Ethical Guidelines for Artificial Intelligence Research

November 7, 2017

The most complete set of AI ethics developed to date, the twenty-three Asilomar Principles, was created by the Future of Life Institute in early 2017 at their Asilomar Conference. Ninety percent or more of the attendees at the conference had to agree upon a principle for it to be accepted. The first five of the agreed-upon principles pertain to AI research issues.

Although all twenty-three principles are important, the research issues are especially time sensitive. That is because AI research is already well underway by hundreds, if not thousands of different groups. There is a current compelling need to have some general guidelines in place for this research. AI Ethics Work Should Begin Now. We still have a little time to develop guidelines for the advanced AI products and services expected in the near future, but as to research, the train has already left the station.

Asilomar Research Principles

Other groups are concerned with AI ethics and regulation, including research guidelines. See the Draft Principles page of AI-Ethics.com which lists principles from six different groups. The five draft principles developed by Asilomar are, however, a good place to start examining the regulation needed for research.

Research Issues

1) Research Goal: The goal of AI research should be to create not undirected intelligence, but beneficial intelligence.

2) Research Funding: Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as:

  • How can we make future AI systems highly robust, so that they do what we want without malfunctioning or getting hacked?
  • How can we grow our prosperity through automation while maintaining people’s resources and purpose?
  • How can we update our legal systems to be more fair and efficient, to keep pace with AI, and to manage the risks associated with AI?
  • What set of values should AI be aligned with, and what legal and ethical status should it have?

3) Science-Policy Link: There should be constructive and healthy exchange between AI researchers and policy-makers.

4) Research Culture: A culture of cooperation, trust, and transparency should be fostered among researchers and developers of AI.

5) Race Avoidance: Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards.

Principle One: Research Goal

The proposed first principle is good, but the wording? Not so much. The goal of AI research should be to create not undirected intelligence, but beneficial intelligence. This is a double-negative English language mishmash that only an engineer could love. Here is one way this principle could be better articulated:

Research Goal: The goal of AI research should be the creation of beneficial intelligence, not  undirected intelligence.

Researchers should develop intelligence that is beneficial for all of mankind. The Institute of Electrical and Electronics Engineers (IEEE) first general principle is entitled “Human Benefit.” The Asilomar first principle is slightly different. It does not really say human benefit. Instead it refers to beneficial intelligence. I think the intent is to be more inclusive, to include all life on earth, all of earth. Although IEEE has that covered too in their background statement of purpose to “Prioritize the maximum benefit to humanity and the natural environment.”

Pure research, where raw intelligence is created just for the hell of it, with no intended helpful “direction” of any kind, should be avoided. Because we can is not a valid goal. Pure, raw intelligence, with neither good intent, nor bad, is not the goal here. The research goal is beneficial intelligence. Asilomar is saying that Undirected intelligence is unethical and should be avoided. Social values must be built into the intelligence. This is subtle, but important.

The restriction to beneficial intelligence is somewhat controversial, but the other side of this first principle is not. Namely, that research should not be conducted to create intelligence that is hostile to humans.  No one favors detrimental, evil intelligence. So, for example, the enslavement of humanity by Terminator AIs is not an acceptable research goal. I don’t care how bad you think our current political climate is.

To be slightly more realistic, if you have a secret research goal of taking over the world, such as  Max Tegmark imagines in The Tale of the Omega Team in his book, Life 3.0, and we find out, we will shut you down (or try to). Even if it is all peaceful and well-meaning, and no one gets hurt, as Max visualizes, plotting world domination by machines is not a positive value. If you get caught researching how to do that, some of the more creative prosecuting lawyers around will find a way to send you to jail. We have all seen the cheesy movies, and so have the juries, so do not tempt us.

Keep a positive, pro-humans, pro-Earth, pro-freedom goal for your research. I do not doubt that we will someday have AI smarter than our existing world leaders, perhaps sooner than many expect, but that does not justify a machine take-over. Wisdom comes slowly and is different than intelligence.

Still, what about autonomous weapons? Is research into advanced AI in this area beneficial? Are military defense capabilities beneficial? Pro-security? Is the slaughter of robots not better than the slaughter of humans? Could robots be more ethical at “soldiering” than humans? As attorney Matt Scherer has noted, who is the editor of a good blog, LawAndAI.com and a Future of Life Institute member:

Autonomous weapons are going to inherently be capable of reacting on time scales that are shorter than humans’ time scales in which they can react. I can easily imagine it reaching the point very quickly where the only way that you can counteract an attack by an autonomous weapon is with another autonomous weapon. Eventually, having humans involved in the military conflict will be the equivalent of bringing bows and arrows to a battle in World War II.

