Скачать или смотреть Dazza Greenwood on The Actual Risks of Generative AI

This is a short talk by Dazza Greenwood, Executive Director of the MIT Computational Law Report, Task Force on Responsible Use of Gen AI in Law.

Greenwood was one of about a dozen remarkable innovators who hope at an event on The Actual Risks of Generative AI, which was created and produced by Reinvent Futures and held at the Shack15 club in the Ferry Building of San Francisco in July 2023.

You can find out more about The Great Progression series of events through the written essays in Substack here: https://peterleyden.substack.com

You can find out more about what we learned at this specific event on The Actual Risks of Generative AI here: https://peterleyden.substack.com/p/th...

You can read extended quotes from the dozen speakers at this event here: https://peterleyden.substack.com/p/th...

You can find out more about the strategic foresight company Reinvent Futures that created and produced the events here: https://www.reinvent.net

Some key extended quotes from this short talk:

I think that the most important real risk, not necessarily the most exotic potential risk, that I'm coming across with respect to the use of Generative AI in the field of law, is it's almost too good. It's seductively good. And yet it's hardly perfect. It has flaws. It has limits. It hallucinates, it has biases.

The main thing I want to talk about is what's automation bias and the risk that humans might get too dependent on AI? I'm a heavy user of this technology to assist in all parts of the sequence of production, refinement and other tasks. It is very tempting, after many hours, and many days, and many weeks, to just copy and paste something that, at a glance, looks good enough. Yet, this is the road to ruin.

The thing with automation bias is it's not just a bad look when something's subpar and gets into the work stream, but it could have serious consequences in certain areas. There's a lot of automation happening already in cockpits, in emergency rooms, in decision support. Now with the advent of Generative AI, it's happening more broadly.

We need now to start to develop a better immune system in general, and fundamentally with respect to Generative AI, to think critically, to review the outputs, to ask the hard questions, and never to just simply accept them, but to take them as a starting point. We need to now build up an entire new set of muscles and practices, where we know how to review, and never over-rely upon the output.

I'm going to say this hidden beast of a risk is so subtle, and it's so seductive — to fall backwards into the warm bath of just using this technology. But don't do it. Don't over-rely — rely on just the right amount.