跟读练习: What is AI Ethics? - 通过视频学习英语口语

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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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本课的词汇与口语要点

这节口语课以视频“What is AI Ethics?”为素材。视频中反复出现的词有：model, intelligence, artificial, organization, thinking。这段视频共有 21 个句子、859 个单词可供跟读。讲话部分时长为 6:09。说话人语速平稳，每分钟约 140 个词，很适合跟读。 86% 的单词属于英语最常用的 3,000 词，其余的词建议在练习前先学一下。全部 21 个句子中有 13 个是问句，适合练习疑问语调和简短回答。

视频中的重点词汇

视频中 15 个值得学习的单词，附发音和释义：

单词	发音	释义
artificial 形容词	/ˌɑː.tɪˈfɪʃ.əl/	人造的
pillar 名词	/ˈpɪlɚ/	柱子, 支柱
technological 形容词	/ˌtɛk.nəˈlɑ.dʒɪ.kəl/	技術性 /技术性
earn 动词	/ɝn/	掙 /挣, 賺 /赚
associate 名词	/əˈsoʊʃi.ət/	同行, 同事
diversity 名词	/daɪˈvɜː(ɹ)sɪti/	多樣性 /多样性, 變化 /变化
tune 名词	/tjuːn/	曲調 /曲调, 曲子
percentage 名词	/pə(ɹ)ˈsɛntɪd͡ʒ/	百分率, 百分比
solve 动词	/sɒlv/	解決 /解决
minority 名词	/maɪˈnɒɹ.ɪ.ti/	少數 /少数
sheet 名词	/ʃit/	篇, 張 /张
belong 动词	/bɪˈlɒŋ/	屬於 /属于
privacy 名词	/ˈpɹaɪ.və.si/	隱私 /隐私
principle 名词	/ˈpɹɪn.sɪ.pəl/	原理, 原則 /原则
wisdom 名词	/ˈwɪzdəm/	智慧

视频中出现的短语动词

单词	释义
come up with 动词	想出, 提出

视频中的语法

说话人最常用的结构，并附上视频中的原话：

结构	视频中的用法
被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的	being made · was trained · is really meant
现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事	has come · you've ever heard

需要注意的发音

说话人用了 13 次缩略和弱读形式，例如 we're, you're, I'm。请按听到的简短形式来说。

“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/
长单词——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/

中文母语者容易读错的音：

/v/ — 不要读成 /w/，上齿轻触下唇: diversity /daɪˈvɜː(ɹ)sɪti/, solve /sɒlv/, privacy /ˈpɹaɪ.və.si/, diverse /daɪˈvɜːs/, dive /daɪv/
词尾辅音 — 要读清楚，后面不要加元音: associate /əˈsoʊʃi.ət/, fairness /ˈfɛɚ.nəs/, percentage /pə(ɹ)ˈsɛntɪd͡ʒ/, solve /sɒlv/, sheet /ʃit/
/r/ 和 /l/ 的区别: principle /ˈpɹɪn.sɪ.pəl/, historically /hɪˈstɔɹɪkli/, morally /ˈmɔɹəli/

如何用这段视频练习

先完整听一遍视频，不要开口，记下不认识的单词。
用正常速度逐句跟读，每句重复到你的节奏与说话人一致为止。
录下自己的声音并与原声对比，特别注意 artificial, pillar, technological 这类单词。

什么是跟读法？

跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。

影子跟读法: 阅读完整分步指南 →

跟读练习: What is AI Ethics? - 通过视频学习英语口语

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