Abstract
Theories of moral, and more generally, practical reasoning sometimes draw on the notion of coherence. Admirably, Paul Thagard has attempted to give a computationally detailed account of the kind of coherence involved in practical reasoning, claiming that it will help overcome problems in foundationalist approaches to ethics. The arguments herein rebut the alleged role of coherence in practical reasoning endorsed by Thagard. While there are some general lessons to be learned from the preceding, no attempt is made to argue against all forms of coherence in all contexts. Nor is the usefulness of computational modelling called into question. The point will be that coherence cannot be as useful in understanding moral reasoning as coherentists may think. This result has clear implications for the future of Machine Ethics, a newly emerging subfield of AI.
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Notes
Figure 1 grossly oversimplifies Thagard’s account of explanatory coherence since it does not consider the role that is granted to simplicity, unification, and other factors.
Thagard frequently abbreviates in presenting constraint networks. For example, strictly speaking, “Paul Bernardo should not be executed” cannot be deduced from “Capital punishment is wrong”, which are two of the propositions in his constraint network. The proposition “Executing Paul Bernardo is an instance of capital punishment” needs to be added in order for the deduction to go through. The last proposition is not in the constraint network; I take this to be an abbreviation. Except where noted, I will follow this strategy myself, often speaking of entailments and assuming that it is understood that propositions need to be added for the deductions to hold. However, it should be kept in mind that Thagard is claiming to model deductive coherence.
Thagard suggests that deliberative coherence, with the goal of promoting the overall good, can be used to capture utilitarian concerns, and deductive coherence can be used to capture roughly Kantian concerns.
By use of the term “views” I am including all elements involved in practical reasoning, including not only beliefs but goals as well.
I am assuming that the evidence node in the network is an abbreviation and that there must be at least one piece of evidence in favour of capital punishment being a deterrent, and one further piece of evidence in favour of capital punishment not being a deterrent. A further abbreviation is that the special evidence unit (which would be connected to each unit representing an evidence statement) is omitted. Since the SEU has its value clamped at 1, I omit it in calculations of the number of possible partitions since its value is never an issue (given that it never changes during a simulation). I consider only nodes whose values are at issue in determining the number of possible partitions.
The cases are inspired by chapter three of Laurence BonJour’s The Structure of Empirical Knowledge.
In empirical matters, we often defer to someone else’s expertise. Jasmine is a physics major and has performed impeccable experiments in a lab to determine the speed of light in different media. Jane may then acquire beliefs about the speed of light based on Jasmine’s testimony. Jane may be justified in her beliefs in some deferential sense. It is not that she has conducted any experiments or can consciously muster any direct (or non-deferential) evidence to support her views; rather, she defers to the judgement of someone who can, or at least at some point, has acquired the direct evidence. Perhaps something like this is the case in moral reasoning. Jane may defer to Jasmine’s judgement on some moral issue or set of moral issues, which she knows Jasmine has thought a lot about (and we will assume that Jasmine is an excellent moral reasoner). Perhaps Jane can acquire some sort of deferential justification in this way even though she cannot give direct reasons for why she holds some specific view. However, not all justification can be indefinitely deferred. As some point, there needs to be someone who can offer direct reasons and acquires justification in a non-deferential sense. It is this non-deferential sense of justification of which I write in this paper.
Ernest Sosa (1997) has made the distinction between animal or prereflective justification, on the one hand, and reflective justification on the other. Of course, Sosa is speaking of the epistemology of empirical knowledge, not of moral epistemology. Still, some might be tempted to suggest that coherence rankings can be arrived at unconsciously, yielding a kind of prereflective justification; that would be to miss the point of what is being argued for herein. Among the contributors to coherence and incoherence in Thagard’s model are arguments and counterarguments, which are offered and considered by reflective agents. While I do not wish to deny that there may be a prereflective (or subreflective) dimension to moral cognition, when the type of cognition we are examining begins to consider arguments and counterarguments, surely we have entered the realm of reflective cognition. The kind of justification being considered in this paper is reflective justification. There may well be different defensible senses of the term “justification”, and no claim is made here that responsible, conscious articulation of reasons captures all senses of that term. However, if global coherence theories (whether understood as internalist or externalist) are incapable of explaining much of what we consider important in reflective and responsible moral cognition, then so much the worse for such theories with respect to understanding reflective cognition and its normative dimensions.
