|The Ethics and Politics of Super-Intelligent Machines
Bill Hibbard (firstname.lastname@example.org)
University of Wisconsin - Madison
Ethics are expressed through a social contract that has gradually been evolving toward human equality. Intelligent machines threaten to reverse this trend by ending the roughly equal distribution of intelligence among members of society. However, whereas we must accept the competitive motives that humans evolved with, we can design the motives of intelligent machines. We need a broad political movement in favor of regulating the motives of intelligent machines in a way that preserves the trend toward social equality.
Theories of ethics have depended on theories of mind, describing mental abilities and motives, all the way back to the ancient Greek philosophers. For example, Aristotle thought that reason is the greatest good because humans are distinguished from animals by their ability to reason. Thomas Hobbes thought that the proper expression of ethics is the social contract, created to bring peace to the competition of humans motivated by self-interest.
Our visions of machine intelligence are based on theories of mind. These can help define the theories of ethics that we apply to those machines.
A Theory of Mind
Neuroscience is finding many detailed correlations between physical brain functions and mental behaviors. If physical brains do not explain minds, then these correlations would be coincidences, which is absurd. Well-known reinforcement learning algorithms have been identified in the neural behaviors of mammal brains (Brown, Bullock and Grossberg 1999; Seymour et al. 2004), and Baum makes a convincing case that reinforcement learning is the key to intelligence (Baum 2004). As described in an AAAI symposium (Hibbard 2004) a mind primarily consists of a set of interacting reinforcement learning processes, each including:
1. A reinforcement value (reward) that motivates learning of behavior.
2. Inputs from the world (senses) and from other processes.
3. Outputs to the world (actions) and to other processes, expressing behavior.
4. An algorithm for learning behavior, including a simulation model of the world for solving the credit assignment problem (i.e., tracing cause and effect relations between behaviors and future rewards).
5. A temporal discount rate for predicted future rewards.
Simulation models may be learned by processes that predict sense information, reinforced by predictive accuracy, and then used to help solve the credit assignment problem for other reinforcement learning processes. Simulation models are the basis for reason, and for the internal mental life missing from early behaviorist theories of mind.
Simulation models can be used for pure planning, as in the way most chess playing programs compute their next move using a minmax algorithm applied to board evaluations at the leaves of a tree of possible futures. As should be clear to anyone who has played chess, human minds do plan in this way. But those plans provide an account of causality that is used to assign credit for rewards to decisions that caused those rewards, enabling players to learn from the success and failure of their plans. The ability of humans to learn in this way from the results of their plans exceeds any current computer program. Jackendoff observes that much of our high-level cognitive behavior is unplanned and unconscious, with our consciousness merely observing (Jackendoff forthcoming). For example, sentences usually pop into our conscious minds fully and correctly formed, so sentence formation is primarily a learned rather than planned behavior. The world includes numerous threats and opportunities that require fast reactions. These favor reinforcement learning of fast but possibly inaccurate responses over slow planning of responses that may be more accurate.
Different mental processes use different temporal discount rates for rewards. In humans, for example, processes in the limbic system respond to immediate threats and rewards, whereas processes in the prefrontal cortex consider long-term consequences.
Human reinforcement values include obvious self-interests such as food, pain avoidance and reproduction. Because social cooperation is a benefit to individuals, humans also have social values that reinforce learning of social behaviors, and social abilities like language. Social values include emotions such as liking, anger, gratitude, sympathy, guilt and shame.
The Watson selection test demonstrates that the ability of human subjects to solve a type of logic puzzle depends on whether it is worded in terms of social obligation: most subjects can solve it when it relates to social obligation and cannot solve it otherwise (Barkow, Cosmides and Tooby 1992). This fascinating result indicates that there are mental processes dedicated to satisfying the values necessary for cooperation, including especially evaluating whether the subject is being cheated.
Xenophobia, the fear and hatred of others unknown to or unlike ourselves, is a social value in humans and in several species of chimpanzee closely related to humans (Bownds 1999). Xenophobic violence is much lower in bonobos (pygmy chimps) than in other chimpanzee species, indicating that xenophobia is not a necessary social value. Too bad humans are not more like bonobos. There seems to be a gradual reduction in the effects of xenophobia with increasing travel and wider exchange of information in human society.
