シャドーイング練習: What is AI Ethics? - 動画で英語スピーキングを学ぶ

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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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このレッスンの語彙とスピーキングのポイント

このスピーキングレッスンは、動画「What is AI Ethics?」を教材にしています。繰り返し出てくる語は次のとおりです：model, intelligence, artificial, organization, thinking。この動画には、シャドーイング用の文が21文、単語が859語あります。音声の長さは6:09です。話す速さは1分あたり約140語で安定しており、シャドーイングしやすいペースです。単語の86%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。全21文のうち13文が疑問文なので、疑問文のイントネーションや短い受け答えの練習に向いています。

この動画の重要語彙

動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
artificial 形容詞	/ˌɑː.tɪˈfɪʃ.əl/	人工の
pillar 名詞	/ˈpɪlɚ/	柱
technological 形容詞	/ˌtɛk.nəˈlɑ.dʒɪ.kəl/	技術の
earn 動詞	/ɝn/	稼ぐ, 得る
associate 名詞	/əˈsoʊʃi.ət/	提携者
diversity 名詞	/daɪˈvɜː(ɹ)sɪti/	多様性, 変化
tune 名詞	/tjuːn/	曲, チューン
percentage 名詞	/pə(ɹ)ˈsɛntɪd͡ʒ/	百分率, 百分比
solve 動詞	/sɒlv/	解決する
minority 名詞	/maɪˈnɒɹ.ɪ.ti/	少数
sheet 名詞	/ʃit/	枚, シート
belong 動詞	/bɪˈlɒŋ/	所属する, 属する
privacy 名詞	/ˈpɹaɪ.və.si/	プライバシー, プライヴァシー
principle 名詞	/ˈpɹɪn.sɪ.pəl/	原理, 原則
wisdom 名詞	/ˈwɪzdəm/	知恵

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	being made · was trained · is really meant
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	has come · you've ever heard

注意したい発音

話し手はwe're, you're, I'mなど、短縮形や弱形を13回使っています。聞こえたとおりの短い形で発音しましょう。

「th」の音: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
「sh」と「zh」の音: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
長い単語（アクセントの位置に注意）: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: principle /ˈpɹɪn.sɪ.pəl/, historically /hɪˈstɔɹɪkli/, morally /ˈmɔɹəli/
/v/ — /b/ にならないように、上の歯を下唇に当てる: diversity /daɪˈvɜː(ɹ)sɪti/, solve /sɒlv/, privacy /ˈpɹaɪ.və.si/, diverse /daɪˈvɜːs/, dive /daɪv/
/f/ — 「フ」ではなく、上の歯と下唇で出す: artificial /ˌɑː.tɪˈfɪʃ.əl/, fairness /ˈfɛɚ.nəs/, framework /ˈfɹeɪm.wɜːk/, fence /fɛns/, fallible /ˈfal.ɪ.bəl/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
通常の速度で一文ずつシャドーイングし、話し手のリズムに合うまで繰り返します。
自分の声を録音して元の音声と比べます。artificial, pillar, technologicalなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: What is AI Ethics? - 動画で英語スピーキングを学ぶ

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