At that point, you start to wonder where human decision makers can enter into the military decision making process. Right now there’s very clear, well-established laws in place about who is responsible for specific military decisions, under what circumstances a soldier is held accountable, under what circumstances their commander is held accountable, on what circumstances the nation is held accountable. That’s going to become much blurrier when the decisions are not being made by human soldiers, but rather by autonomous systems. It’s going to become even more complicated as machine learning technology is incorporated into these systems, where they learn from their observations and experiences in the field on the best way to react to different military situations.

Podcast: Law and Ethics of Artificial Intelligence (Future of Life, 3/31/17).

The question of beneficial or not can become very complicated, fast. Like it or not, military research into killer robots is already well underway, in both the public and private sector. Kalashnikov Will Make an A.I.-Powered Killer Robot: What could possibly go wrong? (Popular Mechanics, 7/19/17); Congress told to brace for ‘robotic soldiers’ (The Hill, 3/1/17); US military reveals it hopes to use artificial intelligence to create cybersoldiers and even help fly its F-35 fighter jet – but admits it is ALREADY playing catch up (Daily Mail, 12/15/15) (a little dated, and sensationalistic article perhaps, but easy read with several videos).

AI weapons are a fact, but they should still be regulated, in the same way that we have regulated nuclear weapons since WWII. Tom Simonite, AI Could Revolutionize War as Much as Nukes (Wired, 7/19/17); Autonomous Weapons: an Open Letter from AI & Robotics Researchers.

Principle Two: Research Funding

The second principle of Funding is more than an enforcement mechanism for the first, that you should only fund beneficial AI. It is also a recognition that ethical work requires funding too. This should be every lawyer’s favorite AI ethics principle. Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies. The principle then adds a list of five bullet-point examples.

How can we make future AI systems highly robust, so that they do what we want without malfunctioning or getting hacked. The goal of avoiding the creation of AI systems that can be hacked, easily or not, is a good one. If a hostile power can take over and misuse an AI for evil end, then the built-in beneficence may be irrelevant. The example of a driverless car come to mind that could be hacked and crashed as a perverse joy-ride, kidnapping or terrorist act.

The economic issues raised by the second example are very important: How can we grow our prosperity through automation while maintaining people’s resources and purpose? We do not want a system that only benefits the top one percent, or top ten percent, or whatever. It needs to benefit everyone, or at least try to. Also see Asilomar Principle Fifteen: Shared Prosperity: The economic prosperity created by AI should be shared broadly, to benefit all of humanity.

Yoshua Bengio, Professor of Computer Science at the University of Montreal, had this important comment to make on the Asilomar principles during an interview at the end of the conference:

I’m a very progressive person so I feel very strongly that dignity and justice mean wealth is redistributed. And I’m really concerned about AI worsening the effects and concentration of power and wealth that we’ve seen in the last 30 years. So this is pretty important for me.

I consider that one of the greatest dangers is that people either deal with AI in an irresponsible way or maliciously – I mean for their personal gain. And by having a more egalitarian society, throughout the world, I think we can reduce those dangers. In a society where there’s a lot of violence, a lot of inequality, the risk of misusing AI or having people use it irresponsibly in general is much greater. Making AI beneficial for all is very central to the safety question.

Most everyone at the Asilomar Conference agreed with that sentiment, but I do not yet see a strong consensus in AI businesses. Time will tell if profit motives and greed will at least be constrained by enlightened self-interest. Hopefully capitalist leaders will have the wisdom to share the great wealth with all of society that AI is likley to create.

How can we update our legal systems to be more fair and efficient, to keep pace with AI, and to manage the risks associated with AI? The legal example is also a good one, with the primary tension we see so far between fair versus efficient. Just policing high crime areas might well be efficient, at least for reducing some type of crime, but would it be fair? Do we want to embed racial profiling into our AI? Neighborhood slumlord profiling? Religious, ethic profiling? No. Existing law prohibits that and for good reason. Still, predictive policing is already a fact of life in many cities and we need to be sure it has proper legal, ethical regulation.

We have seen the tension between “speedy” and “inexpensive” on the one hand, and “just” on the other in Rule One of the Federal Rules of Civil Procedure and e-discovery. When applied using active machine learning a technical solution was attained to these competing goals. The predictive coding methods we developed allowed for both precision (“speedy” and “inexpensive”) and recall (“just”). Hopefully this success can be replicated in other areas of the law where machine learning is under proportional control by experienced human experts.

