117. Beena Ammanath - Defining trustworthy AI

Towards Data Science

03/30/22

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46m

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Trustworthy AI is one of today’s most popular buzzwords. But although everyone seems to agree that we want AI to be trustworthy, definitions of trustworthiness are often fuzzy or inadequate. Maybe that shouldn’t be surprising: it’s hard to come up with a single set of standards that add up to “trustworthiness”, and that apply just as well to a Netflix movie recommendation as a self-driving car.

So maybe trustworthy AI needs to be thought of in a more nuanced way — one that reflects the intricacies of individual AI use cases. If that’s true, then new questions come up: who gets to define trustworthiness, and who bears responsibility when a lack of trustworthiness leads to harms like AI accidents, or undesired biases?

Through that lens, trustworthiness becomes a problem not just for algorithms, but for organizations. And that’s exactly the case that Beena Ammanath makes in her upcoming book, Trustworthy AI, which explores AI trustworthiness from a practical perspective, looking at what concrete steps companies can take to make their in-house AI work safer, better and more reliable. Beena joined me to talk about defining trustworthiness, explainability and robustness in AI, as well as the future of AI regulation and self-regulation on this episode of the TDS podcast.

Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

Link to Track: https://youtu.be/d8Y2sKIgFWc

Chapters:

1:55 Background and trustworthy AI
7:30 Incentives to work on capabilities
13:40 Regulation at the level of application domain
16:45 Bridging the gap
23:30 Level of cognition offloaded to the AI
25:45 What is trustworthy AI?
34:00 Examples of robustness failures
36:45 Team diversity
40:15 Smaller companies
43:00 Application of best practices
46:30 Wrap-up

Previous Episode

116. Katya Sedova - AI-powered disinformation, present and future

March 23, 2022

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54m

Until recently, very few people were paying attention to the potential malicious applications of AI. And that made some sense: in an era where AIs were narrow and had to be purpose-built for every application, you’d need an entire research team to develop AI tools for malicious applications. Since it’s more profitable (and safer) for that kind of talent to work in the legal economy, AI didn’t offer much low-hanging fruit for malicious actors.

But today, that’s all changing. As AI becomes more flexible and general, the link between the purpose for which an AI was built and its potential downstream applications has all but disappeared. Large language models can be trained to perform valuable tasks, like supporting writers, translating between languages, or write better code. But a system that can write an essay can also write a fake news article, or power an army of humanlike text-generating bots.

More than any other moment in the history of AI, the move to scaled, general-purpose foundation models has shown how AI can be a double-edged sword. And now that these models exist, we have to come to terms with them, and figure out how to build societies that remain stable in the face of compelling AI-generated content, and increasingly accessible AI-powered tools with malicious use potential.

That’s why I wanted to speak with Katya Sedova, a former Congressional Fellow and Microsoft alumna who now works at Georgetown University’s Center for Security and Emerging Technology, where she recently co-authored some fascinating work exploring current and likely future malicious uses of AI. If you like this conversation I’d really recommend checking out her team’s latest report — it’s called “AI and the future of disinformation campaigns”.

Katya joined me to talk about malicious AI-powered chatbots, fake news generation and the future of AI-augmented influence campaigns on this episode of the TDS podcast.

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Intro music:

➞ Artist: Ron Gelinas

➞ Track Title: Daybreak Chill Blend (original mix)

➞ Link to Track: https://youtu.be/d8Y2sKIgFWc

***

Chapters:

2:40 Malicious uses of AI
4:30 Last 10 years in the field
7:50 Low handing fruit of automation
14:30 Other analytics functions
25:30 Authentic bots
30:00 Influences of service businesses
36:00 Race to the bottom
42:30 Automation of systems
50:00 Manufacturing norms
52:30 Interdisciplinary conversations
54:00 Wrap-up

Next Episode

118. Angela Fan - Generating Wikipedia articles with AI

April 6, 2022

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51m

Generating well-referenced and accurate Wikipedia articles has always been an important problem: Wikipedia has essentially become the Internet's encyclopedia of record, and hundreds of millions of people use it do understand the world.

But over the last decade Wikipedia has also become a critical source of training data for data-hungry text generation models. As a result, any shortcomings in Wikipedia’s content are at risk of being amplified by the text generation tools of the future. If one type of topic or person is chronically under-represented in Wikipedia’s corpus, we can expect generative text models to mirror — or even amplify — that under-representation in their outputs.

Through that lens, the project of Wikipedia article generation is about much more than it seems — it’s quite literally about setting the scene for the language generation systems of the future, and empowering humans to guide those systems in more robust ways.

That’s why I wanted to talk to Meta AI researcher Angela Fan, whose latest project is focused on generating reliable, accurate, and structured Wikipedia articles. She joined me to talk about her work, the implications of high-quality long-form text generation, and the future of human/AI collaboration on this episode of the TDS podcast.

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Intro music:

Artist: Ron Gelinas

Track Title: Daybreak Chill Blend (original mix)

Link to Track: https://youtu.be/d8Y2sKIgFWc

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Chapters: