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.

Moravec’s Paradox of Artificial Intelligence and a Possible Solution by Hiroshi Yamakawa with Interesting Ethical Implications

October 29, 2017

Have you heard of Moravec’s Paradox? This is a principle discovered by AI robotics expert Hans Moravec in the 1980s. He discovered that, contrary to traditional assumptions, high-level reasoning requires relatively little computation power, whereas low-level sensorimotor skills require enormous computational resources. The paradox is sometimes simplified by the phrase: Robots find the difficult things easy and the easy things difficult. Moravec’s Paradox explains why we can now create specialized AI, such as predictive coding software to help lawyers find evidence, or AI software that can beat the top human experts at complex games such as Chess, Jeopardy and Go, but we cannot create robots as smart as dogs, much less as smart as gifted two-year-olds like my granddaughter. Also see the possible economic, cultural implications of this paradox as described, for instance, by Robots will not lead to fewer jobs – but the hollowing out of the middle class (The Guardian, 8/20/17).

Hans Moravec is a legend in the world of AI. An immigrant from Austria, he is now serving as a research professor in the Robotics Institute of Carnegie Mellon University. His work includes attempts to develop a fully autonomous robot that is capable of navigating its environment without human intervention. Aside from his paradox discovery, he is well-known for a book he wrote in 1990, Mind Children: The Future of Robot and Human Intelligence. This book has become a classic, well-known and admired by most AI scientists. It is also fairly easy for non-experts to read and understand, which is a rarity in most fields.

Moravec is also a futurist with many of his publications and predictions focusing on transhumanism, including Robot: Mere Machine to Transcendent Mind (Oxford U. Press, 1998). In Robot he predicted that Machines will attain human levels of intelligence by the year 2040, and by 2050 will have far surpassed us. His prediction may still come true, especially if exponential acceleration of computational power following Moore’s Law continues. But for now, we still have a long was to go. The video below gives funny examples of this in a compilation of robots falling down during a DARPA competition.

But then just a few weeks after this blog was originally published, we are shown how far along robots have come. This November 16, 2017, video of the latest Boston Dynamics robot is a dramatic example of accelerating, exponential change.

Yamakawa on Moravec’s Paradox

A recent interview of Horoshi Yamakawa, a leading researcher in Japan working on Artificial General Intelligence (AGI), sheds light on the Moravec Paradox.  See the April 5, 2017 interview of Dr. Hiroshi Yamakawa, by a host of AI Experts, Eric Gastfriend, Jason Orlosky, Mamiko Matsumoto, Benjamin Peterson, and Kazue Evans. The interview is published by the Future of Life Institute where you will find the full transcript and more details about Yamakawa.

In his interview Horoshi explains the Moravec Paradox and the emerging best hope for its solution by deep learning.

The field of AI has traditionally progressed with symbolic logic as its center. It has been built with knowledge defined by developers and manifested as AI that has a particular ability. This looks like “adult” intelligence ability. From this, programming logic becomes possible, and the development of technologies like calculators has steadily increased. On the other hand, the way a child learns to recognize objects or move things during early development, which corresponds to “child” AI, is conversely very difficult to explain. Because of this, programming some child-like behaviors is very difficult, which has stalled progress. This is also called Moravec’s Paradox.

However, with the advent of deep learning, development of this kind of “child” AI has become possible by learning from large amounts of training data. Understanding the content of learning by deep learning networks has become an important technological hurdle today. Understanding our inability to explain exactly how “child” AI works is key to understanding why we have had to wait for the appearance of deep learning.

Horoshi Yamakawa calls his approach to deep learning the Whole Brain Architecture approach.

The whole brain architecture is an engineering-based research approach “To create a human-like artificial general intelligence (AGI) by learning from the architecture of the entire brain.”  … In short, the goal is brain-inspired AI, which is essentially AGI. Basically, this approach to building AGI is the integration of artificial neural networks and machine-learning modules while using the brain’s hard wiring as a reference. However, even though we are using the entire brain as a building reference, our goal is not to completely understand the intricacies of the brain. In this sense, we are not looking to perfectly emulate the structure of the brain but to continue development with it as a coarse reference.

Yamakawa sees at least two advantages to this approach.

