跟讀練習: What is AI Ethics? - 通過影片學習英語口語

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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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FOR YOU

本課的詞彙與口語要點

這節口語課以影片“What is AI Ethics?”為素材。影片中反覆出現的詞有：model, intelligence, artificial, organization, thinking。這段影片共有 21 個句子、859 個單詞可供跟讀。講話部分時長為 6:09。說話人語速平穩，每分鐘約 140 個詞，很適合跟讀。 86% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。全部 21 個句子中有 13 個是問句，適合練習疑問語調和簡短回答。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
artificial 形容詞	/ˌɑː.tɪˈfɪʃ.əl/	人造的
pillar 名詞	/ˈpɪlɚ/	柱子, 支柱
technological 形容詞	/ˌtɛk.nəˈlɑ.dʒɪ.kəl/	技術性 /技術性
earn 動詞	/ɝn/	掙 /掙, 賺 /賺
associate 名詞	/əˈsoʊʃi.ət/	同行, 同事
diversity 名詞	/daɪˈvɜː(ɹ)sɪti/	多樣性 /多樣性, 變化 /變化
tune 名詞	/tjuːn/	曲調 /曲調, 曲子
percentage 名詞	/pə(ɹ)ˈsɛntɪd͡ʒ/	百分率, 百分比
solve 動詞	/sɒlv/	解決 /解決
minority 名詞	/maɪˈnɒɹ.ɪ.ti/	少數 /少數
sheet 名詞	/ʃit/	篇, 張 /張
belong 動詞	/bɪˈlɒŋ/	屬於 /屬於
privacy 名詞	/ˈpɹaɪ.və.si/	隱私 /隱私
principle 名詞	/ˈpɹɪn.sɪ.pəl/	原理, 原則 /原則
wisdom 名詞	/ˈwɪzdəm/	智慧

影片中出現的短語動詞

單詞	釋義
come up with 動詞	想出, 提出

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	being made · was trained · is really meant
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	has come · you've ever heard

需要注意的發音

說話人用了 13 次縮略和弱讀形式，例如 we're, you're, I'm。請按聽到的簡短形式來說。

“th” 音: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
“sh” 和 “zh” 音: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
長單詞——注意重音位置: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: diversity /daɪˈvɜː(ɹ)sɪti/, solve /sɒlv/, privacy /ˈpɹaɪ.və.si/, diverse /daɪˈvɜːs/, dive /daɪv/
詞尾子音 — 要讀清楚，後面不要加母音: associate /əˈsoʊʃi.ət/, fairness /ˈfɛɚ.nəs/, percentage /pə(ɹ)ˈsɛntɪd͡ʒ/, solve /sɒlv/, sheet /ʃit/
/r/ 和 /l/ 的區別: principle /ˈpɹɪn.sɪ.pəl/, historically /hɪˈstɔɹɪkli/, morally /ˈmɔɹəli/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
用正常速度逐句跟讀，每句重複到你的節奏與說話人一致為止。
錄下自己的聲音並與原聲對比，特別注意 artificial, pillar, technological 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: What is AI Ethics? - 通過影片學習英語口語

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