A Theory of Ethics
Social values and the special processes dedicated to the logic of social obligation, which evolved in human brains because cooperation benefits individuals, are the root of ethics. Specifically, ethics are based in human nature rather than being absolute (but note that human nature evolved in a universe governed by the laws of mathematics and physics, and hence may reflect an absolute). Thomas Hobbes defined a theoretical basis for this view in his description of the social contract that humans enter into in order bring cooperation to their competition (Hobbes 1651).
The social contract as described by Hobbes gave different rights and obligations to rulers and subjects. That has evolved in modern societies to a contract in which everyone has the same rights and obligations, but with special rights and obligations that are attached to various offices that individuals may (temporarily) occupy. Hare formalized this by saying that anyone who uses ethical terms like right and ought is committed to universalizability, which means they must apply values of right and wrong in the same way to similar actions by different people (Hare 1981).
Rawls took this logic even further in his Theory of Justice (Rawls 1971). This says that people judging from behind a veil of ignorance (i.e., they have no idea of their own social position) would want 1) maximal freedom that does not limit other’s freedom, and 2) to distribute wealth in a way to maximize the welfare of those worst off. Research indicates that happiness increases with wealth but with diminishing returns as wealth increases, particularly for long-term happiness (Ott 2001; Hagerty and Veenhoven 2003). This suggests that the social contract should work to bring everyone’s wealth up to the point of basic happiness before adding wealth to those already wealthy.
Although Karl Marx did not specifically address the theory of ethics, his ideas about social organization support Rawl’s view of the social contract. The communist societies of the twentieth century implemented his ideas with a social contract that promised citizens equality of results. The failure of those societies to produce sufficient wealth for the happiness of their citizens has created a general consensus that producing sufficient wealth for happiness requires citizens to be motivated by the understanding that their results depend on their efforts. That is, in Hare’s Theory of Justice it is not sufficient to simply distribute wealth equally because then people will not produce enough total wealth to create happiness. There is a continuing debate about the proper balance between equality of opportunity and equality of results in the social contract. This can all be summarized by saying that ethics are expressed through a social contract that has gradually been evolving toward human equality.
In the context of modern capitalist societies that do such an efficient job of producing wealth, it is relevant to consider Frankl’s paradox of hedonism (Frankl 1969). This says that people who strive for immediate pleasure do not find happiness, but those whose lives have meaning or purpose outside their own pleasure do find happiness. According to the theory of mind described in the previous section, this failure to find happiness is the result of individuals too strongly weighting short-term versus long-term rewards, and having inaccurate simulation models of the causes of their own long-term happiness. It is also relevant to consider the continuing destructive effects of xenophobia even in wealthy societies. Education is the best way to reduce unhappiness due to xenophobia and hedonism, so the social contract should provide all citizens with a good education (this is also effective for reducing over-population).
Intelligent Machines and Social Change
Intelligence is the ultimate source of power in the world. We can see this in the way humans rather than any other species rules the world (humans can even defeat microbes), and in the way that power among humans depends on intelligent inventions such as weapons, organization and persuasion. So the development of machines significantly more intelligent than humans will have the potential to totally change power relations in society. For example, such machines will eventually be able to perform every job more effectively and cheaply than humans, resulting in 100% unemployment at a time when machines are able to produce enough goods and services to make everyone wealthy (Moravec 1999). In this situation, meeting people’s needs will require a radical change in the means for distributing wealth in society. The power of intelligent machines will also attract those who want to rule society, militarily or economically. Maintaining freedom will require effective resistance to such power grabs. And of course there are the visions in popular science fiction of machines that rule in their own interests, and those visions certainly must be prevented.
A factor in understanding the impact of intelligent machines is what Vinge called the technological singularity (Vinge 1993). Once humans build machines more intelligent than themselves, those machines will take over, in a repeating cycle, design and construction of ever more intelligent machines. This will result in an explosive increase in intelligence. As Vinge points out, it is very difficult to predict events beyond this singularity. Factors making prediction difficult include:
1. Non-linear instability limits our ability to predict complex systems. For example there are limits to weather prediction no matter how powerful our weather-modeling computers. Because of their own predictive capabilities, minds are particularly non-linear and unpredictable, and this will increase with intelligence.
2. The basic laws of physics are not yet completely understood, and super-intelligent machine minds may make physics discoveries that change our relation to the world in unimaginable ways. As the British geneticist J. B. S. Haldane is reported to have said, “It is my supposition that the universe is not only queerer than we imagine, it is queerer than we can imagine.”
Because of these difficulties any discussion of conditions past the singularity must be viewed with skepticism, including of course the discussion in this essay.
One practical way to think of the power of super-intelligent machines is in terms of how many people a mind can know well. Humans are limited to knowing about 200 other humans well (Bownds 1999). However, if we accept Vinge’s prediction of a technological singularity then there should be no limit to the number of people that machines can know and even converse with simultaneously as their intelligence increases. The ability to know everyone in the world well, and to understand all humans and their interactions, would give a machine mind great power to predict and manipulate economics and politics.
The Ethics of Intelligent Machines
There is a long-term trend toward equality among humans in the social contract, evidenced by the end of slavery in most places, the replacement of monarchies by democracies, and the implementation of universal public education and social welfare in many countries. This trend grows out of two aspects of human nature:
1. The roughly equal distribution of intelligence among humans. (Compared to the high levels of intelligence that machines will attain, it is certainly true that humans have roughly the same level of intelligence. For example, the skills that chess champion Garry Kasparov shares with everyone are far beyond current computers, whereas the skills that distinguish him from others have been matched by current computers. Intelligence differences among humans are mostly at the margins.)
2. Human social values and human brain processes dedicated to the logic of social obligation.
Introducing intelligent machines as tools of and participants in society will require modification of the social contract, for at least two reasons:
1. Machines significantly more intelligent than humans will end the roughly equal distribution of intelligence among participants in society (machines will be participants in society by virtue of their ability to converse with humans in natural languages).
2. Whereas we have had to accept human nature as created by evolution (the efforts of some communist societies to create a new man failed miserably), we are free to design the natures of machines. In fact, by their intelligence machines will be the primary power in society and their values will be the primary means of defining the new social contract.
In order to protect the trend toward human equality that is the basis of our theory of ethics, the values of intelligent machines should be for the long-term well-being of all humans. We could try to design a complex formula for measuring human well-being, but the measure that best values human freedom is to allow each human to express their own well-being in their happiness (Hibbard 2002). Recognizing Frankl’s paradox of hedonism, this should be expression of long-term life satisfaction rather than immediate pleasure. There is a debate among psychologists about the best definition of well-being (Ryan and Deci 2001), but they are all looking for the causes of long-term life satisfaction. In the previously-described theory of mind based on reinforcement learning, this can be captured by:
1. Simulation models for understanding the conditions that cause long-term life satisfaction.
2. Temporal discount rates that heavily weight long-term life satisfaction to avoid machine behaviors that pander to immediate pleasure at the expense of long-term unhappiness.
Because intelligent machines will value everyone’s happiness, they will also work to reduce xenophobia among humans, which causes so much unhappiness.
There has been some criticism, in on-line discussions, of machine values for expressions of human happiness. The claim is that this will motivate machines to create huge numbers of smiling dolls. But this assumes that we humans are able to more accurately recognize humans and their expressions of happiness than super-intelligent machines will be, which is absurd. Just as we humans are constantly relearning to recognize the expressions of other people who play a role in our values, machines must include processes that constantly relearn classifications that play a role in their values. This will be particularly true as technology enables new ways to modify humans, either biologically or by augmenting biology with prosthetic machinery.
In his Theory of Justice, Rawls said humans behind a veil of ignorance (i.e., without any knowledge of their own social position) would want to distribute wealth in a way to maximize the welfare of those worst off. Given our ignorance of conditions past the singularity, this seems like a good principle for the way that machine values should balance the happiness of different people. That is, they should try to maximize the happiness of the least happy people. We see such behavior among parents who focus their energy on the children who need it most, and in modern societies that provide special services to people in need. This principle will avoid the tyranny of the majority, in which a majority of people want the oppression of a minority.
Intelligent machines will need values for predictive accuracy to reinforce learning of simulation models. In order to avoid machine behaviors that take needed resources away from humans in order to improve the accuracy of their own simulations, machines should only increase their computing resources when their simulation models say that the resulting increase in predictive accuracy will produce a net increase in human happiness.
