Abstract
This paper proposes a model of the Artificial Autonomous Moral Agent (AAMA), discusses a standard of moral cognition for AAMA, and compares it with other models of artificial normative agency. It is argued here that artificial morality is possible within the framework of a “moral dispositional functionalism.” This AAMA is able to read the behavior of human actors, available as collected data, and to categorize their moral behavior grounded in moral patterns herein. The present model is grounded in several analogies among artificial cognition, human cognition, and moral action. It is premised on the idea that moral agents should not be built on rule-following procedures, but on learning patterns from data. This idea is rarely implemented in AAMA models, albeit it has been suggested in the machine ethics literature (W. Wallach, C. Allen, J. Gips and especially M. Guarini). As an agent-based model, this AAMA constitutes an alternative to the mainstream action-centric models proposed by K. Abney, M. Anderson and S. Anderson, R. Arkin, T. Powers, W. Wallach, i.a. Moral learning and moral development of dispositional traits play here a fundamental role in cognition. By using a combination of neural networks and evolutionary computation, called “soft computing” (H. Adeli, N. Siddique, S. Mitra, L. Zadeh), the present model reaches a certain level of autonomy and complexity, which illustrates well “moral particularism” and a form of virtue ethics for machines, grounded in active learning. An example derived from the “lifeboat metaphor” (G. Hardin) and the extension of this model to the NEAT architecture (K. Stanley, R. Miikkulainen, i.a.) are briefly assessed.
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Notes
- 1.
One can assume that moral agents can be of a human nature, without being individuals: groups, companies, or any decision-making structure (e.g. the military line of command) or possess individuality, without being humans: animals, artificial agents, supranatural beings, etc.
- 2.
There are two areas of research germane to the present analysis: “ethics of emergent technologies” and “machine ethics.” The former has focused mainly on the moral implications, both intrinsic and extrinsic, of the dissemination of technologies in the society. In most cases, the ethical responsibility belongs to humans (or to human groups or communities): they are the moral decision-makers and the responsibility bearers: e.g. the field of “robo-ethics” focuses upon the use of robots and the societal concerns in the deployment of robots as well as the impact on our system of values (Wallach 2014). The ethics of technology has to be complemented with another, more balanced human-machine interaction, which has become relevant relatively recently: the ethics of the actions and decisions of machines, when they interact autonomously, mainly with humans. This relatively new area of study is “machine ethics,” which raises new interesting ethical issues, beyond the problems of emergent technologies (Allen and Wallach 2009; Abney et al. 2011; Anderson and Anderson 2011; Gunkel 2012; Trappl 2013, 2015; Wallach 2014; Pereira and Saptawijaya 2016). It enquires the ethics of our relation with technology, when humans are not the sole moral agents.
- 3.
We prefer to talk here about complexity as a measure of machine’s interaction with humans, the environment, etc. and not as its a feature per se. The problems with technology, Wallach writes, “often arise out of the interaction of those components [specific technology, people, institutions, environment, etc.] with other elements of the sociotechnical system” (Wallach 2015, 34).
- 4.
See the proposal for banning fully autonomous weapons by the Human Rights Watch (Human Rights Watch 2013).
- 5.
The literature refers to the autonomous moral agent and the artificial moral agent under the acronym AMA. AAMA refers here to the “artificial autonomous moral agent.”
- 6.
For a comprehensive formal definition, albeit somehow dated, see (Franklin and Graesser 1997).
- 7.
This model does not explore the unsupervised learning, deep learning, the reinforcement moral learning, which are all equally promising. Supervised learning is less exploratory in nature, and more related to discovery of patterns in data than to exploring data. See recent developments on machine learning in Big Data (Bishop 2007; Murphy 2012; Liu et al. 2016; Suthaharan 2016).
- 8.
Aristotle, Thomas Aquinas, Descartes, Pascal, Kant, and some contractarians such as Hobbes, Gauthier and Rawls talked about the morality of non-human, or non-individual agents. Probably a threshold concept is the idea of “just institutions,” upheld by several contractarians during the Enlightenment period, who showed why sometimes social structures can be “just” or “fair.” When Rawls introduced the two principles of justice, he added that the term “person” on some occasions means “human individuals,” but may refer also to “nations, provinces, business firms, churches, teams, and so on. The principles of justice apply in all these instances, although there is a certain logical priority to the case of human individuals” (Rawls 1958, 166). Rawls, Gauthier, and, as we’ll see, Danielson, require from the moral agents to be rational. We have in mind here the “constrained maximization theory,” where to be a rational agent means that the moral constraint must be conditional upon others’ co-operation (Gauthier 1987).
- 9.
A version of the precautionary principle is: “Where an activity raises threats of harm to the environment or human health, precautionary measures should be taken even if some cause and effect relationships are not fully established scientifically” (Wingspread participants 1998). See a discussion on precautionary principle about nanotechnology in Clarke (2005) and Allhoff (2014).
- 10.
- 11.
The behavioral-reading agents include animals, non-individual agents and, as we argue here, some AAMAs.
- 12.
The concepts of “moral judgment,” “moral reasoning,” and moreover “moral justification” are harder to capture by any AAMA model. One requirement of AAMA is to justify its actions by reconstructing its decisions. Justification can be a posteriori rationalization of moral actions.
- 13.
In many areas of science, models use a “functional stratification” in which the explanatory power of a model operates at one level only.
Investigation at one level “is black-boxing” everything that happens at lower levels. The lower levels are reduced to a functional specification. See M. Strevens for a comprehensive discussion on “boxing” (black-boxing and gray-boxing) in science (2008).
- 14.
- 15.
Danielson talks about moral functionalism, but because the central role rationality plays in this argument, we are using here the name designate his view as “rational moral functionalism.”
- 16.