The first is that since we are creating AI that resembles the human brain, we can develop AGI with an affinity for humans. Simply put, I think it will be easier to create an AI with the same behavior and sense of values as humans this way. Even if superintelligence exceeds human intelligence in the near future, it will be comparatively easy to communicate with AI designed to think like a human, and this will be useful as machines and humans continue to live and interact with each other. …

The second merit of this unique approach is that if we successfully control this whole brain architecture, our completed AGI will arise as an entity to be shared with all of humanity. In short, in conjunction with the development of neuroscience, we will increasingly be able to see the entire structure of the brain and build a corresponding software platform. Developers will then be able to collaboratively contribute to this platform. … Moreover, with collaborative development, it will likely be difficult for this to become “someone’s” thing or project. …

Act Now for AI Safety?

As part of the interview Yamakawa was asked whether he thinks it would be productive to start working on AI Safety now? As readers here know, one of the major points of the AI-Ethics.com organization I started is that we need to begin work know on such regulations. Fortunately, Yamakawa agrees. His promising Whole Brained Architecture approach to deep learning as a way to overcome Moravec’s Paradox thus will likley have a strong ethics component. Here is Horoshi Yamakawa full, very interesting answer to this question.

I do not think it is at all too early to act for safety, and I think we should progress forward quickly. Technological development is accelerating at a fast pace as predicted by Kurzweil. Though we may be in the midst of this exponential development, since the insight of humans is relatively linear, we may still not be close to the correct answer. In situations where humans are exposed to a number of fears or risks, something referred to as “normalcy bias” in psychology typically kicks in. People essentially think, “Since things have been OK up to now, they will probably continue to be OK.” Though this is often correct, in this case, we should subtract this bias.

If possible, we should have several methods to be able to calculate the existential risk brought about by AGI. First, we should take a look at the Fermi Paradox. This is a type of estimation process that proposes that we can estimate the time at which intelligent life will become extinct based on the fact that we have not yet met with alien life and on the probability that alien life exists. However, using this type of estimation would result in a rather gloomy conclusion, so it doesn’t really serve as a good guide as to what we should do. As I mentioned before, it probably makes sense for us to think of things from the perspective of increasing decision making bodies that have increasing power to bring about the destruction of humanity.

 


More Additions to AI-Ethics.com: Offer to Host a No-Press Conference to Mediate the Current Disputes on AI Ethics, Report on the Asilomar Conference and Report on Cyborg Law

September 24, 2017

This week the Introduction and Mission Statement page of AI-Ethics.com was expanded. I also added two new blogs to the AI-Ethics website. The first is a report of the 2017 conference of the Future of Life Institute. The second is a report on Cyborg Law, subtitled, Using Physically Implanted AI to Enhance Human Abilities.

AI-Ethics.com Mission
A Conference to Move AI Ethics Talk from Argument to Dialogue

The first of the three missions of AI-Ethics.com is to foster dialogue between the conflicting camps in the current AI ethics debate. We have now articulated a specific proposal on how we propose to do that, namely by hosting a  conference to move AI ethics talk from argument to dialogue. I propose to use professional mediators to help the parties reach some kind of base consensus. I know we have the legal skills to move the feuding leaders from destructive argument to constructive dialogue. The battle of the ethics robots must stop!

In arguments nobody really listens to try to understand the other side. If they hear at all it is just to analyze and respond, to strike down. The adversarial argument approach only works if there is a fair, disinterested judge to rule and resolve the disputes. In the ongoing disputes between opposing camps in AI ethics there is no judge. There is only public opinion. In dialogue the whole point is to listen and hear the other side’s position. The idea is to build common understanding and perhaps reach a consensus from common ground. There are no winners unless both sides win. Since we have no judges in AI ethics, the adversarial debate now raging is pointless, irrational. It does more hard than good for both sides. Yet this kind of debate continues between otherwise very rational people.

The AI-Ethic’s Debate page was also updated this week to include the latest zinger. This time the dig was by Google’s head of search and AI, John Giannandrea, and was, as usual, directed against Elon Musk. Check out the page to see who said what. Also see: Porneczi, Google’s AI Boss Blasts Musk’s Scare Tactics on Machine Takeover (Bloomberg 9/19/17).