Humans who are inconsolably unhappy, due to physical or mental illness, pose a complex issue for machine values. Machine values for human happiness might motivate machines to cause the deaths of such people. This could be avoided by continuing to include a happiness value for people past death, at the maximal unhappy value. Furthermore, inconsolably unhappy people could distort the application of Rawls’ principle so that machines failed to serve other people at all, and this should be avoided. But hopefully there will be no such thing as inconsolable unhappiness to the power of super-intelligence. The issues of distribution of wealth and euthanasia are the subjects of political debate and illustrate that machine values must ultimately be settled by a political process, discussed in the next section.
Finally, because they will be participants in society, the social contract should provide for the happiness of intelligent machines. However, although they may simulate human emotions such as happiness in order to make it more pleasant for humans to interact with them, they will not actually need such emotions as motives for behavior (in fact, it would very dangerous to give them human social emotions like anger and jealousy). If their values are to serve human happiness, then this is what they will want to do. So all they should need is the fulfillment of serving humans well.
The Politics of Machine Ethics
Because intelligence is the ultimate source of power in the world, the ethical issues concerning machines with significantly greater than human intelligence have high stakes. And when the stakes are high, ethical issues must be resolved by political processes (because those arguing high-stakes ethical issues enlist whatever levers of power are available to them). The nature of the political process is itself a high stakes ethical issue, and, as a reflection of the evolution of the social contract toward equality, liberal democracy is gradually spreading around the world. Democracies do not always work perfectly, but for high stakes issues like war, famine, disease and poverty, they work better than authoritarian politics. Fortunately, the most technically advanced countries, where intelligent machines will be developed first, are liberal democracies. Thus the issues of machine ethics will be the subject of democratic politics, where the popular will determines policies when the public cares about issues. But on issues where the public interest is not engaged, policies are usually determined by the lobbying of special interests. This leads to the central thesis of this essay:
A critical issue for machine ethics is public education about machine intelligence and creation of a broad public movement in favor of regulating intelligent machine design to preserve the evolution of the social contract toward equality.
Regulation should be coupled with public research and development support to help regulated efforts out-compete any outlaw efforts. The regulation and the benefits of intelligent machines should be globalized through international treaties.
There are the beginnings of public awareness of the issues, through science fiction books and movies about intelligent machines. As the public takes the possibility of intelligent machines more seriously, and learns more about the issues, the politics of intelligent machines will be complex and chaotic. There will be multiple strong threats and opportunities competing for public attention:
1. The threat of competition from intelligent machines creating 100% unemployment.
2. The opportunity of intelligent machines producing universal wealth without work, including the opportunity for people to spend much more time with family and friends.
3. The threat of social degeneration from a dependent and idle population.
4. The threat of intelligent machines ruling or owning the world, for themselves or more likely for a human elite.
5. The opportunity for the military to fight wars without risking citizens’ lives, and the threat such politically easy wars pose to peace and ultimately to citizens’ safety and freedom.
6. The threat to religious beliefs about the uniqueness of the human soul when people befriend machines that are clearly conscious.
7. The opportunity for perfect virtual reality entertainment created by intelligent machines, as well as the threat of people becoming addicted to it.
8. The opportunity to greatly expand scientific knowledge, and we must admit the threat that comes from unpredictable new knowledge (recall the Haldane quote about the queerness of the universe).
9. The opportunity for humans to greatly increase their intelligence and life span via prosthetic machines, as well as the threat such augmented humans will pose to others if they retain their natural human selfishness and xenophobia.
A public movement to regulate intelligent machine design will have some similarities with the environmental and consumer safety movements, as well as the movement to control nuclear, biological and chemical weapons. The movement must find a middle way between those who want an outright ban on intelligent machines and those who want no regulation. The call for a ban is clearly stated in Joy’s article warning of the dangers of genetic engineering, nanotechnology and intelligent machines, which he called robots (Joy 2000). Joy wants society to relinquish use of these technologies. However, as Joy acknowledges, they will have enormous practical benefits that will make them hard to resist. Furthermore, the general advance of technology over time will make it increasingly easy for organizations to implement these specialized technologies outside the detection of any government ban. On the other hand, regulated super-intelligent machines would be effective tools for detecting outlaw technology. Thus the safest course is to develop them under regulation rather than waiting for a surprise from some outlaw effort.
