Ethics? - Learn English Speaking with Video
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I want to start off with talking to you about three things that keep me up at night, right?
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Three things.
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The first, and it may be, you know, very common for you too, is climate change.
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Climate change absolutely keeps me up at night.
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The second thing that keeps me up at night is
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that people may have no idea that an artificial intelligence is making a decision
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that directly impacts their lives what percentage interest rate you get on your loan,
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whether you get that job that you applied for, whether your kid gets into that college that they really want to go to.
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Today, AI is making decisions that directly impact you.
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The third thing that keeps me up at night is even
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when When people know that an AI is making a decision about them,
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they may assume that because it's not a fallible human with bias,
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that somehow the AI is going to make a decision that's morally or ethically squeaky clean.
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And that could not be farther from the truth.
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So if you think about organizations and what happens, over 80 % of the time,
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proof of concepts associated with artificial intelligence actually gets stalled in testing.
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And more often than not, it is because people do not trust the results from that AI model.
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So we're going to talk a lot about trust.
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And when thinking about trust, I'm going to switch colors here, there's actually five pillars.
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Okay.
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When you're thinking about what does it take to earn trust in an artificial intelligence
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that's being made by your organization or being procured by your organization, five pillars.
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The first thing to be thinking about is fairness.
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How can you ensure that the AI model is fair towards everybody, in particular historically underrepresented groups?
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The second is explainable.
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Is your AI model explainable such that you'd be able to tell somebody,
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an end user, what data sets were being used in order to curate that model?
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What methods, what expertise, what's the data lineage and provenance associated with how that model was trained?
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The third, robustness.
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Can you assure end users that nobody can hack such an AI model such
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that a person could disadvantage willfully other people and or make the results of that model benefit one particular person over another?
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The fourth is transparency.
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Are you telling people right off the bat than the AI model is indeed being used to make that decision?
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And are you giving people access to a fact sheet or metadata so that they can learn more about that model?
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And the fifth one is, are you assuring people's data privacy?
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So those are the five pillars, okay?
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Now, IBM has come up with three principles when thinking about AI in an organization.
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The first being that the purpose of artificial intelligence is really meant to be to augment human intelligence,
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not to replace it.
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The second is that data and the insights from those data belong to the creator alone.
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And the third is that AI systems, and I would opine the entire AI lifecycle, really should be transparent and explainable right?
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So those are the five pillars.
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Now, the next thing I want you to remember
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as you're thinking about this space of earning trust and artificial intelligence is that this is not a technological challenge.
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It can't be solved with just throwing tools and tech over some kind of a fence.
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This is a socio -technological challenge.
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Socio meaning people, people, people.
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Socio -technological challenge.
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And because it's a socio -technological challenge.
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It must be addressed holistically, okay?
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Holistically, meaning there's three major things that you should think about.
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I mentioned people, people, the culture of your organization, right?
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Thinking about the diversity of your teams, you know, your data science team who is curating that data to train that model.
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How many women are on that team?
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How many minorities are on that team, right?
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Think about diversity.
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I don't know if you've ever heard of the the wisdom of crowns that's actually a proven mathematical theory
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The more diverse your group of people, the less chance for error.
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And that is absolutely true in the realm of artificial intelligence.
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The second thing is process or governance, right?
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What is it that you as your organization,
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what are you going to promise both your employees as well as the market with respect to what standards
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you're going to stand by for your AI model in terms of things like fairness and explainability, accountability, etc., right?
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And the third area is tooling, right?
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What are the tools, AI engineering,
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methods, frameworks that you can use in order to ensure these things, ensure those five pillars?
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And we're going to do a deep dive into that as well.
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But the next show that I'm going to be running with you, we're actually going to be talking about this one, about people and culture.
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So stay tuned.
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If you like this video and series, please comment below.
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Stay tuned for more videos that are part of this series.
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And to get updates, please like and subscribe.
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Vocabulary and speaking notes for this lesson
This speaking lesson is built on the video “What is AI Ethics?”. The speaker keeps coming back to these words: model, intelligence, artificial, organization, thinking. This video has 21 sentences and 859 words to shadow. The speech runs for 6:09. The speaker talks at a steady 140 words per minute, a comfortable pace for shadowing. 86% of the words are among the 3,000 most common in English; the rest is worth studying before you start. 13 of its 21 sentences are questions, so it is good practice for question intonation and short answers.
Key vocabulary in this video
15 words from the video worth learning, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| artificial adjective | /ˌɑː.tɪˈfɪʃ.əl/ | Man-made; made by humans; of artifice. |
| pillar noun | /ˈpɪlɚ/ | A large post, often used as supporting architecture. |
| technological adjective | /ˌtɛk.nəˈlɑ.dʒɪ.kəl/ | Of, relating to, or involving technology, especially modern scientific technology. |
| socio noun | At an institute of education, a class where sociology is taught. | |
| earn verb | /ɝn/ | To gain (success, reward, recognition) through applied effort or work. |
| associate noun | /əˈsoʊʃi.ət/ | A person united with another or others in an act, enterprise, or business; a partner or employee. |
| diversity noun | /daɪˈvɜː(ɹ)sɪti/ | The quality of being diverse or different; a difference or unlikeness. |
| tune noun | /tjuːn/ | A melody. |
| assure verb | /əˈʃʊɹ/ | To make sure and secure; ensure. |
| fairness noun | /ˈfɛɚ.nəs/ | The property of being fair or equitable. |
| percentage noun | /pə(ɹ)ˈsɛntɪd͡ʒ/ | The amount, number or rate of something, regarded as part of a total of 100; a part of a whole. |
| solve verb | /sɒlv/ | To find an answer or solution to a problem or question; to work out. |
| minority noun | /maɪˈnɒɹ.ɪ.ti/ | The state or condition of being smaller, inferior, or subordinate to something or someone else; lesserness. |
| sheet noun | /ʃit/ | A thin bed cloth used as a covering for a mattress or as a layer over the sleeper. |
| belong verb | /bɪˈlɒŋ/ | To have its proper place. |
Phrasal verbs you will hear
| Word | Meaning |
|---|---|
| come up with verb | To manage to produce, deliver, or present (something) by inventing, creating, thinking of, or obtaining it. |
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Passive voice be + past participle — the focus is on what happens, not who does it | being made · was trained · is really meant |
| Present perfect have/has + past participle — a past action that still matters now | has come · you've ever heard |
Pronunciation to watch
The speaker uses 13 contractions and reduced forms, such as we're, you're, I'm. Say them the short way, as you hear them.
- The “th” sounds: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
- The “sh” and “zh” sounds: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
- Long words — get the stress right: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Shadow it sentence by sentence at normal speed, repeating each one until your rhythm matches the speaker.
- Record yourself and compare with the original, paying attention to words like artificial, pillar, technological.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.