Shadowing Practice: What is Responsible AI? A Guide to AI Governance - Learn English Speaking with Video
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The work of having human values be reflected in AI is not strictly a technical challenge with a technical solution,
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but one that is indeed socio -technical.
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And with any socio -technical challenge, it has to be approached holistically, meaning you need to be thinking about people, process, tools.
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People meaning what is the right organizational culture that is required to curate AI responsibly, which are the right AI governance processes,
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and the right tools in AI engineering frameworks.
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When I take the time to ask large audiences at AI
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summits who in their organization is accountable for responsible outcomes from artificial intelligence,
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the top three answers that I get are pretty bad.
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The first answer I typically get is no one, which is overtly terrible.
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The second common response that I get is we don't use AI.
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Although you might not be keeping track of it in a formal inventory program,
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absolutely you have employees that are using artificial intelligence in some way, shape, or form.
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And then the last common response that I get is everyone.
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And I would opine that if everyone is being held accountable for responsible outcomes from AI, is anyone actually being held accountable?
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The job of those who are being held accountable for responsible outcomes from artificial intelligence is expanding.
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It's a big job, right?
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Not only do these people have to actually achieve value alignment within their organizations, they also have to keep track of AI model inventory.
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They have to keep track of regulations, right?
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And there are a growing amount of regulations around the world.
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But there's also recognition that you can have AI models be lawful but awful,
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which means their purview, their responsibility actually has to push into ethics.
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And as soon as you push into ethics, you have to be a pretty darn good teacher.
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You have to be teaching those not only who are building AI models on your behalf
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and governing AI models on your behalf, but also who are going to be procuring AI models on your behalf.
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You want them to be able to do this work, and again, in a way that reflects your organization's values.
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First, I want to talk about what does AI literacy look
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like for those who are going to be governing AI models on your behalf.
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And the best way, my favorite way of approaching this kind of training is applied training.
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So the way that we work with those who are going to be governing AI models is,
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first of all, to dive into teaching people how do you operationalize principles like fairness, like explainability, like transparency,
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thinking through how do you make sure
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you can detail what are those functional requirements for what you expect to see in AI models,
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but also the non -functional requirements of what you expect to see in those systems around the use of those AI models.
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Then the second group
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that you would offer this applied training to are those who will be building and buying models on your behalf.
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And this applied training includes things like making sure that you're choosing AI model use cases to actually work on,
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which ones are really important to your organization.
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And then diving into each of those use cases,
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starting with, how do you make sure that the investment in that AI model is actually aligned to your business strategy?
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Teaching those teams working on those use cases, how do you assess for the risk of that particular use case, unintended effects of that use case?
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How would you approach mitigating those kinds of risks holistically.
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Then we give an introduction to fact sheets.
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In particular, not just how do you build a fact sheet, but how do you build one that's actually interpretable, that empowers people?
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We give an introduction to audits, and then we teach those teams how do you actually interpret the results from an audit
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so that they would know what to do when they see those audit results.
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Those doing this kind of applied training that I'm describing truly, truly benefit from actually working with a diverse and multidisciplinary team.
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Now, more than ever, having a leader or team that ensures the responsible use and responsible outcomes from artificial intelligence is absolutely crucial.
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Without a dedicated leader with a funded mandate to do this work, AI governance can absolutely fall through the cracks,
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leaving organizations vulnerable to the risks associated with the technology.
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A successful, responsible AI leader has a seat at the table and ensures that there are seats at the table for others.
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including the CISO, ensuring the AI ethics is weaved into the very fabric of the organization, not just left off at the end,
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but incorporated across the entire AI life cycle.
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They make accountability policies transparent and work across the organization to see them implemented.
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Finally, championing AI literacy in a holistic way is absolutely essential,
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ensuring that everyone within the organization understands how to build and buy AI models that actually reflects the organization's values.
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By investing in a responsible AI leader with that funded mandate to do the work, organizations unlock the full potential of artificial intelligence.
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They drive innovation and they create a culture of responsible and transparent AI use, ultimately leading to better decision making,
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improved customer experiences and sustained business success.
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Vocabulary and speaking notes for this lesson
This speaking lesson is built on the video “What is Responsible AI? A Guide to AI Governance”. The speaker keeps coming back to these words: model, responsible, organization, artificial, behalf. This video has 32 sentences and 900 words to shadow. The speech runs for 5:53. The speaker talks at a natural 153 words per minute, close to everyday conversation. 82% of the words are among the 3,000 most common in English; the rest is worth studying before you start. 7 of its 32 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 |
|---|---|---|
| behalf noun | /bɪˈhæf/ | The interest, benefit, or wellbeing of someone or something. |
| artificial adjective | /ˌɑː.tɪˈfɪʃ.əl/ | Man-made; made by humans; of artifice. |
| outcome noun | /ˈaʊtkʌm/ | That which is produced or occurs as a result of an event or process. |
| accountable adjective | /əˈkaʊntəbl̩/ | Obliged, when called upon, to answer (for one’s deeds); answerable. |
| reflect verb | /ɹɪˈflɛkt/ | To bend back (light, etc.) from a surface. |
| ethics noun | /ˈɛθ.ɪks/ | Morality. |
| audit noun | /ˈɔːdɪt/ | A judicial examination. |
| govern verb | /ˈɡʌv.ən/ | To make and administer the public policy and affairs of; to exercise sovereign authority in. |
| sheet noun | /ʃit/ | A thin bed cloth used as a covering for a mattress or as a layer over the sleeper. |
| regulation noun | /ˌɹɛɡjʊˈleɪʃən/ | The act of regulating or the condition of being regulated. |
| requirement noun | /ɹɪˈkwaɪɹmənt/ | A necessity or prerequisite; something required or obligatory. Its adpositions are generally of in relation to who or what has given it, on in relation to whom… |
| inventory noun | /ˈɪn.vən.tɹi/ | The stock of an item on hand at a particular location or business. |
| dive verb | /daɪv/ | To swim under water. |
| governance noun | /ˈɡʌvəɹnəns/ | The process, or the power, of governing; government or administration. |
| transparent adjective | /tɹænsˈpæɹənt/ | See-through, clear; having the property that light passes through it almost undisturbed, such that one can see through it clearly. |
Phrasal verbs you will hear
| Word | Meaning |
|---|---|
| fall through verb | To be unsuccessful, come to nothing; to be cancelled; not to proceed. |
| think through verb | To fully consider, so as to understand all the consequences of. |
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 | be reflected · be approached · is required |
| Relative clauses who / which + clause — extra information about a person or thing | one, which is · those who are · those who will |
Pronunciation to watch
The speaker uses 3 contractions and reduced forms, such as don't, I'm, you're. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: artificial /ˌɑː.tɪˈfɪʃ.əl/, sheet /ʃit/, regulation /ˌɹɛɡjʊˈleɪʃən/, recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, associate /əˈsoʊʃi.ət/
- Long words — get the stress right: artificial /ˌɑː.tɪˈfɪʃ.əl/, regulation /ˌɹɛɡjʊˈleɪʃən/, literacy /ˈlɪtəɹəsi/, recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, associate /əˈsoʊʃi.ət/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like behalf, artificial, outcome.
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.