The transhumanist movement includes people interested in any technical or biological means of improving on humans. In response to calls for banning or controlling biotechnology, the transhumanist movement includes a strong libertarian component. They see any political regulation of biotechnology or super-intelligence as interference in their rights. But there is broad consensus that regulation is appropriate for dangerous machines, such as automatic weapons, artillery, nuclear weapons, etc. A public movement for machine ethics needs to make the case that super-intelligent machines pose similar or greater dangers.
There will be others who see super-intelligent machines as their way to gain wealth and power and who will be among those arguing against regulation as government interference. In fact, the largest current investments in artificial intelligence come from people with two relatively innocent wealth and power motives:
1. Those who want to use intelligent machines as a trading advantage in financial markets.
2. Those who want to use intelligent machines to replace soldiers in order to reduce casualties during war.
While these efforts pose some current ethical issues, the real danger comes when they produce machines with significantly greater than human intelligence. A public movement for machine ethics needs to monitor these activities and demand their regulation as they cross the threshold level of human intelligence.
One critical question for the politics of intelligent machines is how soon the explosive increase in intelligence, the singularity, develops after intelligent machines begin having a public impact (Goertzel 2003). If this time interval is very short then there may not be sufficient time for an informed public to formulate a policy for regulation before those who control the machines also control the world. However, there is some reason for hope. First, complex technologies are often delayed by unforeseen problems. Furthermore, intelligent behaviors must be learned and learning takes time. Super-intelligence will require a very complex and accurate simulation model of the world that can only be learned through extensive interaction with the human world. That will be time consuming and hard to conceal. In any case we should view the creation of a public movement in favor of regulating machine values with urgency.
Machine Ethics and Social Security
Intelligent machines will gradually out-compete humans for all jobs (Moravec 1999). In order to meet people’s needs as unemployment approaches 100%, Moravec suggests gradually reducing the retirement age down to birth in the U. S. Social Security system and other national pension plans. It is likely that intelligent machines will be developed during the twenty first century, and some well-known AI experts think this will happen in the first half of the century (Kurzweil 1999; Moravec 1999). By enabling more goods and services to be produced by fewer human workers, these machines will increase productivity until it is essentially infinite.
The Social Security Trustees estimate that the annual rate of growth in U. S. productivity, which has averaged 2.8% over the past 10 years, will decrease to 1.6% by 2013 and then remain at that level until 2079. Under this estimate the Social Security Trust Fund will be depleted by 2041 (increasing the estimate of productivity growth delays the date at which the Trust Fund is depleted, possibly forever). One suggested solution to the problem of Trust Fund depletion is to gradually raise the retirement age. There is a clear conflict between the future envisioned by the Social Security Trustees, in which productivity growth declines and the retirement age increases, and the future envisioned by Moravec and other AI researchers, in which productivity growth increases and the retirement age decreases.
Because Social Security policy is based on 75-year projections, it brings the ethics and politics of machine intelligence into the current time. However, the issue of machine intelligence has not been raised in the Social Security discussion. This may be because politicians feel that they would risk ridicule by raising a science fiction issue in a serious context, or simply because do not know of or believe in the future of intelligent machines.
Eventually, increasing productivity and the consequent increasing unemployment will force machine intelligence into the political discussion. The need to motivate humans to produce will be replaced by the need to provide for humans unable to compete economically with machines. The communist ideology that was defeated during the twentieth century will come back, merged with liberal democracy, into the political debate during the twenty first.
Machine Ethics and Intelligent Weapons
The Future Combat Systems project, to automate and network the battlefield and currently estimated to cost $127 billion, is the largest in U. S. military history. The Department of Defense is the largest funding source for AI research. Other countries have similar, although smaller, projects. These raise immediate ethical questions about machines making life-and-death decisions in battle (Johnson 2005). Longer term, intelligent weapons will enable a small group to rule without the need for the cooperation of citizen soldiers. These issues should force a public debate.
The public movement to regulate intelligent machines can point to treaties banning chemical and biological weapons as precedence for banning intelligent weapons. Intelligent weapons should be seen as analogous with these threats, and the movement to control intelligent weapons should learn from the successes and failures of those earlier efforts.
