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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Bu dersin kelimeleri ve konuşma notları
Bu konuşma dersi “What is AI Ethics?” videosuna dayanıyor. En çok tekrarlanan kelimeler: model, intelligence, artificial, organization, thinking. Bu videoda gölgeleme çalışması için 21 cümle ve 859 kelime var. Konuşma bölümü 6:09 sürüyor. Konuşmacı dakikada yaklaşık 140 kelimelik dengeli bir hızla konuşuyor; gölgeleme için rahat bir tempo. Kelimelerin %86’i İngilizcede en sık kullanılan 3.000 kelime arasında; geri kalanına başlamadan önce bakmakta fayda var. 21 cümlenin 13 tanesi soru; soru tonlaması ve kısa cevaplar için iyi bir alıştırma.
Bu videodaki önemli kelimeler
Videodan öğrenmeye değer 15 kelime, telaffuzu ve anlamıyla:
| Kelime | Telaffuz | Anlam |
|---|---|---|
| artificial sıfat | /ˌɑː.tɪˈfɪʃ.əl/ | yapay, suni |
| pillar isim | /ˈpɪlɚ/ | sütun, direk |
| technological sıfat | /ˌtɛk.nəˈlɑ.dʒɪ.kəl/ | teknolojik |
| earn fiil | /ɝn/ | hak etmek |
| associate isim | /əˈsoʊʃi.ət/ | iştirakçi, ortak |
| diversity isim | /daɪˈvɜː(ɹ)sɪti/ | çeşitlilik |
| assure fiil | /əˈʃʊɹ/ | temin etmek, güvence altına almak |
| solve fiil | /sɒlv/ | çözmek, halletmek |
| sheet isim | /ʃit/ | varak, sayfa |
| privacy isim | /ˈpɹaɪ.və.si/ | gizlilik, mahremiyet |
| principle isim | /ˈpɹɪn.sɪ.pəl/ | prensip, ilke |
| wisdom isim | /ˈwɪzdəm/ | bilgelik, hikmet |
| framework isim | /ˈfɹeɪm.wɜːk/ | çatkı, çatık |
| fence isim | /fɛns/ | çit, duvar |
| bias isim | /ˈbaɪ.əs/ | yanlılık, ön yargı |
Bu videodaki dil bilgisi
Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:
| Yapı | Videoda |
|---|---|
| Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli | being made · was trained · is really meant |
| Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem | has come · you've ever heard |
Dikkat edilecek telaffuzlar
Konuşmacı we're, you're, I'm gibi kısaltılmış biçimleri 13 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “th” sesleri: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethically /ˈɛθɪkəli/
- “sh” ve “zh” sesleri: artificial /ˌɑː.tɪˈfɪʃ.əl/, associate /əˈsoʊʃi.ət/, assure /əˈʃʊɹ/, sheet /ʃit/
- Uzun kelimeler — vurguyu doğru yere koyun: artificial /ˌɑː.tɪˈfɪʃ.əl/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, associate /əˈsoʊʃi.ət/, diversity /daɪˈvɜː(ɹ)sɪti/, minority /maɪˈnɒɹ.ɪ.ti/
Türkçe konuşanların zorlandığı sesler:
- /w/ — dudaklar yuvarlak, /v/ değil: wisdom /ˈwɪzdəm/, framework /ˈfɹeɪm.wɜːk/, squeaky /ˈskwiːki/, wilfully /ˈwɪlfəli/
- Kelime başındaki ünsüz kümesi — araya ünlü eklemeyin: tune /tjuːn/, privacy /ˈpɹaɪ.və.si/, principle /ˈpɹɪn.sɪ.pəl/, framework /ˈfɹeɪm.wɜːk/, creator /kɹiːˈeɪ̯təː/
- /æ/ — “e”den daha açık: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, transparent /tɹænsˈpæɹənt/, transparency /tɹænsˈpæɹənsi/, disadvantage /ˌdɪsədˈvæntɪd͡ʒ/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- Normal hızda cümle cümle tekrar edin; ritminiz konuşmacıyla aynı olana kadar her cümleyi yineleyin.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; artificial, pillar, technological gibi kelimelere özellikle dikkat edin.
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.