Thagard has never said anything like this. The objections considered in this section come from informal exchanges with several colleagues over the last few years.
The coherentist could also argue that in the future we will acquire beliefs such that adding P1 to those beliefs will lead to a higher coherence rating than our current set of beliefs. Assuming that the preceding is possible, the burden is clearly on the coherentist to provide those further beliefs.
Trying to argue that the overall justification of P1 comes in part from its foundational status and in part from its coherence with other beliefs inherits the problems of both approaches: P2 still has a better claim to being foundational than P1, and P1 would not fit in well with the rest of our beliefs.
Notice: it has not been asserted that coherence can play no role whatsoever in constructing an AMI. The arguments in this paper are not strong enough to establish that point. To see why I am making the preceding qualification, consider an android that gets around in the world about as well as a 5-year-old child. Imagine that it is unable to linguistically articulate or consciously reflect on its moral standards, but it does have standards because it refrains from biting, kicking, or otherwise harming humans, and it even makes attempts to break up fights that occur among human children. In some sense, this android has a kind of moral intelligence (even if it cannot articulate or reflect on why it behaves the way it does). Could this kind of intelligence be generated by a coherence engine of some sort? I do not know. No argument in this paper is strong enough, on its own, to rule out such a possibility. The kind of intelligence that has been focused on herein is reflective intelligence, and it is with respect to such intelligence that I have raised concerns about the use of coherence.
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Acknowledgements
I wish to thank Andrew Bailey, Pierre Boulos, and Paul Thagard for comments and questions during the early stages of the work that eventually lead to this paper. I would also like to thank the participants at both the Dartmouth AI@50 conference (July 2006) and the North American Computing and Philosophy Conference (August 2006) for valuable input. For financial assistance during the writing of this paper, I am indebted to the Social Sciences and Humanities Research Council of Canada.
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Appendix
Appendix
The arguments fleshed out below (in favour and against capital punishment) are versions of the deductive arguments in Thagard’s Bernardo example. The idea here is not, in any way, to attempt a complete rendering of the arguments in question. Rather, my claim is that even if we start to fill in just a few of the details of the deductive considerations, it will become difficult for most people to carry out the required computations.
It is my view that many of the principles Thagard cites in his version of the Bernardo example have implicit ceteris paribus clauses and do not license deductions; at best, they license conclusions that are prima facia valid but subject to defeat. The logic of the application of these principles strikes me as non-monotonic. However, Thagard appears to give them a deductive, monotonic treatment. I have attempted to fill in the missing premises that are required for the deductions to go through. If still further premises are required to preserve deductive form, then that further aids my case (since increasing the number of premises leads to an increase in the amount of computation required to solve the coherence problem, and my argument turns on computational difficulty). Moreover, further arguments might be added to this simulation. The statement, “Capital punishment neither prevents serious crime nor promotes a sense of security”, is not used to draw any conclusions, but it could be used to do so, increasing the number of links to existing propositions or to new propositions, and that increases the number of computations required. Of course, arguments not even related to those already in the simulation could be added, increasing complexity even further.
In favour of capital punishment
Promoting a sense of security is justified.
Capital punishment prevents serious crime and promotes a sense of security.
Capital punishment will promote a sense of security and prevent serious crime if applied to those who committed serious crimes.
Therefore, capital punishment is justified for persons who have committed serious crimes.
Preventing serious crimes is justified.
Capital punishment prevents serious crime and promotes a sense of security.
Capital punishment will promote a sense of security and prevent serious crime if applied to those who committed serious crimes.
Therefore, capital punishment is justified for persons who have committed serious crimes.
Capital punishment is justified for persons who have committed serious crimes.
Paul Bernardo is a person who has committed serious crimes.
Therefore, Paul Bernardo should be executed.
Against capital punishment
Killing a defenceless person is wrong.
Paul Bernardo is a defenceless person.
Therefore, Paul Bernardo should not be executed.
Capital punishment always requires killing a defenceless person.
Killing a defenceless person is wrong.
Therefore, capital punishment is wrong for all persons.
Capital punishment is wrong for all persons.
Paul Bernardo is a defenceless person.
Therefore, Paul Bernardo should not be executed.
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Guarini, M. Computation, Coherence, and Ethical Reasoning. Minds & Machines 17, 27–46 (2007). https://doi.org/10.1007/s11023-007-9056-4
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DOI: https://doi.org/10.1007/s11023-007-9056-4