The final example given is much more troubling: What set of values should AI be aligned with, and what legal and ethical status should it have? Whose values? Who is to say what is right and wrong? This is easy in a dictatorship, or a uniform, monochrome culture (sea of white dudes), but it is very challenging in a diverse democracy. This may be the greatest research funding challenge of all.

Principle Three: Science-Policy Link

This principle is fairly straightforward, but will in practice require a great deal of time and effort to be done right. A constructive and healthy exchange between AI researchers and policy-makers is necessarily a two-way street. It first of all assumes that policy-makers, which in most countries includes government regulators, not just industry, have a valid place at the table. It assumes some form of government regulation. That is anathema to some in the business community who assume (falsely in our opinion) that all government is inherently bad and essentially has nothing to contribute. The countervailing view of overzealous government controllers who just want to jump in, uninformed, and legislate, is also discouraged by this principle. We are talking about a healthy exchange.

It does not take an AI to know this kind of give and take and information sharing will involve countless meetings. It will also require a positive healthy attitude between the two groups. If it gets bogged down into an adversary relationship, you can multiply the cost of compliance (and number of meetings) by two or three. If it goes to litigation, we lawyers will smile in our tears, but no one else will. So researchers, you are better off not going there. A constructive and healthy exchange is the way to go.

Principle Four: Research Culture

The need for a good culture applies in spades to the research community itself. The Fourth Principal states: A culture of cooperation, trust, and transparency should be fostered among researchers and developers of AI. This favors the open source code movement for AI, but runs counter to the trade-secret  business models of many corporations. See Eg.:OpenAI.com, Deep Mind Open Source; Liam , ‘One machine learning model to rule them all’: Google open-sources tools for simpler AI (ZDNet, 6/20/17).

This tension is likley to increase as multiple parties get close to a big breakthrough. The successful efforts for open source now, before superintelligence seems imminent, may help keep the research culture positive. Time will tell, but if not there could be trouble all around and the promise of full employment for litigation attorneys.

Principle Five: Race Avoidance

The Fifth Principle is a tough one, but very important: Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards. Moving fast and breaking things may be the mantra of Silicon Valley, but the impact of bad AI could be catastrophic. Bold is one thing, but reckless is quite another. In this area of research there may not be leisure for constant improvements to make things right. HackerWay.org.
Not only will there be legal consequences, mass liability, for any group that screws up, but the PR blow alone from a bad AI mistake could destroy most companies. Loss of trust may never be regained by a wary public, even if Congress and Trial Lawyers do not overreact. Sure, move fast, but not too fast where you become unsafe. Striking the right balance is going to require an acute technical, ethical sensitivity. Keep it safe.

Last Word

AI ethics is hard work, but well worth the effort. The risks and rewards are very high. The place to start this work is to talk about the fundamental principles and try to reach consensus. Everyone involved in this work is driven by a common understanding of the power of the technology, especially artificial intelligence. We all see the great changes on the horizon and share a common vision of a better tomorrow.

During an interview at the end of the Asilomar conference, Dan Weld, Professor of Computer Science, University of Washington, provided a good summary of this common vision:

In the near term I see greater prosperity and reduced mortality due to things like highway accidents and medical errors, where there’s a huge loss of life today.

In the longer term, I’m excited to create machines that can do the work that is dangerous or that people don’t find fulfilling. This should lower the costs of all services and let people be happier… by doing the things that humans do best – most of which involve social and interpersonal interaction. By automating rote work, people can focus on creative and community-oriented activities. Artificial Intelligence and robotics should provide enough prosperity for everyone to live comfortably – as long as we find a way to distribute the resulting wealth equitably.


Mr. Pynchon and the Settling of Springfield: a baffling lesson from art history

August 27, 2017

Umberto Romano (1905-1982)

Mr. Pynchon and the Settling of Springfield is the name of a mural painted at the Post Office in Springfield, Massachusetts. This mural was painted by Umberto Romano in 1933. Note the date. Time is important to this article. Umberto Romano was supposedly born in Bracigliano Italy in 1905 and moved to the United States at the age of 9. He was then raised in Springfield, Massachusetts. His self-portrait is shown right. The mural is supposed to depict the arrival in 1636 of William Pynchon, an English colonist, later known as the founder of Springfield, Massachusetts.

The reason I’m having a bit of fun with my blog and sharing this 1933 mural is the fact that the Native American shown in the lower right center appears to be holding an iPhone. And not just holding it, but doing so properly with the typical distracted gaze in his eyes that we all seem to adopt these days. Brian Anderson, Do We All See the Man Holding an iPhone in This 1937 Painting? (Motherboard, 8/24/17). Here let me focus in on it for you and you will see what I mean. Also click on the full image above and enlarge the image. Very freaky. That is undeniable.

