Prática de Shadowing: What is AI Ethics? - Aprenda a falar inglês com vídeo
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I want to start off with talking to you about three things that keep me up at night, right?
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Three things.
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The first, and it may be, you know, very common for you too, is climate change.
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Climate change absolutely keeps me up at night.
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The second thing that keeps me up at night is
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that people may have no idea that an artificial intelligence is making a decision
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that directly impacts their lives what percentage interest rate you get on your loan,
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whether you get that job that you applied for, whether your kid gets into that college that they really want to go to.
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Today, AI is making decisions that directly impact you.
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The third thing that keeps me up at night is even
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when When people know that an AI is making a decision about them,
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they may assume that because it's not a fallible human with bias,
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that somehow the AI is going to make a decision that's morally or ethically squeaky clean.
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And that could not be farther from the truth.
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So if you think about organizations and what happens, over 80 % of the time,
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proof of concepts associated with artificial intelligence actually gets stalled in testing.
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And more often than not, it is because people do not trust the results from that AI model.
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So we're going to talk a lot about trust.
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And when thinking about trust, I'm going to switch colors here, there's actually five pillars.
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Okay.
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When you're thinking about what does it take to earn trust in an artificial intelligence
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that's being made by your organization or being procured by your organization, five pillars.
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The first thing to be thinking about is fairness.
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How can you ensure that the AI model is fair towards everybody, in particular historically underrepresented groups?
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The second is explainable.
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Is your AI model explainable such that you'd be able to tell somebody,
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an end user, what data sets were being used in order to curate that model?
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What methods, what expertise, what's the data lineage and provenance associated with how that model was trained?
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The third, robustness.
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Can you assure end users that nobody can hack such an AI model such
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that a person could disadvantage willfully other people and or make the results of that model benefit one particular person over another?
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The fourth is transparency.
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Are you telling people right off the bat than the AI model is indeed being used to make that decision?
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And are you giving people access to a fact sheet or metadata so that they can learn more about that model?
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And the fifth one is, are you assuring people's data privacy?
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So those are the five pillars, okay?
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Now, IBM has come up with three principles when thinking about AI in an organization.
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The first being that the purpose of artificial intelligence is really meant to be to augment human intelligence,
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not to replace it.
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The second is that data and the insights from those data belong to the creator alone.
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And the third is that AI systems, and I would opine the entire AI lifecycle, really should be transparent and explainable right?
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So those are the five pillars.
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Now, the next thing I want you to remember
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as you're thinking about this space of earning trust and artificial intelligence is that this is not a technological challenge.
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It can't be solved with just throwing tools and tech over some kind of a fence.
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This is a socio -technological challenge.
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Socio meaning people, people, people.
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Socio -technological challenge.
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And because it's a socio -technological challenge.
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It must be addressed holistically, okay?
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Holistically, meaning there's three major things that you should think about.
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I mentioned people, people, the culture of your organization, right?
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Thinking about the diversity of your teams, you know, your data science team who is curating that data to train that model.
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How many women are on that team?
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How many minorities are on that team, right?
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Think about diversity.
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I don't know if you've ever heard of the the wisdom of crowns that's actually a proven mathematical theory
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The more diverse your group of people, the less chance for error.
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And that is absolutely true in the realm of artificial intelligence.
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The second thing is process or governance, right?
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What is it that you as your organization,
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what are you going to promise both your employees as well as the market with respect to what standards
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you're going to stand by for your AI model in terms of things like fairness and explainability, accountability, etc., right?
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And the third area is tooling, right?
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What are the tools, AI engineering,
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methods, frameworks that you can use in order to ensure these things, ensure those five pillars?
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And we're going to do a deep dive into that as well.
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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.
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So stay tuned.
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If you like this video and series, please comment below.
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Stay tuned for more videos that are part of this series.
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And to get updates, please like and subscribe.
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Vocabulário e dicas de fala para esta lição
Esta aula de conversação usa o vídeo “What is AI Ethics?”. As palavras que mais se repetem: model, intelligence, artificial, organization, thinking. Este vídeo tem 21 frases e 859 palavras para praticar shadowing. A fala dura 6:09. O falante mantém um ritmo estável de cerca de 140 palavras por minuto, confortável para o shadowing. 86% das palavras estão entre as 3.000 mais comuns do inglês; vale a pena estudar o restante antes de começar. 13 das 21 frases são perguntas, por isso é um bom treino para a entonação de perguntas e respostas curtas.
Vocabulário principal deste vídeo
15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| artificial adjetivo | /ˌɑː.tɪˈfɪʃ.əl/ | artificial |
| pillar substantivo | /ˈpɪlɚ/ | pilar |
| technological adjetivo | /ˌtɛk.nəˈlɑ.dʒɪ.kəl/ | tecnológico |
| earn verbo | /ɝn/ | receber |
| associate substantivo | /əˈsoʊʃi.ət/ | associado |
| diversity substantivo | /daɪˈvɜː(ɹ)sɪti/ | diversidade |
| tune substantivo | /tjuːn/ | melodia |
| assure verbo | /əˈʃʊɹ/ | assegurar |
| fairness substantivo | /ˈfɛɚ.nəs/ | imparcialidade |
| percentage substantivo | /pə(ɹ)ˈsɛntɪd͡ʒ/ | percentagem, porcentagem |
| solve verbo | /sɒlv/ | resolver, solucionar |
| minority substantivo | /maɪˈnɒɹ.ɪ.ti/ | menoridade |
| sheet substantivo | /ʃit/ | folha |
| belong verbo | /bɪˈlɒŋ/ | pertencer |
| privacy substantivo | /ˈpɹaɪ.və.si/ | intimidade, privacidade |
Phrasal verbs que você vai ouvir
| Palavra | Significado |
|---|---|
| come up with verbo | pensar (em), alcançar |
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | being made · was trained · is really meant |
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | has come · you've ever heard |
Pronúncia para ficar de olho
O falante usa 13 contrações e formas reduzidas, como we're, you're, I'm. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “th”: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
- Os sons de “sh” e “zh”: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
- Palavras longas — acerte a sílaba tônica: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/
Sons difíceis para falantes de português:
- Consoante final — sem acrescentar um “i” depois: associate /əˈsoʊʃi.ət/, sheet /ʃit/, framework /ˈfɹeɪm.wɜːk/, insight /ˈɪnsaɪ̯t/, subscribe /səbˈskɹaɪb/
- /l/ final — a língua toca o céu da boca, não vira “u”: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, principle /ˈpɹɪn.sɪ.pəl/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, stall /stɔːl/
- s + consoante no início — sem um “i” antes: stall /stɔːl/, squeaky /ˈskwiːki/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Faça shadowing frase por frase na velocidade normal, repetindo cada uma até o seu ritmo ficar igual ao do falante.
- Grave a sua voz e compare com o original, prestando atenção a palavras como artificial, pillar, technological.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.