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.


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.


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