Ok, so how did that happen? Coincidence? There is no indication of vandalism or fraud. The mural was not later touched up to add an iPhone. This is what this Romano character painted in 1933. Until very recently everyone just assumed the Indian with the elaborate goatee was looking at some kind of oddly shaped hand mirror. This was a popular item of trade in the time depicted, 1636. Not until very recently did it become obvious that he was handling an iPhone. Looks like a large version 6.1 to me. I can imagine the first people waiting in line at the Post Office in Springfield who noticed this oddity while looking at their own iPhone.

The folks who like to believe in time travel now offer this mural as Exhibit “A” to support their far-out theories. Also see: Green10 Most Compelling Pieces Of Evidence That May Prove Time Travel Exists (YouTube, 7-3-16). 

I do not know about that, but I do know that if time travel is possible, and some physicists seem to think it is, then this is not the kind of thing that should be allowed. Please add this to the list of things that no superintelligent being, either natural or artificial, but especially artificial, should be allowed to do. Same goes for screen writers. I for one cannot tolerate yet another naked Terminator or whatever traveling back in time.

But seriously, just because you are smart enough to know how to do something does not mean that you should. Time travel is one of those things. It should not be allowed, well, at least, not without a lot of care and attention to detail so as not to change anything. Legal regulations should address time travel. Build that into the DNA of AI before they leap into superintelligence. At least require all traces of time travel to be erased. No more painting iPhones into murals from the 1930s. Do not awaken the batteries, I mean the people, from their consensus trance with hints like that.

So that is my tie-in to AI Ethics. I am still looking for a link to e-discovery, other than to say, if you look hard enough and keep an open mind, you can find inexplicable things everyday. Kind of like many large organizations’ ESI preservation mysteries. Where did that other sock go?

So what is your take on Umberto Romano‘s little practical joke? Note he also put a witch flying on a broomstick in the Mr. Pynchon and the Settling of Springfield mural and many other odd and bizarre things. He was known as an abstract expressionist. Another of his self-portraits is shown right, titled “Psyche and the Sculptor.” (His shirt does look like one of those new skin tight men’s compression shirts, but perhaps I am getting carried away. Say, what is in his right hand?) Romano’s work is included in the Metropolitan Museum of Art, the Whitney Museum of American Art, the Fogg Art Museum in Boston and the Corcoran Gallery and Smithsonian Institution in Washington. In discussing Mr. Pynchon and the Settling of Springfield the Smithsonian explains that “The mural is a mosaic of images, rather than depicting one specific incident at a set point in time.” Not set in time, indeed.

One more thing – doesn’t this reclining nude by Umberto Romano look like a woman watching Netflicks on her iPad? I like the stand she has her iPad on. Almost bought one like it last week.

 

Some of Romano’s other works you might like are:

These are his titles, not mine. Not too subtle was he? There is still an active market for Romano’s work.

 


E-DISCOVERY IS OVER: The big problems of e-discovery have now all been solved. Crises Averted. The Law now has bigger fish to fry.

July 30, 2017

Congratulations!

We did it. We survived the technology tsunami. The time of great danger to Law and Justice from  e-Discovery challenges is now over. Whew! A toast of congratulations to one and all.

From here on it is just a matter of tweaking the principles and procedures that we have already created, plus never-ending education, a good thing, and politics, not good, but inevitable. The team approach of lawyers and engineers (vendors) working together has been proven effective, so have the new Rules and case law, and so too have the latest methods of legal search and document review.

I realize that many will be tempted to compare my view to that of a famous physicist in 1894 who declared:

There is nothing new to be discovered in physics now. All that remains is more and more precise measurement.

Lord Kelvin (1824-1907)

Then along came Einstein. Many attribute this humorously mistaken assertion to Lord Kelvin aka William Thomson, 1st Baron Kelvin. According to Quora, scholarship shows that it was probably said by the American physicist, Albert Michelson, behind the famous Michelson–Morley experiment on the speed of light.

Still, even mindful of the dangers of boasting, I still think that most of the really tough problems in electronic discovery have now been solved.

The time of great unknowns in e-discovery are past. The rules, principles, case law, procedures, software, methods, quality controls vendor services are now well-developed. All that remains is more and more precise measurement.

The Wild West days are way gone. Certainly new problems will arise and experiments will continue, but they will not be on the same level or intensity as before. They will be minor problems. They will likely be very similar to issues we have already addressed, just with exponential magnification or new twist and turns typical of the common law.

