쉐도잉 연습: What is AI Ethics? - 영상으로 영어 말하기 배우기

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I want to start off with talking to you about three things that keep me up at night, right?

Three things.

The first, and it may be, you know, very common for you too, is climate change.

Climate change absolutely keeps me up at night.

The second thing that keeps me up at night is

that people may have no idea that an artificial intelligence is making a decision

that directly impacts their lives what percentage interest rate you get on your loan,

whether you get that job that you applied for, whether your kid gets into that college that they really want to go to.

Today, AI is making decisions that directly impact you.

The third thing that keeps me up at night is even

when When people know that an AI is making a decision about them,

they may assume that because it's not a fallible human with bias,

that somehow the AI is going to make a decision that's morally or ethically squeaky clean.

And that could not be farther from the truth.

So if you think about organizations and what happens, over 80 % of the time,

proof of concepts associated with artificial intelligence actually gets stalled in testing.

And more often than not, it is because people do not trust the results from that AI model.

So we're going to talk a lot about trust.

And when thinking about trust, I'm going to switch colors here, there's actually five pillars.

Okay.

When you're thinking about what does it take to earn trust in an artificial intelligence

that's being made by your organization or being procured by your organization, five pillars.

The first thing to be thinking about is fairness.

How can you ensure that the AI model is fair towards everybody, in particular historically underrepresented groups?

The second is explainable.

Is your AI model explainable such that you'd be able to tell somebody,

an end user, what data sets were being used in order to curate that model?

What methods, what expertise, what's the data lineage and provenance associated with how that model was trained?

The third, robustness.

Can you assure end users that nobody can hack such an AI model such

that a person could disadvantage willfully other people and or make the results of that model benefit one particular person over another?

The fourth is transparency.

Are you telling people right off the bat than the AI model is indeed being used to make that decision?

And are you giving people access to a fact sheet or metadata so that they can learn more about that model?

And the fifth one is, are you assuring people's data privacy?

So those are the five pillars, okay?

Now, IBM has come up with three principles when thinking about AI in an organization.

The first being that the purpose of artificial intelligence is really meant to be to augment human intelligence,

not to replace it.

The second is that data and the insights from those data belong to the creator alone.

And the third is that AI systems, and I would opine the entire AI lifecycle, really should be transparent and explainable right?

So those are the five pillars.

Now, the next thing I want you to remember

as you're thinking about this space of earning trust and artificial intelligence is that this is not a technological challenge.

It can't be solved with just throwing tools and tech over some kind of a fence.

This is a socio -technological challenge.

Socio meaning people, people, people.

Socio -technological challenge.

And because it's a socio -technological challenge.

It must be addressed holistically, okay?

Holistically, meaning there's three major things that you should think about.

I mentioned people, people, the culture of your organization, right?

Thinking about the diversity of your teams, you know, your data science team who is curating that data to train that model.

How many women are on that team?

How many minorities are on that team, right?

Think about diversity.

I don't know if you've ever heard of the the wisdom of crowns that's actually a proven mathematical theory

The more diverse your group of people, the less chance for error.

And that is absolutely true in the realm of artificial intelligence.

The second thing is process or governance, right?

What is it that you as your organization,

what are you going to promise both your employees as well as the market with respect to what standards

you're going to stand by for your AI model in terms of things like fairness and explainability, accountability, etc., right?

And the third area is tooling, right?

What are the tools, AI engineering,

methods, frameworks that you can use in order to ensure these things, ensure those five pillars?

And we're going to do a deep dive into that as well.

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.

So stay tuned.

If you like this video and series, please comment below.

Stay tuned for more videos that are part of this series.

And to get updates, please like and subscribe.

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이 레슨의 어휘와 말하기 포인트

이 말하기 레슨은 영상 “What is AI Ethics?”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: model, intelligence, artificial, organization, thinking. 이 영상에는 섀도잉할 문장 21개와 단어 859개가 있습니다. 말하는 구간의 길이는 6:09입니다. 화자는 분당 약 140단어의 일정한 속도로 말해서 섀도잉하기에 편한 속도입니다. 단어의 86%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요. 전체 21문장 중 13문장이 질문이어서 의문문 억양과 짧은 대답을 연습하기에 좋습니다.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
artificial 형용사	/ˌɑː.tɪˈfɪʃ.əl/	인공, 인조
pillar 명사	/ˈpɪlɚ/	기둥
technological 형용사	/ˌtɛk.nəˈlɑ.dʒɪ.kəl/	기술
earn 동사	/ɝn/	벌다
associate 명사	/əˈsoʊʃi.ət/	제휴자, 조합원
tune 명사	/tjuːn/	곡, 곡조
percentage 명사	/pə(ɹ)ˈsɛntɪd͡ʒ/	백분율, 백분비
solve 동사	/sɒlv/	해결하다
minority 명사	/maɪˈnɒɹ.ɪ.ti/	미성년
sheet 명사	/ʃit/	장, 시트
belong 동사	/bɪˈlɒŋ/	속하다
privacy 명사	/ˈpɹaɪ.və.si/	사생활, 프라이버시
principle 명사	/ˈpɹɪn.sɪ.pəl/	원리, 원칙
wisdom 명사	/ˈwɪzdəm/	지혜, 슬기
creator 명사	/kɹiːˈeɪ̯təː/	조물주

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	being made · was trained · is really meant
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	has come · you've ever heard

주의할 발음

화자는 we're, you're, I'm 같은 축약형과 약화된 형태를 13번 사용합니다. 들리는 대로 짧게 발음하세요.

“th” 소리: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
“sh”와 “zh” 소리: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
긴 단어 — 강세 위치에 주의: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: artificial /ˌɑː.tɪˈfɪʃ.əl/, fairness /ˈfɛɚ.nəs/, framework /ˈfɹeɪm.wɜːk/, fence /fɛns/, fallible /ˈfal.ɪ.bəl/
/v/ — ㅂ(/b/)과 구별하기: diversity /daɪˈvɜː(ɹ)sɪti/, solve /sɒlv/, privacy /ˈpɹaɪ.və.si/, diverse /daɪˈvɜːs/, dive /daɪv/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: wisdom /ˈwɪzdəm/, expertise /ˌɛkspɜːˈtiːz/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
보통 속도로 한 문장씩 섀도잉하고, 화자의 리듬과 맞을 때까지 반복합니다.
자신의 목소리를 녹음해 원본과 비교하고, artificial, pillar, technological 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

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쉐도잉 연습: What is AI Ethics? - 영상으로 영어 말하기 배우기

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