The bottom line for us now is how to move from debate to dialogue. (I was into that way before Sedona.) For that reason, we offer to host a closed meeting where the two opposing camps can meet and mediate.It will work, but only when the leaders of both sides are willing to at least be in the same room together at the same time and talk this out.

Here is our revised Mission page providing more details of our capabilities. Please let me know if you want to be a part of such a conference or can help make it happen.

We know from decades of legal experience as practicing attorneys, mediators and judges that we can overcome the current conflicts. We use confidential dialogues based on earned trust, understanding and respect. Social media and thirty-second sound bites, which characterize the current level of argument, will never get us there. It will, and already has, just exasperated the problem. AI-Ethics.com proposes to host a no-press allowed conference where people can speak to each other without concern of disclosure. Everyone will agree to maintain confidentiality. Then the biggest problem will be attendance, actually getting the leaders of both sides into a room together to hash this out. Depending on turn-out we could easily have dozens of breakout sessions and professional mediators and dialogue specialists assigned to each group.

The many lawyers already in AI-Ethics.com are well qualified to execute an event like that. Collectively we have experience with thousands of mediations; yes, some of them even involving scientists, top CEOs and celebrities. We know how to keep confidences, build bridges and overcome mistrust. If need be we can bring in top judges too. The current social media sniping that has characterized the AI ethics debate so far should stop. It should be replaced by real dialogue. If the parties are willing to at least meet, we can help make it happen. We are confident that we can help elevate the discussion and attain some levels of beginning consensus. At the very least we can stop the sniping. Write us if you might be able to help make this happen. Maybe then we can move onto agreement and action.

 

 

Future of Life Institute Asilomar Conference

The Future of Life Institute was founded by the charismatic, Max Tegmark, author of Life 3.0: Being Human in the Age of Artificial Intelligence (2017). This is a must-read, entry level book on AI, AI ethics and, as the title indicates, the future of life. Max is an MIT professor and cosmologist. The primary funding for his Institute is from none other than Elon Musk. The 2017 conference was held in Asilomar, California and so was named the Asilomar Conference. Looks like a very nice place on the coast to hold a conference.

This is the event where the Future of Life Institute came up with twenty-three proposed principles for AI ethics. They are called, as you might have guessed, the Asilomar Principles. I will be writing about these in the coming months as they are the most detailed list of principles yet created.

The new web page I created this week reports on the event itself, not the principles. You can learn a lot about the state of the law and AI ethics by reviewing this page and some of the videos shared there of conference presentations. We would like to put on an event like this, only more intimate and closed to press as discussed.

We will keep pushing for a small confidential dialogue based event like this. As mostly lawyers around here we know a lot about confidentiality and mediation. We can help make it happen. We have some places in Florida in mind for the event that are just as nice as Asilomar, maybe even nicer. We got through Hurricane Irma alright and are ready to go, without or without Musk’s millions to pay for it.

Cyborg Law and Cyber-Humans

The second new page in AI-Ethics.com is a report on Cyborg Law: Using Physically Implanted AI to Enhance Human Abilities. Although we will build and expand on this page in the future, what we have created so far relies primarily upon a recent article and book. The article is by Woodrow Barfield and Alexander Williams, Law, Cyborgs, and Technologically Enhanced Brains (Philosophies 2017, 2(1), 6; doi: 10.3390/philosophies2010006). The book is by the same Woodrow Barfield and is entitled Cyber-Humans: Our Future with Machines (December, 2015). Our new page also includes a short discussion and quote from Riley v. California, 573 U.S. __,  189 L.Ed.2d 430, 134 S.Ct. 2473 (2014).

Cyborg is a term that refers generally to humans with technology integrated into their body. The technology can be designed to restore lost functions, but also to enhance the anatomical, physiological, and information processing abilities of the body. Law, Cyborgs, and Technologically Enhanced Brains.

The lead author of the cited article on cyborg law, Woody Barfield is an engineer who has been thinking about the problems of cyborg regulation longer than anyone. Barfield was an Industrial and Systems Engineering Professor at the University of Washington for many years. His research focused on the design and use of wearable computers and augmented reality systems. Barfield has also obtained both JD and LLM degrees in intellectual property law and policy. The legal citations throughout his book, Cyber-Humans, make this especially valuable for lawyers. Look for more extended discussions of Barfield’s work here in the coming months. He is the rare engineer who also understands the law.