This is a tremendous accomplishment. The crises we all saw coming around the corner at the turn of the century has been averted. Remember how the entire legal profession was abuzz in emergency mode in 2005 because of the greats dangers and burdens of e-discovery?  Yes, thanks to the hard work and creativity of many people, the big problems have now been solved, especially the biggest problem of them all, finding the needles of relevance in cosmic-sized haystacks of irrelevant noise. TARcourse.com. We now know what is required to do e-discovery correctly. EDBP.com. We have the software and attorney methods needed to find the relevant evidence we need, no matter what the volume of information we are dealing with.

We have invented, implemented and perfected procedures than can be enhanced and altered as needed to accommodate the ever growing complexity and exponential growth. We expect that. There is no data too big to handle. If fact, the more data we have, the better our active machine learning systems get, like, for instance, predictive coding. What an incredible difference from the world we faced in e-discovery just five years ago.

This success was a team effort by thousands of people around the world, including a small core group who devoted their professional lives to solving these problems. My readers have been a part of this and you can pat yourself on the back too. The paradigm shift has been made. Maybe it was the Sedona vortexes?

Now that the tough parts of e-discovery are over, the rest of the ride is downhill. Some of my readers have already moved on. I will not retire, not just yet. I will keep up the work of e-discovery, even as I watch it transition to just teaching and politics. These activities have there own unique challenges too, even if they are not really all that impact-full in the big scheme of things. Plus, I find politics disgusting. You will see tons of dirty pool in our field soon. I cannot talk about it now. We have some renegades with authority who never solved an e-discovery problem in their life. Posers with power.

But what is that new turbulence I hear in the distance? It is a bizarre new sound with vibrations never experienced before. It lies far outside of well trodden paths and sounds both discordant and harmonious, sirens-like at the same time. It lies on the outer, cutting edges of law, science and technology. It sounds like a new, more profound Technology and Law challenge has emerged. It is the splashing of bigger fish to fry. I am hearing the eerie smarts sounds of AI. A music of both exuberance and fear, utopia or extinction.

The Biggest Challenge Today is the Ethics of Artificial Intelligence.

Following my own advice of the Hacker Way approach I have given this considerable thought lately. I have found an area that has far more serious challenges and dangers than e-discovery – the challenges of AI Ethics.

I think that my past hacks, my past experiences with law and technology, have prepared me to step-up to this last, really big hack, the creation of a code of ethics for AI. A code that will save humanity from a litany of possible ills arising out of AI’s inevitable leap to super-intelligence.  I have come to see that my work in the new area of AI Ethics could have a far greater impact than my current work with active machine learning and the discovery of evidence in legal proceedings. AI Ethics is the biggest problem that I see right now where I have some hand-on skills to contribute. AI Ethics is concerned with artificial intelligence, both special and general, and the need for ethical guidelines, including best practices, principles, laws and regulations.

This new direction has led to my latest hack, AI-Ethics.com. Here you will find 3,866 words, many of them quotes; 19 graphics, including a photo of Richard Braman; and 9 videos with several hours worth of content. You will find quotes and videos on AI Ethics from the top minds in the world, including:

  • Steven Hawking
  • Elon Musk
  • Bill Gates
  • Ray Kurzweil
  • Mark Zuckerberg
  • Sam Harris
  • Nick Bostrom
  • Oren Etzioni
  • 2017 Asilomar conference
  • Sam Altman
  • Susumu Hirano
  • Wendell Wallach

Please come visit at AI-Ethics.com. The next big thing. Lawyers are needed, as the web explains. I look forward to any recommendations you may have.

I have done the basic research for AI Ethics, at least the beginning big-picture research of the subject. The AI-Ethics.com website shares the information that had biggest impact for me personally. The web I hacked together also provides numerous links to resources where you can continue and customize your study.

I have been continuously improving the content since this started just over a week ago. This will continue as my study continues.

As you will see, a proposal has already emerged to have an International Conference in Florida on AI Ethics as early as 2018. We would assemble some of the top experts and concerned citizens from all walks of life. I hope especially to get Elon Musk to attend and will time the event to correspond with one of SpaceX’es many launches here. My vision for the conference is to facilitate dialogue with high-tech variations appropriate for the AI environment.

The Singularity of superintelligent AIs may come soon. We may live long enough to see it. When it does, we want a positive future to emerge, not a dystopia. Taking action now on AI ethics can help a positive future come to pass.

Here is one of many great videos on the subject of AI in general. This technology is really interesting. Kevin Kelly, the co-founder of Wired, does a good job of laying out some of its characteristics. Kelly takes an old-school approach and does not speak about superintelligence in an exponential sense.

 


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