Pratique du Shadowing: What is AI Ethics? - Apprendre l'anglais à l'oral avec la vidéo
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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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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale s’appuie sur la vidéo « What is AI Ethics? ». Les mots qui reviennent le plus souvent : model, intelligence, artificial, organization, thinking. Cette vidéo contient 21 phrases et 859 mots à répéter en shadowing. La partie parlée dure 6:09. Le locuteur parle à un rythme régulier d’environ 140 mots par minute, confortable pour le shadowing. 86 % des mots font partie des 3 000 mots les plus courants en anglais ; le reste mérite d’être vu avant de commencer. 13 de ses 21 phrases sont des questions : un bon entraînement pour l’intonation interrogative et les réponses courtes.
Vocabulaire clé de cette vidéo
15 mots de la vidéo à retenir, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| artificial adjectif | /ˌɑː.tɪˈfɪʃ.əl/ | artificiel |
| pillar nom | /ˈpɪlɚ/ | pilier, pile |
| technological adjectif | /ˌtɛk.nəˈlɑ.dʒɪ.kəl/ | technologique |
| earn verbe | /ɝn/ | gagner |
| diversity nom | /daɪˈvɜː(ɹ)sɪti/ | diversité |
| tune nom | /tjuːn/ | mélodie, air |
| assure verbe | /əˈʃʊɹ/ | assurer |
| fairness nom | /ˈfɛɚ.nəs/ | justice |
| percentage nom | /pə(ɹ)ˈsɛntɪd͡ʒ/ | pourcentage |
| solve verbe | /sɒlv/ | résoudre, régler |
| minority nom | /maɪˈnɒɹ.ɪ.ti/ | minorité |
| sheet nom | /ʃit/ | feuille |
| belong verbe | /bɪˈlɒŋ/ | avoir sa place |
| privacy nom | /ˈpɹaɪ.və.si/ | intimité, vie privée |
| principle nom | /ˈpɹɪn.sɪ.pəl/ | principe |
Les verbes à particule que vous entendrez
| Mot | Sens |
|---|---|
| come up with verbe | inventer, imaginer |
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | being made · was trained · is really meant |
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | has come · you've ever heard |
Prononciation à surveiller
Le locuteur utilise 13 contractions et formes réduites, comme we're, you're, I'm. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « th »: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
- Les sons « sh » et « zh »: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
- Mots longs — placez bien l’accent: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/
Les sons difficiles pour les francophones :
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, percentage /pə(ɹ)ˈsɛntɪd͡ʒ/, disadvantage /ˌdɪsədˈvæntɪd͡ʒ/, lineage /ˈlɪn.i.ɪd͡ʒ/
- /r/ anglais — langue recourbée, sans frotter la gorge: diversity /daɪˈvɜː(ɹ)sɪti/, assure /əˈʃʊɹ/, percentage /pə(ɹ)ˈsɛntɪd͡ʒ/, minority /maɪˈnɒɹ.ɪ.ti/, privacy /ˈpɹaɪ.və.si/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Répétez phrase par phrase à vitesse normale, en reprenant chacune jusqu’à ce que votre rythme corresponde à celui du locuteur.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme artificial, pillar, technological.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.