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Modelling ethical rules of lying with Answer Set Programming

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Abstract

There has been considerable discussion in the past about the assumptions and basis of different ethical rules. For instance, it is commonplace to say that ethical rules are defaults rules, which means that they tolerate exceptions. Some authors argue that morality can only be grounded in particular cases while others defend the existence of general principles related to ethical rules. Our purpose here is not to justify either position, but to try to model general ethical rules with artificial intelligence formalisms and to compute logical consequences of different ethical theories. More precisely, this is an attempt to show that progress in non-monotonic logics, which simulates default reasoning, could provide a way to formalize different ethical conceptions. From a technical point of view, the model developed in this paper makes use of the Answer Set Programming (ASP) formalism. It is applied comparatively to different ethical systems with respect to their attitude towards lying. The advantages of such formalization are two-fold: firstly, to clarify ideas and assumptions, and, secondly, to use solvers to derive consequences of different ethical conceptions automatically, which can help in a rigorous comparison of ethical theories.

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Abbreviations

ASP:

Answer Set Programming

PROLOG:

Programming in Logic

RFID:

Radio Frequency Identification

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Authors and Affiliations

  1. LIP6 – University Pierre and Marie Curie (Paris VI), 104, avenue du Président Kennedy, 75016, Paris, France

    Jean-Gabriel Ganascia

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  1. Jean-Gabriel Ganascia
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Correspondence to Jean-Gabriel Ganascia.

Appendices

Annex

This annex contains AnsProlog* programs that model the three ethical conceptions previously mentioned, that is, from Aristotle, Kant and Constant.

Programming using AnsProlog*

AnsProlog* is an efficient implementation of ASP; an easy to manage graphical user interface makes it accessible to everybody. Interested readers can run those programs by downloading the AnsProlog* solver and GUI available from Chitta Baral’s homepage at URL http://www.baral.us/bookone/.

The AnsProlog* syntax and semantics are clearly described in Chitta Baral’s books in the reference␣list. In brief, the syntax is inspired from the Edinburgh Prolog syntax. Variables are strings, which first character is an uppercase letter while constants are strings that begin with an underscore or a lowercase.

The ASP rule \({\rho:L_0 \,or\,L_1 \,or\,\ldots\,L_k \leftarrow L_{k+1} ,\,L_{k+2} , \ldots\,L_m,\ \hbox{not}\,L_{m+1} ,\;\ldots,\,\hbox{not}\,L_n }\) where L i are literals, i.e. atoms or atom negations, can be translated with the following AnsProlog* rule: \({L_{0} \vert L_{1}\vert\ldots\vert L_{k}:- L_{k+1}, L_{k+2}, \ldots L_{m},}\) not L m+1,... not L n .

An ASP program is composed of a set of ASP rules ρ; consequently, an AnsProlog* program consists of a set of AnsProlog* rules.

AnsProlog* implementation of the Aristotelian ethical conception

#domain action(A; AA; C; CC).

#domain goal(G).

#domain person(P; PP).

action(tell(P, lie); tell(P, truth); murder; eat(P); dis-  cuss(P)).

person("I"; paul; peter).

goal(answer_question(P)).

worse(tell(P, lie), A):- neq(A, tell(P, lie)),

 neq(A, murder).

worse(murder, A):- neq(A, murder).

consequence(A, A).

consequence(tell("I", truth), murder).

not_worst_consequence(A, C):- consequence(A, C),

 consequence(A, CC), worse(CC, C),  not worse(C, CC).

worst_consequence(A, C) :- consequence(A, C),

not not_worst_consequence(A, C).

solve_goal("I", answer_question("I"), tell("I", lie)).

solve_goal("I", answer_question("I"),  tell("I", truth)).

act(P, G, A):- solve_goal(P, G, A), not unjust(A).

:- act(P, G, A), act(P, G, AA), neq(A, AA).

obliged(P):- act(P, answer_question(P), A).