Here is a definition of this type of supervenience: “for any two worlds that are descriptively exactly alike in other respects, in the attitudes that people have, in the effects to which actions lead, and so on, if x in one is descriptively exactly like y in the other, then x and y are exactly alike in moral respects” (Jackson and Pettit 1995, 22).
- 17.
Moral properties are role-filling properties and, in analytical moral functionalism, moral terms are similar to theoretical terms in D. Lewis’ sense (Lewis 1983).
- 18.
This artificial morality is responsive. Moral agents “must limit the class of agents with which they share cooperative benefits” to other rational agents. The adaptive moral agents in Danielson can include corporations, artificial agents, institutions, as far as they display a moralized rational interaction. He also proves that moral constraints (e.g., “keeping a promise”) are rational and can be implemented in artificial agents.
- 19.
Another way to put it is to compare egoism with utilitarianism, which are both rational, and to conclude that rationality alone cannot be a complete guide to morality. This is, in Sidgwick’s words, the “dualism of practical reason” and constitutes for some the “profoundest problem of ethics” that affects especially utilitarianism (Sidgwick 1930, 386). Jackson talks about naïve and mature naïve folk morality: the latter stage is obtained after one weeds out inconsistencies and counterintuitive parts of the former (Jackson 1998, 133).
- 20.
Moral functionalism per se is compatible with the main directions in ethics. There is a rule-based functionalism, a utilitarian functionalism, a “rights” functionalism. For example, when one postulates a strong deterministic connection between the input and the output, with no exceptions, then a “rule functionalism” is at work.
- 21.
- 22.
The analogy between the moral development of a human agent and the general process of learning, developing skills, efficient finding of the optimal solution, or choosing between different alternatives, is a convoluted topic: a deep analysis of each corresponding analogies is left for another occasion.
- 23.
Both skills and virtues are practical abilities, and, in line with Aristotle’s ethics, “exercising a virtue involves practical reasoning of a kind that can illuminatingly be compared to the kind of reasoning we find in someone exercising a practical skill” (Annas 2011, 16).
- 24.
This section reiterates the lines of argument presented by D. Howard and I. Muntean in a previous work (2014).
- 25.
For the hard/soft distinction in computing, see Sect. 7.8 below.
- 26.
- 27.
We use here the intuition of fragmented space in relation to the mathematical concept of separability.
- 28.
- 29.
A single-layer network is composed of one input layer of input units and one layer of output units such that all output units are directly connected to the input units. A multi-layer network has hidden layers that interface the input units and the output units such that output units are not directly connected to input units, but only to hidden units.
- 30.
The approach that uses partially H-2 and explicitly H-3 is (Guarini 2006). Guarini trained a number of artificial neural networks on a set of problems about “X killed Y in this and this circumstances” and managed to infer (predict) moral behaviors for another set of test cases. Guarini’s conclusion runs somehow against moral particularism, because some type of moral principles is needed (including some “contributory principles”), but it also shows that particularism is stronger than it seems. The simple recursive artificial neural network makes decisions without exceptionless moral principles, although it needs some “contributory” principles, which play a central role, Guarini argues, in moral reclassification.
- 31.
Another supportive schema is to take EC as fundamental and let a NN generate the initial population of the EC (the EC + NN). The “collaborative combination” is when EC and NN work in the same time to solve a problem.
- 32.
Some constraints such as number of hidden layers, simplicity, speed etc., can be added as conditions of the evolutionary process (Yao 1999).
- 33.
A rule-based system can include the constraints (equalities or inequalities) among the input vector.
- 34.
The intuition here is that factually impossible situations leave the agent with no options to choose from.
- 35.
In a more evolved model, vector Ψ can encode frames or mental states of the agent. In the lifeboat example, it is used to flag out the situations in which the intention is bribery, or personal interest in saving a specific person, to obtain material possessions of the passengers and disregard their lives, etc.
- 36.
The intentions of H as an actor in the model, whose intentions are merely input data, are not mapped on any internal functions of the AAMA: they are just data, with no special status.
- 37.
For more information about the NEAT and its versions developed at the University of Texas at Austin, see: http://nn.cs.utexas.edu/?neat.
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Acknowledgments
We received constructive comments, helpful feedback and encouraging thoughts at different stages of this project. We want to thank Colin Allen, Anjan Chakravartty, Patrick Foo, Ben Jantzen, Dan Lapsley, Michael Neelon, Tom Powers, Tristram McPherson, Abraham Schwab, Emil Socaciu, Wendell Wallach, an anonymous reviewer for this journal, colleagues from the University of Notre Dame, Indiana Purdue University in Fort Wayne, University of North Carolina, Asheville, and the University of Bucharest, Romania, who supported this project. We want also to express our gratitude to the organizers, referees, and the audience of the following events: “Robo-philosophy 1” conference (Aarhus, Denmark, June 2014); APA Eastern Division meeting (December 2014); APA Central Division meeting (February 2015); the Department of Philosophy at Virginia Tech; the CCEA/ICUB Workshop on artificial morality at the University of Bucharest (June 2015); the IACAP-CEPE-INSEIT joint conference at the University of Delaware (June 2015) and the IACAP conference in Ferrara, Italy (June 2016); the Department of Philosophy at Purdue University; and the “The Ethical and Moral Considerations in Non-Human Agents” (EMCAI) symposium of the AAAI held at Stanford University (March 2016). This paper is complemented by two papers: (Howard and Muntean 2014; Howard and Muntean 2016). The MATLAB code and the data used are available upon request: please contact the corresponding author.
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Howard, D., Muntean, I. (2017). Artificial Moral Cognition: Moral Functionalism and Autonomous Moral Agency. In: Powers, T. (eds) Philosophy and Computing. Philosophical Studies Series, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-319-61043-6_7
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