Shadowing-Übung: What is AI Ethics? - Englisch Sprechen Lernen mit 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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Wortschatz und Sprechhinweise zu dieser Lektion
Diese Sprechübung basiert auf dem Video „What is AI Ethics?“. Diese Wörter kommen am häufigsten vor: model, intelligence, artificial, organization, thinking. Dieses Video enthält 21 Sätze und 859 Wörter zum Nachsprechen. Der gesprochene Teil dauert 6:09. Der Sprecher spricht gleichmäßig mit etwa 140 Wörtern pro Minute – ein angenehmes Tempo fürs Shadowing. 86 % der Wörter gehören zu den 3.000 häufigsten im Englischen; den Rest solltest du dir vor dem Üben ansehen. 13 der 21 Sätze sind Fragen – gut, um Frageintonation und kurze Antworten zu üben.
Wichtiger Wortschatz in diesem Video
15 lernenswerte Wörter aus dem Video, mit Aussprache und Bedeutung:
| Wort | Aussprache | Bedeutung |
|---|---|---|
| artificial Adjektiv | /ˌɑː.tɪˈfɪʃ.əl/ | künstlich |
| pillar Substantiv | /ˈpɪlɚ/ | Pfeiler, Säule |
| technological Adjektiv | /ˌtɛk.nəˈlɑ.dʒɪ.kəl/ | technologisch |
| earn Verb | /ɝn/ | verdienen |
| associate Substantiv | /əˈsoʊʃi.ət/ | Weggefährte |
| diversity Substantiv | /daɪˈvɜː(ɹ)sɪti/ | Vielfältigkeit, Mannigfaltigkeit |
| tune Substantiv | /tjuːn/ | Melodie |
| assure Verb | /əˈʃʊɹ/ | sichern |
| percentage Substantiv | /pə(ɹ)ˈsɛntɪd͡ʒ/ | Prozentsatz, Hundertsatz |
| solve Verb | /sɒlv/ | lösen |
| minority Substantiv | /maɪˈnɒɹ.ɪ.ti/ | Minderheit, Minorität |
| sheet Substantiv | /ʃit/ | Blatt, Bogen |
| belong Verb | /bɪˈlɒŋ/ | hingehören, dazugehören |
| privacy Substantiv | /ˈpɹaɪ.və.si/ | Privatsphäre, Ungestörtheit |
| principle Substantiv | /ˈpɹɪn.sɪ.pəl/ | Grundsatz, Prinzip |
Phrasal Verbs, die du hören wirst
| Wort | Bedeutung |
|---|---|
| come up with Verb | ausdenken |
Grammatik in diesem Video
Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:
| Struktur | Im Video |
|---|---|
| Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut | being made · was trained · is really meant |
| Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist | has come · you've ever heard |
Aussprache, auf die du achten solltest
Der Sprecher verwendet 13 Kurzformen und abgeschwächte Formen, zum Beispiel we're, you're, I'm. Sprich sie in der kurzen Form, so wie du sie hörst.
- Die „th“-Laute: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
- Die Laute „sh“ und „zh“: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
- Lange Wörter – auf die Betonung achten: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/
Laute, die Deutschsprachigen schwerfallen:
- /w/ — mit runden Lippen, nicht wie das deutsche „w“ (/v/): wisdom /ˈwɪzdəm/, framework /ˈfɹeɪm.wɜːk/, squeaky /ˈskwiːki/, wilfully /ˈwɪlfəli/
- Stimmhafter Auslaut — /b/, /d/, /g/, /z/, /v/ am Wortende nicht verhärten: percentage /pə(ɹ)ˈsɛntɪd͡ʒ/, solve /sɒlv/, dive /daɪv/, expertise /ˌɛkspɜːˈtiːz/, subscribe /səbˈskɹaɪb/
- /æ/ — offener als „ä“: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, transparent /tɹænsˈpæɹənt/, transparency /tɹænsˈpæɹənsi/, disadvantage /ˌdɪsədˈvæntɪd͡ʒ/
So übst du mit diesem Video
- Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
- Sprich Satz für Satz in normaler Geschwindigkeit nach und wiederhole jeden, bis dein Rhythmus zum Sprecher passt.
- Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie artificial, pillar, technological.
Was ist die Shadowing-Technik?
Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.