Pratica di Shadowing: What is AI Ethics? - Impara a parlare inglese con i 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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Vocabolario e note di pronuncia per questa lezione
Questa lezione di conversazione si basa sul video “What is AI Ethics?”. Le parole che ritornano più spesso: model, intelligence, artificial, organization, thinking. Questo video contiene 21 frasi e 859 parole da ripetere con lo shadowing. Il parlato dura 6:09. Chi parla mantiene un ritmo costante di circa 140 parole al minuto, comodo per lo shadowing. Il 86% delle parole rientra nelle 3.000 più comuni dell’inglese; conviene studiare le altre prima di iniziare. 13 delle sue 21 frasi sono domande: un buon esercizio per l’intonazione interrogativa e le risposte brevi.
Vocaboli chiave di questo video
15 parole del video che vale la pena imparare, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| artificial aggettivo | /ˌɑː.tɪˈfɪʃ.əl/ | artificiale, artificioso |
| pillar sostantivo | /ˈpɪlɚ/ | pilastro |
| technological aggettivo | /ˌtɛk.nəˈlɑ.dʒɪ.kəl/ | tecnologico |
| earn verbo | /ɝn/ | guadagnare |
| diversity sostantivo | /daɪˈvɜː(ɹ)sɪti/ | diversità |
| tune sostantivo | /tjuːn/ | melodia |
| assure verbo | /əˈʃʊɹ/ | assicurare |
| fairness sostantivo | /ˈfɛɚ.nəs/ | equità, imparzialità |
| percentage sostantivo | /pə(ɹ)ˈsɛntɪd͡ʒ/ | percentuale |
| solve verbo | /sɒlv/ | risolvere |
| minority sostantivo | /maɪˈnɒɹ.ɪ.ti/ | minoranza |
| sheet sostantivo | /ʃit/ | foglio |
| privacy sostantivo | /ˈpɹaɪ.və.si/ | privatezza, intimità |
| principle sostantivo | /ˈpɹɪn.sɪ.pəl/ | principio |
| wisdom sostantivo | /ˈwɪzdəm/ | saggezza, senno |
I phrasal verb che sentirai
| Parola | Significato |
|---|---|
| come up with verbo | ideare, escogitare |
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | being made · was trained · is really meant |
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | has come · you've ever heard |
Pronuncia a cui fare attenzione
Chi parla usa 13 contrazioni e forme ridotte, come we're, you're, I'm. Pronunciale nella forma breve, così come le senti.
- I suoni “th”: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
- I suoni “sh” e “zh”: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
- Parole lunghe — attenzione all’accento: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/
I suoni difficili per chi parla italiano:
- Consonante finale — senza aggiungere una vocale dopo: associate /əˈsoʊʃi.ət/, fairness /ˈfɛɚ.nəs/, percentage /pə(ɹ)ˈsɛntɪd͡ʒ/, solve /sɒlv/, sheet /ʃit/
- /æ/ — più aperta della “e”: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, transparent /tɹænsˈpæɹənt/, transparency /tɹænsˈpæɹənsi/, disadvantage /ˌdɪsədˈvæntɪd͡ʒ/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Fai shadowing frase per frase a velocità normale, ripetendo ognuna finché il tuo ritmo coincide con quello di chi parla.
- Registrati e confronta con l’originale, facendo attenzione a parole come artificial, pillar, technological.
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.