On AI, prescription drugs, and managing the risks of things we don’t understand

Source: IWSMT


Last month, Technology Review published a good article discussing the “dark secret at the heart of AI”–namely, that “[n]o one really knows how the most advanced algorithms do what they do.”  The opacity of algorithmic systems is something that has long drawn attention and criticism.  But it is a concern that has broadened and deepened in the past few years, during which breakthroughs in “deep learning” have led to a rapid increase in the sophistication of AI.  These deep learning systems operate using deep neural networks that are designed to roughly simulate the way the human brain works–or, to be more precise, to simulate the way the human brain works as we currently understand it.

Such systems can effectively “program themselves” by creating much or most of the code through which they operate.  The code generated by such systems can be very complex.  It can be so complex, in fact, that even the people who built and initially programmed the system may not be able to fully explain why the systems do what they do:

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Is AI personhood already possible under U.S. LLC laws? (Part Two: Uniform LLC Act)


This will, as it turns out, be a three-part series examining whether legal personhood is already possible under US laws governing limited liability companies (LLCs), which Shawn Bayern suggests provide an active path to personhood for autonomous systems.  Bayern relied primarily on two sources of law: New York’s LLC statute, and the Revised Uniform LLC Act (RULLCA).  Last week’s post explained why New York’s statute does not appear to provide a plausible path to AI personhood.  This week’s will take the same critical approach to RULLCA and, more importantly, the states that have adopted some variation of RULLCA.

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Is AI personhood already possible under U.S. LLC laws? (Part One: New York)

Forewarning, this will be far longer and far more of a technical legal post than usual.  It is also part 1 of what will be a 3-part post.  Part 2 is posted here, and Part 3 is posted here.

One particularly hot topic in the world of law and AI is that of “artificial personhood.”  The usual framing of this issue is: “should we grant ‘legal personhood’ to A.I. systems and give them legal recognition in the same way that the law recognizes corporations and natural persons?”  This is, to be sure, an excellent question, and artificial personhood is one of my favorite topics to discuss and write about.

But some authors in the past few years, most notably Shawn Bayern, have gone one step further, claiming that existing laws already permit the recognition of AI personhood for all intents and purposes.  Bayern focuses his attention primarily on the prospect of a “Zero-Member” or “memberless” LLC.  (“Members” of a LLC are roughly analogous to partners in a partnership).

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Questions from a young reader

Credit: Tom Toles, The Buffalo News, 1997


Last week I got an email from Will, an 8th Grader from Big D (little A, double L, A, S).  He is in a class where the students get to choose a topic to write about, and he chose AI because he had “always wondered about what makes a machine better than humans in an area.”

Will emailed me wanting to know if I could answer some questions he had about AI and its impact on our society.  I happily agreed, and he responded by sending five excellent questions.  After getting approval from Will and his teacher (thanks, Ms. Peterson!), I am posting Will’s questions and my responses below.  (I also sent Will an email with much shorter responses so that he wouldn’t fall asleep halfway through my answers).

Here they are:

 

What are your thoughts on the rapidly increasing investment in AI of huge companies such as Google and Microsoft?

This is one of the hottest topics in the world of AI policy right now.  In some ways, the investment in AI by these companies is a good thing.  There are so many things we could do with better AI systems, from having more accurate weather forecasts to reducing traffic on highways to helping doctors come up with better diagnoses when someone is sick.  Those things would bring great benefits to lots of people, and they could happen much more quickly if big companies focus their time and money on improving AI.

On the other hand, there are always dangers when big companies get too much power.  The usual way that we deal with those dangers has been through government action.  But modern AI technologies are very complicated—so complicated that sometimes even the people who design them may not totally understand why they do what they do!  It is hard to come up with good rules for things that no one completely understands.

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