97. Anthony Habayeb - The present and future of AI regulation

97. Anthony Habayeb - The present and future of AI regulation

Author: The TDS team October 6, 2021 Duration: 49:31

Corporate governance of AI doesn’t sound like a sexy topic, but it’s rapidly becoming one of the most important challenges for big companies that rely on machine learning models to deliver value for their customers. More and more, they’re expected to develop and implement governance strategies to reduce the incidence of bias, and increase the transparency of their AI systems and development processes. Those expectations have historically come from consumers, but governments are starting impose hard requirements, too.

So for today’s episode, I spoke to Anthony Habayeb, founder and CEO of Monitaur, a startup focused on helping businesses anticipate and comply with new and upcoming AI regulations and governance requirements. Anthony’s been watching the world of AI regulation very closely over the last several years, and was kind enough to share his insights on the current state of play and future direction of the field.

--- 

Intro music:

➞ Artist: Ron Gelinas

➞ Track Title: Daybreak Chill Blend (original mix)

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

--- 

Chapters:

- 0:00 Intro

- 1:45 Anthony’s background

- 6:20 Philosophies surrounding regulation

- 14:50 The role of governments

- 17:30 Understanding fairness

- 25:35 AI’s PR problem

- 35:20 Governments’ regulation

- 42:25 Useful techniques for data science teams

- 46:10 Future of AI governance

- 49:20 Wrap-up


While the active production of Towards Data Science has concluded, its archive remains a vital resource. Created by The TDS team, this collection captures a specific moment in the rapid evolution of data science and artificial intelligence. Each conversation pulls you directly into the room with leading researchers and practitioners who were shaping the tools and theories of their time. The discussions are not abstract lectures; they are grounded explorations of real-world problems, ethical dilemmas, and technical challenges that defined the field's trajectory. You'll hear experts dissect the implications of their work, from algorithmic fairness to the practicalities of deploying models at scale. This podcast served as a forum for nuanced debate, where complex ideas were unpacked with clarity and depth. Listening now offers a unique historical perspective, a chance to understand the foundational conversations that continue to influence where technology is headed next. The archive of Towards Data Science stands as a substantive record of insight, preserving the voices and questions from the forefront of a digital revolution.
Author: Language: en-us Episodes: 50

Towards Data Science
Podcast Episodes
87. Evan Hubinger - The Inner Alignment Problem [not-audio_url] [/not-audio_url]

Duration: 1:09:32
How can you know that a super-intelligent AI is trying to do what you asked it to do? The answer, it turns out, is: not easily. And unfortunately, an increasing number of AI safety researchers are warning that this is a…
86. Andy Jones - AI Safety and the Scaling Hypothesis [not-audio_url] [/not-audio_url]

Duration: 1:25:44
When OpenAI announced the release of their GPT-3 API last year, the tech world was shocked. Here was a language model, trained only to perform a simple autocomplete task, which turned out to be capable of language transl…
85. Brian Christian - The Alignment Problem [not-audio_url] [/not-audio_url]

Duration: 1:06:19
In 2016, OpenAI published a blog describing the results of one of their AI safety experiments. In it, they describe how an AI that was trained to maximize its score in a boat racing game ended up discovering a strange ha…
83. Rosie Campbell - Should all AI research be published? [not-audio_url] [/not-audio_url]

Duration: 52:37
When OpenAI developed its GPT-2 language model in early 2019, they initially chose not to publish the algorithm, owing to concerns over its potential for malicious use, as well as the need for the AI industry to experime…
82. Jakob Foerster - The high cost of automated weapons [not-audio_url] [/not-audio_url]

Duration: 54:07
Automated weapons mean fewer casualties, faster reaction times, and more precise strikes. They’re a clear win for any country that deploys them. You can see the appeal. But they’re also a classic prisoner’s dilemma. Once…
81. Nicolas Miailhe - AI risk is a global problem [not-audio_url] [/not-audio_url]

Duration: 56:03
In December 1938, a frustrated nuclear physicist named Leo Szilard wrote a letter to the British Admiralty telling them that he had given up on his greatest invention — the nuclear chain reaction. "The idea of a nuclear…