Shadowing Practice: What is AI Ethics? - Learn English Speaking with Video
Kies een oefenmodus
Elke modus opent een volledig scherm oefenruimte.
72 zinnen
Als zinnen te kort of te lang zijn, klik op Bewerken om ze aan te passen.
1
I want to start off with talking to you about three things that keep me up at night, right?
2
Three things.
3
The first, and it may be, you know, very common for you too, is climate change.
4
Climate change absolutely keeps me up at night.
5
The second thing that keeps me up at night is
6
that people may have no idea that an artificial intelligence is making a decision
7
that directly impacts their lives what percentage interest rate you get on your loan,
8
whether you get that job that you applied for, whether your kid gets into that college that they really want to go to.
9
Today, AI is making decisions that directly impact you.
10
The third thing that keeps me up at night is even
11
when When people know that an AI is making a decision about them,
12
they may assume that because it's not a fallible human with bias,
13
that somehow the AI is going to make a decision that's morally or ethically squeaky clean.
14
And that could not be farther from the truth.
15
So if you think about organizations and what happens, over 80 % of the time,
16
proof of concepts associated with artificial intelligence actually gets stalled in testing.
17
And more often than not, it is because people do not trust the results from that AI model.
18
So we're going to talk a lot about trust.
19
And when thinking about trust, I'm going to switch colors here, there's actually five pillars.
20
Okay.
21
When you're thinking about what does it take to earn trust in an artificial intelligence
22
that's being made by your organization or being procured by your organization, five pillars.
23
The first thing to be thinking about is fairness.
24
How can you ensure that the AI model is fair towards everybody, in particular historically underrepresented groups?
25
The second is explainable.
26
Is your AI model explainable such that you'd be able to tell somebody,
27
an end user, what data sets were being used in order to curate that model?
28
What methods, what expertise, what's the data lineage and provenance associated with how that model was trained?
29
The third, robustness.
30
Can you assure end users that nobody can hack such an AI model such
31
that a person could disadvantage willfully other people and or make the results of that model benefit one particular person over another?
32
The fourth is transparency.
33
Are you telling people right off the bat than the AI model is indeed being used to make that decision?
34
And are you giving people access to a fact sheet or metadata so that they can learn more about that model?
35
And the fifth one is, are you assuring people's data privacy?
36
So those are the five pillars, okay?
37
Now, IBM has come up with three principles when thinking about AI in an organization.
38
The first being that the purpose of artificial intelligence is really meant to be to augment human intelligence,
39
not to replace it.
40
The second is that data and the insights from those data belong to the creator alone.
41
And the third is that AI systems, and I would opine the entire AI lifecycle, really should be transparent and explainable right?
42
So those are the five pillars.
43
Now, the next thing I want you to remember
44
as you're thinking about this space of earning trust and artificial intelligence is that this is not a technological challenge.
45
It can't be solved with just throwing tools and tech over some kind of a fence.
46
This is a socio -technological challenge.
47
Socio meaning people, people, people.
48
Socio -technological challenge.
49
And because it's a socio -technological challenge.
50
It must be addressed holistically, okay?
51
Holistically, meaning there's three major things that you should think about.
52
I mentioned people, people, the culture of your organization, right?
53
Thinking about the diversity of your teams, you know, your data science team who is curating that data to train that model.
54
How many women are on that team?
55
How many minorities are on that team, right?
56
Think about diversity.
57
I don't know if you've ever heard of the the wisdom of crowns that's actually a proven mathematical theory
58
The more diverse your group of people, the less chance for error.
59
And that is absolutely true in the realm of artificial intelligence.
60
The second thing is process or governance, right?
61
What is it that you as your organization,
62
what are you going to promise both your employees as well as the market with respect to what standards
63
you're going to stand by for your AI model in terms of things like fairness and explainability, accountability, etc., right?
64
And the third area is tooling, right?
65
What are the tools, AI engineering,
66
methods, frameworks that you can use in order to ensure these things, ensure those five pillars?
67
And we're going to do a deep dive into that as well.
68
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.
69
So stay tuned.
70
If you like this video and series, please comment below.
71
Stay tuned for more videos that are part of this series.
72
And to get updates, please like and subscribe.
App downloaden
Alles wat je nodig hebt om vloeiend te spreken
AI UitspraakScoor elke zin
IPA OefeningBeheers elk geluid
WoordenschatBouw je woordenschat op
WoordenspelLeren terwijl je speelt
Zelfde Kanaal
B21
Every Generation Thinks This Time Is Different: The AI Job Myth
Daily ConversationWorking Adults
C12
What is Event Driven Architecture (EDA)?
Daily ConversationUniversity StudentsWorking Adults
C11
Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production
Daily ConversationUniversity StudentsWorking Adults
C18
AI, Machine Learning, Deep Learning and Generative AI Explained
Daily ConversationUniversity StudentsWorking Adults
C11
Spec-Driven Development: AI Assisted Coding Explained
Daily ConversationUniversity StudentsWorking Adults
C15
AI Agents vs Business Rules: Which Should Make Decisions?
Daily ConversationUniversity StudentsWorking Adults
C13
AI & Education: Generative AI & the Future of Critical Thinking
Business EnglishUniversity StudentsWorking Adults
C12
EDR vs. EPP vs. NGAV
Business EnglishWorking Adults
C13
MCP vs ADK: How Modern AI Agents Connect and Work Together
University StudentsWorking Adults
C11
Cybersecurity Architecture: Data Security
Business EnglishUniversity StudentsWorking Adults
C113
The 7 Skills You Need to Build AI Agents
Business EnglishUniversity StudentsWorking Adults
C14
What Is Jev? The AI Model That Doesn't Generate Text
Daily ConversationUniversity StudentsWorking Adults
C118
How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact
Business EnglishUniversity StudentsWorking Adults
C111
What are AI Agents?
Business EnglishUniversity StudentsWorking Adults
C16
Cybersecurity Architecture: Five Principles to Follow (and One to Avoid)
Business EnglishUniversity StudentsWorking Adults
C11
Zero Trust Explained in 4 mins
Business EnglishUniversity StudentsWorking Adults
C1What is Responsible AI? A Guide to AI Governance
Daily ConversationUniversity StudentsWorking Adults
FOR YOU
Aanbevolen Video
C11
105 zinnen
AI Governance Explained: Ethical, Transparent & Accountable AI for the Future
Daily ConversationUniversity StudentsWorking Adults
C141 zinnen
No, Really — Is A.I. a Bubble? | The Ezra Klein Show
Daily ConversationUniversity StudentsWorking Adults
C11
74 zinnen
AI & Inequality | Daron Acemoglu: Big Tech poses risks as AI reshapes society
Daily ConversationUniversity StudentsWorking Adults
C14
76 zinnen
Make AI Believe in God to Keep Humans Safe, Scientist Argues
Daily ConversationUniversity StudentsWorking Adults
C18
55 zinnen
When AIs act emotional
Daily ConversationUniversity StudentsWorking Adults
C11
114 zinnen
Harnessing generative AI in architecture, engineering & construction
Daily ConversationUniversity StudentsWorking Adults
C13
211 zinnen
IAM
Daily ConversationUniversity StudentsWorking Adults
C12
74 zinnen
How AI is helping brands identify consumer trends faster
Daily ConversationUniversity StudentsWorking Adults
C11
623 zinnen
The Only SaaS Moats Left in 2026
Daily ConversationUniversity StudentsWorking Adults
C14
106 zinnen
DPM Gan Kim Yong's Speech at the "Trust and AI: Navigating a World in Transition" Leaders Dialogue
Daily ConversationUniversity StudentsWorking Adults
Woordenschat en spreektips bij deze les
Deze spreekles is gebaseerd op de video “What is AI Ethics?”. Deze woorden komen het vaakst terug: model, intelligence, artificial, organization, thinking. Deze video bevat 21 zinnen en 859 woorden om na te spreken. Het gesproken deel duurt 6:09. De spreker praat in een gelijkmatig tempo van ongeveer 140 woorden per minuut, prettig om te shadowen. 86% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden; de rest kun je beter vooraf bekijken. 13 van de 21 zinnen zijn vragen, dus het is goede oefening voor vraagintonatie en korte antwoorden.
