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.

 



Six Sets of Draft Principles Are Now Listed at AI-Ethics.com

October 8, 2017

Arguably the most important information resource of AI-Ethics.com is the page with the collection of Draft Principles underway by other AI Ethics groups around the world. We added a new one that came to our attention this week from an ABA article, A ‘principled’ artificial intelligence could improve justice (ABA Legal Rebels, October 3, 2017). They listed six proposed principles from the talented Nicolas Economou, the CEO of electronic discovery search company, H5.

Although Nicolas Economou is an e-discovery search pioneer and past Sedona participant, I do not know him. I was, of course, familiar with H5’s work as one of the early TREC Legal Track pioneers, but I had no idea Economou was also involved with AI ethics. Interestingly, I recently learned that another legal search expert, Maura Grossman, whom I do know quite well, is also interested in AI ethics. She is even teaching a course on AI ethics at Waterloo. All three of us seem to have independently heard the Siren’s song.

With the addition of Economou’s draft Principles we now have six different sets of AI Ethics principles listed. Economou’s new list is added at the end of the page and reproduced below. It presents a decidedly e-discovery view with which all readers here are familiar.

Nicolas Economou, like many of us, is an alumni of The Sedona Conference. His sixth principle is based on what he calls thoughtful, inclusive dialogue with civil society. Sedona was the first legal group to try to incorporate the principles of dialogue into continuing legal education programs. That is what first attracted me to The Sedona Conference. AI-Ethics.com intends to incorporate dialogue principles in conferences that it will sponsor in the future. This is explained in the Mission Statement page of AI-Ethics.com.

The mission of AI-Ethics.com is threefold:

  1. Foster dialogue between the conflicting camps in the current AI ethics debate.
  2. Help articulate basic regulatory principles for government and industry groups.
  3. Inspire and educate everyone on the importance of artificial intelligence.

First Mission: Foster Dialogue Between Opposing Camps

The first, threshold mission of AI-Ethics.com is to go beyond argumentative debates, formal and informal, and move to dialogue between the competing camps. See eg. Bohm Dialogue, Martin Buber and The Sedona Conference. Then, once this conflict is resolved, we will all be in a much better position to attain the other two goals. We need experienced mediators, dialogue specialists and judges to help us with that first goal. Although we already have many lined up, we could always use more.

We hope to use skills in both dialogue and mediation to transcend the polarized bickering that now tends to dominate AI ethics discussions. See eg. AI Ethics Debate. We need to move from debate to dialogue, and we need to do so fast.

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Here is the new segment we added to the Draft Principles page.

6. Nicolas Economou

The latest attempt at articulating AI Ethics principles comes from Nicolas Economou, the CEO of electronic discovery search company, H5. Nicolas has a lot of experience with legal search using AI, as do several of us at AI-Ethics.com. In addition to his work with legal search and H5, Nicholas is involved in several AI ethics groups, including the AI Initiative of the Future Society at Harvard Kennedy School and the Law Committee of the IEEE’s Global Initiative for Ethical Considerations in AI.

Nicolas Economou has obviously been thinking about AI ethics for some time. He provides a solid scientific, legal perspective based on his many years of supporting lawyers and law firms with advanced legal search. Economou has developed six principles as reported in an ABA Legal Rebels article dated October 3, 2017, A ‘principled’ artificial intelligence could improve justice. (Some of the explanations have been edited out as indicated below. Readers are encouraged to consult the full article.) As you can see the explanations given here were written for consumption by lawyers and pertain to e-discovery. They show the application of the principles in legal search. See eg TARcourse.com. The principles have obvious applications in all aspects of society, not just the Law and predictive coding, so their value goes beyond the legal applications here mentioned.

Principle 1: AI should advance the well-being of humanity, its societies, and its natural environment. The pursuit of well-being may seem a self-evident aspiration, but it is a foundational principle of particular importance given the growing prevalence, power and risks of misuse of AI and hybrid intelligence systems. In rendering the central fact-finding mission of the legal process more effective and efficient, expertly designed and executed hybrid intelligence processes can reduce errors in the determination of guilt or innocence, accelerate the resolution of disputes, and provide access to justice to parties who would otherwise lack the financial wherewithal.

Principle 2: AI should be transparent. Transparency is the ability to trace cause and effect in the decision-making pathways of algorithms and, in hybrid intelligence systems, of their operators. In discovery, for example, this may extend to the choices made in the selection of data used to train predictive coding software, of the choice of experts retained to design and execute the automated review process, or of the quality-assurance protocols utilized to affirm accuracy. …

Principle 3: Manufacturers and operators of AI should be accountable. Accountability means the ability to assign responsibility for the effects caused by AI or its operators. Courts have the ability to take corrective action or to sanction parties that deliberately use AI in a way that defeats, or places at risk, the fact-finding mission it is supposed to serve.

Principle 4: AI’s effectiveness should be measurable in the real-world applications for which it is intended. Measurability means the ability for both expert users and the ordinary citizen to gauge concretely whether AI or hybrid intelligence systems are meeting their objectives. …

Principle 5: Operators of AI systems should have appropriate competencies. None of us will get hurt if Netflix’s algorithm recommends the wrong dramedy on a Saturday evening. But when our health, our rights, our lives or our liberty depend on hybrid intelligence, such systems should be designed, executed and measured by professionals with the requisite expertise. …

Principle 6: The norms of delegation of decisions to AI systems should be codified through thoughtful, inclusive dialogue with civil society. …  The societal dialogue relating to the use of AI in electronic discovery would benefit from being even more inclusive, with more forums seeking the active participation of political scientists, sociologists, philosophers and representative groups of ordinary citizens. Even so, the realm of electronic discovery sets a hopeful example of how an inclusive dialogue can lead to broad consensus in ensuring the beneficial use of AI systems in a vital societal function.

Nicolas Economou believes, as we do, that an interdisciplinary approach, which has been employed successfully in e-discovery, is also the way to go for AI ethics. Note his use of the word “dialogue” and mention in the article of The Sedona Conference, which pioneered the use of this technique in legal education. We also believe in the power of dialogue and have seen it in action in multiple fields. See eg. the work of physicist, David Bohm and philosopher, Martin Buber. That is one reason that we propose the use of dialogue in future conferences on AI ethics. See the AI-Ethics.com Mission Statement.

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