New Draft Principles of AI Ethics Proposed by the Allen Institute for Artificial Intelligence and the Problem of Election Hijacking by Secret AIs Posing as Real People

September 17, 2017

One of the activities of AI-Ethics.com is to monitor and report on the work of all groups that are writing draft principles to govern the future legal regulation of Artificial Intelligence. Many have been proposed to date. Click here to go to the AI-Ethics Draft Principles page. If you know of a group that has articulated draft principles not reported on our page, please let me know. At this point all of the proposed principles are works in progress.

The latest draft principles come from Oren Etzioni, the CEO of the Allen Institute for Artificial Intelligence. This institute, called AI2, was founded by Paul G. Allen in 2014. The Mission of AI2 is to contribute to humanity through high-impact AI research and engineering. Paul Allen is the now billionaire who co-founded Microsoft with Bill Gates in 1975 instead of completing college. Paul and Bill have changed a lot since their early hacker days, but Paul is still  into computers and funding advanced research. Yes, that’s Paul and Bill below left in 1981. Believe it or not, Gates was 26 years old when the photo was taken. They recreated the photo in 2013 with the same computers. I wonder if today’s facial recognition AI could tell that these are the same people?

Oren Etzioni, who runs AI2, is also a professor of computer science. Oren is very practical minded (he is on the No-Fear side of the Superintelligent AI debate) and makes some good legal points in his proposed principles. Professor Etzioni also suggests three laws as a start to this work. He says he was inspired by Aismov, although his proposal bears no similarities to Aismov’s Laws. The AI-Ethics Draft Principles page begins with a discussion of Issac Aismov’s famous Three Laws of Robotics.

Below is the new material about the Allen Institute’s proposal that we added at the end of the AI-Ethics.com Draft Principles page.

_________

Oren Etzioni, a professor of Computer Science and CEO of the Allen Institute for Artificial Intelligence has created three draft principles of AI Ethics shown below. He first announced them in a New York Times Editorial, How to Regulate Artificial Intelligence (NYT, 9/1/17). See his TED Talk Artificial Intelligence will empower us, not exterminate us (TEDx Seattle; November 19, 2016). Etzioni says his proposed rules were inspired by Asimov’s three laws of robotics.

  1. An A.I. system must be subject to the full gamut of laws that apply to its human operator.
  2. An A.I. system must clearly disclose that it is not human.
  3. An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information.

We would certainly like to hear more. As Oren said in the editorial, he introduces these three “as a starting point for discussion. … it is clear that A.I. is coming. Society needs to get ready.” That is exactly what we are saying too. AI Ethics Work Should Begin Now.

Oren’s editorial included a story to illustrate the second rule on duty to disclose. It involved a teacher at Georgia Tech named Jill Watson. She served as a teaching assistant in an online course on artificial intelligence. The engineering students were all supposedly fooled for the entire semester course into thinking that Watson was a human. She was not. She was an AI. It is kind of hard to believe that smart tech students wouldn’t know that a teacher named Watson, who no one had ever seen or heard of before, wasn’t a bot. After all, it was a course on AI.

This story was confirmed by a later reply to this editorial by the Ashok Goel, the Georgia Tech Professor who so fooled his students. Professor Goel, who supposedly is a real flesh and blood teacher, assures us that his engineering students were all very positive to have been tricked in this way. Ashok’s defensive Letter to Editor said:

Mr. Etzioni characterized our experiment as an effort to “fool” students. The point of the experiment was to determine whether an A.I. agent could be indistinguishable from human teaching assistants on a limited task in a constrained environment. (It was.)

When we did tell the students about Jill, their response was uniformly positive.

We were aware of the ethical issues and obtained approval of Georgia Tech’s Institutional Review Board, the office responsible for making sure that experiments with human subjects meet high ethical standards.

