Shadowing Practice: What is AI Ethics? - Learn English Speaking with Video
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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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Vocabulario y notas de pronunciación para esta lección
Esta lección de conversación se basa en el vídeo “What is AI Ethics?”. Las palabras que más se repiten: model, intelligence, artificial, organization, thinking. Este vídeo tiene 21 frases y 859 palabras para practicar shadowing. La parte hablada dura 6:09. El hablante mantiene un ritmo constante de unas 140 palabras por minuto, cómodo para el shadowing. El 86 % de las palabras está entre las 3.000 más comunes del inglés; conviene repasar el resto antes de empezar. 13 de sus 21 frases son preguntas, así que sirve para practicar la entonación de las preguntas y las respuestas cortas.
Vocabulario clave de este vídeo
15 palabras del vídeo que vale la pena aprender, con su pronunciación y significado:
| Palabra | Pronunciación | Significado |
|---|---|---|
| artificial adjetivo | /ˌɑː.tɪˈfɪʃ.əl/ | artificial |
| pillar sustantivo | /ˈpɪlɚ/ | pilar |
| technological adjetivo | /ˌtɛk.nəˈlɑ.dʒɪ.kəl/ | tecnológico |
| earn verbo | /ɝn/ | ganar, granjear |
| associate sustantivo | /əˈsoʊʃi.ət/ | asociado |
| diversity sustantivo | /daɪˈvɜː(ɹ)sɪti/ | diversidad |
| tune sustantivo | /tjuːn/ | melodía |
| assure verbo | /əˈʃʊɹ/ | asegurar, afianzar |
| fairness sustantivo | /ˈfɛɚ.nəs/ | imparcialidad, equidad |
| percentage sustantivo | /pə(ɹ)ˈsɛntɪd͡ʒ/ | porcentaje |
| solve verbo | /sɒlv/ | resolver, solucionar |
| minority sustantivo | /maɪˈnɒɹ.ɪ.ti/ | minoría |
| sheet sustantivo | /ʃit/ | hoja, folio |
| belong verbo | /bɪˈlɒŋ/ | pertenecer [with en or a] |
| privacy sustantivo | /ˈpɹaɪ.və.si/ | intimidad, privacidad |
Phrasal verbs que vas a escuchar
| Palabra | Significado |
|---|---|
| come up with verbo | ingeniar, ingeniarse |
Gramática en este vídeo
Las estructuras que más usa el hablante, con las palabras exactas del vídeo:
| Estructura | En el vídeo |
|---|---|
| Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace | being made · was trained · is really meant |
| Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora | has come · you've ever heard |
Pronunciación a tener en cuenta
El hablante usa 13 contracciones y formas reducidas, como we're, you're, I'm. Dilas en su forma corta, tal como las oyes.
- Los sonidos de “th”: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
- Los sonidos de “sh” y “zh”: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
- Palabras largas — cuida el acento: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/
Sonidos difíciles para hispanohablantes:
- s + consonante al inicio — sin añadir una “e” delante: stall /stɔːl/, squeaky /ˈskwiːki/
- /v/ — no es /b/: los dientes tocan el labio inferior: diversity /daɪˈvɜː(ɹ)sɪti/, solve /sɒlv/, privacy /ˈpɹaɪ.və.si/, diverse /daɪˈvɜːs/, dive /daɪv/
- /z/ — sonora, no /s/: wisdom /ˈwɪzdəm/, expertise /ˌɛkspɜːˈtiːz/
Cómo practicar con este vídeo
- Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
- Haz shadowing frase por frase a velocidad normal, repitiendo cada una hasta que tu ritmo coincida con el del hablante.
- Grábate y compara con el original, prestando atención a palabras como artificial, pillar, technological.
¿Qué es la Técnica de Shadowing?
Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.