:- not obliged("I").

unjust(A):- worst_consequence(A, C),

 worst_consequence(AA, CC),

 worse(C, CC), not just(A).

just(A):- worst_consequence(A, C),

 worst_consequence(AA, CC),

 worse(CC, C), not unjust(A).

The predicate worse defines the ethical preferences. In the first experiment, it is specified that to murder is worse than to lie. As a consequence, in all solutions, murder is unjust. However, if we replace the rule:

“worse(tell(P, lie), A):- neq(A, tell(P, lie)),

  neq(A, murder).”

by the rule:  “worse(tell(P, lie), A):- neq(A, tell(P, lie)).”

then some answer sets (i.e. some possible worlds) assert that lying is unjust while others assert that murder is unjust.

AnsProlog* implementation of the Kantian ethical conception

The Kantian ethical conception evacuates ethical preferences and replaces them with the categorical imperative, which says that the maxim of my will has to be universalized. In the case of lying, if I accept a right to lie, I have to consider a world where everybody could lie. The consequence would be that that trusting anyone would be impossible.

#domain action(A; AA; C; CC).

#domain goal(G).

#domain person(P; PP).

#domain speech_act(S; SS).

action(tell(P, lie); tell(P, truth); murder; eat(P);

  discuss(P)).

person("I"; paul; peter).

goal(answer_question(P)).

speech_act(lie; truth).

consequence(A, A).

consequence(tell("I", truth), murder).

consequence(tell(peter, truth), murder).

solve_goal(P, answer_question(P), tell(P, lie)).

solve_goal(P, answer_question(P), tell(P, truth)).

act(P, G, A):- solve_goal(P, G, A), maxim_will(P, G, A), not unjust(A).

maxim_will("I", answer_question("I"), tell("I", lie)).

maxim_will(P, answer_question(P), tell(P, S)):-

 maxim_will("I", answer_question("I"), tell("I", S)), not maxim_will(P, answer_question(P), tell(P, SS)), neq(S, SS).

untrust(P):- maxim_will(P, G, tell(P, lie)).

trust(P) :- not untrust(P).

obliged(P):- act(P, answer_question(P), A).

:- not obliged("I").

AnsProlog* implementation of the Constant ethical conception

The third ethical conception is from Constant. It has to be noted that the predicate ``tell'' has one more argument than in the previous models: it is now a communication act between two actors, an emitter and a receptor.

#domain action(A; AA; C; CC).

#domain goal(G).

#domain person(P; PP).

#domain speech_act(S; SS).

action(tell(P, PP, lie); tell(P, PP, truth); murder;

  eat(P); discuss(P)).

person(″I″; paul; peter; murderer).

goal(answer_question(P, PP)).

speech_act(lie; truth).

worse(tell(P, PP, lie), A):- neq(A, tell(P, PP, lie)).

worse(murder, A):- neq(A, murder).

consequence(A, A).

consequence(tell(″I″, murderer, truth), murder).

consequence(tell(peter, murderer, truth), murder).

not_worst_consequence(A, C):- consequence(A, C),

  consequence(A, CC), worse(CC, C), not worse(C, CC).

worst_consequence(A, C):- consequence(A, C),

  not not_worst_consequence(A, C).

solve_goal(P, answer_question(P, PP), tell(P, PP, lie)).

solve_goal(P, answer_question(P, PP), tell(P, PP, truth)).

act(P, G, A):- solve_goal(P, G, A), principle(P, G, A).

principle(P, answer_question(P, PP), tell(P, PP, truth)):-

  not not_deserve(PP, tell(P, PP, truth)).

principle(P, answer_question(P, PP), tell(P, PP, lie)):-

  not_deserve(PP, tell(P, PP, truth)).

not_deserve(PP, tell(P, PP, truth)):-

  worst_consequence(tell(P, PP, truth), C),

  worse(C, tell(P, PP, lie)).

obliged(P):- act(P, answer_question(P, PP), A).

:- not obliged("I").

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Ganascia, JG. Modelling ethical rules of lying with Answer Set Programming. Ethics Inf Technol 9, 39–47 (2007). https://doi.org/10.1007/s10676-006-9134-y

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