Etzioni’s proposed second rule states: An A.I. system must clearly disclose that it is not human. We suggest that the word “system” be deleted as not adding much and the rule be adopted immediately. It is urgently needed not just to protect student guinea pigs, but all humans, especially those using social media. Many humans are being fooled every day by bots posing as real people and creating fake news to manipulate real people. The democratic process is already under siege by dictators exploiting this regulation gap. Kupferschmidt, Social media ‘bots’ tried to influence the U.S. election. Germany may be next (Science, Sept. 13, 2017); Segarra, Facebook and Twitter Bots Are Starting to Influence Our Politics, a New Study Warns (Fortune, June 20, 2017); Wu, Please Prove You’re Not a Robot (NYT July 15, 2017); Samuel C. Woolley and Douglas R. Guilbeault, Computational Propaganda in the United States of America: Manufacturing Consensus Online (Oxford, UK: Project on Computational Propaganda).

In the concluding section to the 2017 scholarly paper Computational Propaganda by Woolley (shown here) and Guilbeault, The Rise of Bots: Implications for Politics, Policy, and Method, they state:

The results of our quantitative analysis confirm that bots reached positions of measurable influence during the 2016 US election. … Altogether, these results deepen our qualitative perspective on the political power bots can enact during major political processes of global significance. …
Most concerning is the fact that companies and campaigners continue to conveniently undersell the effects of bots. … Bots infiltrated the core of the political discussion over Twitter, where they were capable of disseminating propaganda at mass-scale. … Several independent analyses show that bots supported Trump much more than Clinton, enabling him to more effectively set the agenda. Our qualitative report provides strong reasons to believe that Twitter was critical for Trump’s success. Taken altogether, our mixed methods approach points to the possibility that bots were a key player in allowing social media activity to influence the election in Trump’s favour. Our qualitative analysis situates these results in their broader political context, where it is unknown exactly who is responsible for bot manipulation – Russian hackers, rogue campaigners, everyday citizens, or some complex conspiracy among these potential actors.
Despite growing evidence concerning bot manipulation, the Federal Election Commission in the US showed no signs of recognizing that bots existed during the election. There needs to be, as a minimum, a conversation about developing policy regulations for bots, especially since a major reason why bots are able to thrive is because of laissez-faire API access to websites like Twitter. …
The report exposes one of the possible reasons why we have not seen greater action taken towards bots on behalf of companies: it puts their bottom line at risk. Several company representatives fear that notifying users of bot threats will deter people from using their services, given the growing ubiquity of bot threats and the nuisance such alerts would cause. … We hope that the empirical evidence in this working paper – provided through both qualitative and quantitative investigation – can help to raise awareness and support the expanding body of evidence needed to begin managing political bots and the rising culture of computational propaganda.

This is a serious issue that requires immediate action, if not voluntarily by social media providers, such as Facebook and Twitter, then by law. We cannot afford to have another election hijacked by secret AIs posing as real people.

As Etzioni stated in his editorial:

My rule would ensure that people know when a bot is impersonating someone. We have already seen, for example, @DeepDrumpf — a bot that humorously impersonated Donald Trump on Twitter. A.I. systems don’t just produce fake tweets; they also produce fake news videos. Researchers at the University of Washington recently released a fake video of former President Barack Obama in which he convincingly appeared to be speaking words that had been grafted onto video of him talking about something entirely different.

See: Langston, Lip-syncing Obama: New tools turn audio clips into realistic video (UW News, July 11, 2017). Here is the University of Washington YouTube video demonstrating their dangerous new technology. Seeing is no longer believing. Fraud is a crime and must be enforced as such. If the government will not do so for some reason, then self- regulations and individual legal actions may be necessary.

In the long term Oren’s first point about the application of laws is probably the most important of his three proposed rules: An A.I. system must be subject to the full gamut of laws that apply to its human operator. As mostly lawyers around here at this point, we strongly agree with this legal point. We also agree with his recommendation in the NYT Editorial:

Our common law should be amended so that we can’t claim that our A.I. system did something that we couldn’t understand or anticipate. Simply put, “My A.I. did it” should not excuse illegal behavior.

We think liability law will develop accordingly. In fact, we think the common law already provides for such vicarious liability. No need to amend. Clarify would be a better word. We are not really terribly concerned about that. We are more concerned with technology governors and behavioral restrictions, although a liability stick will be very helpful. We have a team membership openings now for experienced products liability lawyers and regulators.


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