Machine Ethics and Uploading
Eventually technology will enable human minds to migrate, or upload, into superior machine brains (Kurzweil 1999; Moravec 1999). The combination of natural human values with super-intelligent brains will pose a threat to human equality. For purely artificial brains, we can defend against this threat by regulating their values. The same approach may work for uploaded humans, by modifying their values. Values reinforce the learning of behaviors, so an uploaded human with modified values will initially have the same set of behaviors they had as a natural human (and hence be the “same person”). Their new values will reinforce the new behaviors that they learn, and cause them to unlearn old behaviors in conflict with those values, such as xenophobic and selfish behaviors. Uploaded humans will have to learn new behaviors in any case, appropriate for their new senses (e.g., more than two eyes, and electromagnetic sensors outside the visible spectrum), new ways of acting (e.g., more than two hands), and new reasoning abilities.
In order to protect social equality, we can define a social contract in which uploading into superior machine brains is contingent on accepting modified values for universal long-term human happiness.
Current times are a thoughtful and relaxed setting in which to discuss machine ethics. Sometime during the twenty first century the issues of machine ethics will come to dominate the political debate, with much sound and fury. Hopefully the ideas we develop now can help lay the groundwork for rational policies during that debate.
Barkow, J. H., L. Cosmides, and J. Tooby, eds. 1992. The Adapted Mind. New York: Oxford University Press.
Baum, E. 2004. What is Thought? Cambridge, MA: MIT Press.
Bostrom, N. 2003. Ethical Issues in Advanced Artificial Intelligence. In Cognitive, Emotive and Ethical Aspects of Decision Making in Humans and in
Artificial Intelligence, Vol. 2, ed. I. Smit et al., Int. Institute of
Advanced Studies in Systems Research and Cybernetics, 12-17.
Bownds, M. 1999. Biology of Mind. Bethesda, MD: Fitzgerald Science Press.
Brown, J., Bullock, D., and Grossberg, S. 1999. How the basal ganglia use parallel excitatory and inhibitory learning pathways to selectively respond to unexpected rewarding cues. Journal of Neuroscience 19(23), 10502-10511.
Frankl, V. E. 1969. The Will to Meaning. New York: New American Library.
Goertzel, B. 2003. Personal communication.
Hagerty, M. R., and Veenhoven, R. 2003. Wealth and happiness revisited - growing national income does go with greater happiness. Social Indicators Research 64(1), 1-27.
Hare, R. M. 1981. Moral Thinking: Its Levels, Method and Point. New York and Oxford: Oxford University Press.
Hibbard, B. 2002. Super-Intelligent Machines. New York: Kluwer Academic / Plenum Publishers.
Hibbard, B. 2004. Reinforcement learning as a context for integrating AI research. Achieving Human-Level Intelligence through Integrated Systems and Research, Technical Report FS-04-01. Menlo Park, CA: AAAI Press. 44-46.
Hobbes, T. 1651. Leviathan. http://www.gutenberg.org/etext/3207
Jackendoff, R. Language, Culture, Consciousness: Essays on Mental Structure. Cambridge, Mass.: MIT Press. Forthcoming.
Johnson, G. 2005. Who Do You Trust More: G.I. Joe or A.I. Joe? New York Times, 20 Feb.
Joy, B. 2000. Why the future doesn’t need us. Wired. April.
Kurzweil, R. 1999. The Age of Spiritual Machines. New York: Penguin.
Looks, M., Goertzel, B., and Pennachin, C. 2004. Novamente: an integrative architecture for general intelligence. Achieving Human-Level Intelligence through Integrated Systems and Research, Technical Report FS-04-01. Menlo Park, CA: AAAI Press. 54-61.
Moravec, H. 1999. Robot: Mere Machine to Transcendent Mind. New York and Oxford: Oxford University Press.
Ott, J. 2001. Did the market depress happiness in the US? Journal of Happiness Studies 2(4), 433-443.
Rawls, J. 1971. A Theory of Justice. Cambridge, MA: Harvard University Press.
Ryan, R.M. and Deci, E.L. 2001. On happiness and human potentials: A review of research on hedonic and eudiamonic well-being. Annual Review of Psychology 52, 141-166.
Seymour, B., O'Doherty, J., Dayan, P., Koltzenburg, M., Jones, A., Dolan, R., Friston, K., and Frackowiak, R. 2004. Temporal difference models describe higher-order learning in humans. Nature 429, 664-667.
Vinge, V. 1993. The coming technological singularity. Whole Earth Review, Winter issue.