Belangrijke woorden in deze video
15 woorden uit de video die het leren waard zijn, met uitspraak en betekenis:
| Woord | Uitspraak | Betekenis |
|---|---|---|
| artificial bijvoeglijk naamwoord | /ˌɑː.tɪˈfɪʃ.əl/ | artificieel, kunstmatig |
| pillar zelfstandig naamwoord | /ˈpɪlɚ/ | pijler, zuil |
| technological bijvoeglijk naamwoord | /ˌtɛk.nəˈlɑ.dʒɪ.kəl/ | technologisch |
| earn werkwoord | /ɝn/ | verdienen |
| diversity zelfstandig naamwoord | /daɪˈvɜː(ɹ)sɪti/ | verscheidenheid, diversiteit |
| tune zelfstandig naamwoord | /tjuːn/ | melodie |
| assure werkwoord | /əˈʃʊɹ/ | verzekeren, afzekeren |
| percentage zelfstandig naamwoord | /pə(ɹ)ˈsɛntɪd͡ʒ/ | percentage |
| solve werkwoord | /sɒlv/ | oplossen |
| minority zelfstandig naamwoord | /maɪˈnɒɹ.ɪ.ti/ | minderheid |
| sheet zelfstandig naamwoord | /ʃit/ | blad |
| belong werkwoord | /bɪˈlɒŋ/ | thuishoren, horen |
| privacy zelfstandig naamwoord | /ˈpɹaɪ.və.si/ | afzondering, privacy |
| principle zelfstandig naamwoord | /ˈpɹɪn.sɪ.pəl/ | principe, beginsel |
| wisdom zelfstandig naamwoord | /ˈwɪzdəm/ | wijsheid |
Phrasal verbs die je zult horen
| Woord | Betekenis |
|---|---|
| come up with werkwoord | bedenken, verzinnen |
Grammatica in deze video
De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:
| Structuur | In de video |
|---|---|
| Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet | being made · was trained · is really meant |
| Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt | has come · you've ever heard |
Uitspraak om op te letten
De spreker gebruikt 13 samentrekkingen en verkorte vormen, zoals we're, you're, I'm. Spreek ze kort uit, zoals je ze hoort.
- De “th”-klanken: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
- De klanken “sh” en “zh”: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
- Lange woorden — let op de klemtoon: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/
Klanken die Nederlandstaligen lastig vinden:
- Stemhebbende eindmedeklinker — /b/, /d/, /g/, /z/, /v/ niet verscherpen: percentage /pə(ɹ)ˈsɛntɪd͡ʒ/, solve /sɒlv/, dive /daɪv/, expertise /ˌɛkspɜːˈtiːz/, subscribe /səbˈskɹaɪb/
- /æ/ — opener dan de Nederlandse “e”: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, transparent /tɹænsˈpæɹənt/, transparency /tɹænsˈpæɹənsi/, disadvantage /ˌdɪsədˈvæntɪd͡ʒ/
- /g/ — een harde plofklank, geen Nederlandse “g”: governance /ˈɡʌvəɹnəns/, augment /ɔɡˈmɛnt/
Zo oefen je met deze video
- Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
- Spreek zin voor zin na op normale snelheid en herhaal elke zin tot je ritme gelijk is aan dat van de spreker.
- Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als artificial, pillar, technological.
Wat is de Shadowing-techniek?
Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.