Diane Proudfoot

Given actual autonomous systems with capacities for harm and the public’s apparent willingness to take moral advice from large language models (LLMs), Einar Duenger Bohn’s (2024) renewed discussion of the Moral Turing Test (MTT) is timely. Bohn’s aim is to defend an unequivocally behavioural test. In this paper, I argue against this direction. Interpreted as testing mere behaviour, the Turing test is a poor test of either intelligence or moral agency, and neither Bohn’s version of the test nor Allen, Varner and Zinser’s influential (2000) version avoids these problems. Also, the MTT’s advantages as advertised by Bohn do not hold up: it is an open empirical question whether embracing a merely behavioural MTT will significantly reduce the challenges in building a computer to pass the test. This issue aside, I argue that Turing’s actual test is superior to current AI benchmarks and to Bohn’s version of the test. A test of moral reasoning or agency in machines that is based on Turing’s actual test has advantages over current tests of moral reasoning in AI, including versions of the MTT. Such a test is an intriguing possibility still to be investigated.

Given actual autonomous systems with capacities for harm and the public’s apparent willingness to take moral advice from large language models (LLMs), Einar Duenger Bohn’s (2024) renewed discussion of the Moral Turing Test (MTT) is timely. Bohn’s aim is to defend an unequivocally behavioural test. In this paper, I argue against this direction. Interpreted as testing mere behaviour, the Turing test is a poor test of either intelligence or moral agency, and neither Bohn’s version of the test nor Allen, Varner and Zinser’s influential (2000) version avoids these problems. Also, the MTT’s advantages as advertised by Bohn do not hold up: it is an open empirical question whether embracing a merely behavioural MTT will significantly reduce the challenges in building a computer to pass the test. This issue aside, I argue that Turing’s actual test is superior to current AI benchmarks and to Bohn’s version of the test. A test of moral reasoning or agency in machines that is based on Turing’s actual test has advantages over current tests of moral reasoning in AI, including versions of the MTT. Such a test is an intriguing possibility still to be investigated.

The modern project of naturalizing intelligence began in the middle of last century, and Alan Turing is one of its most celebrated proponents. The assumption that Turing shared the ontological and methodological commitments of canonical naturalists is based on certain widespread beliefs about Turing—namely, that his test of intelligence is behaviourist and his approach to the mind computationalist. This chapter argues that influential versions of these assumptions are false, and instead that, in his claim that intelligence is an ‘emotional concept’, Turing proposed a response-dependence approach to intelligence. Recent objections, however, take the response-dependence interpretation to commit Turing to anti-realism—a philosophical stance that is typically anathema to naturalists. This chapter argues that those objections fail.

In this paper I argue that Turing proposed a new approach to the concept of thinking, based on his claim that intelligence is an ‘emotional concept’; and that the response-dependence interpretation of Turing’s ‘criterion for “thinking”’ is a better fit with his writings than orthodox interpretations. The aim of this paper is to clarify the response-dependence interpretation, by addressing such questions as: What did Turing mean by the expression ‘emotional’? Is Turing’s criterion subjective? Are ‘emotional’ judgements decided by social consensus? Turing’s take on these issues impacts current philosophical debates on response-dependent concepts and on the nature of artificial intelligence.

In the 70 years since Alan Turing’s ‘Computing Machinery and Intelligence’ appeared in Mind, there have been two widely-accepted interpretations of the Turing test: the canonical behaviourist interpretation and the rival inductive or epistemic interpretation. These readings are based on Turing’s Mind paper; few seem aware that Turing described two other versions of the imitation game. I have argued that both readings are inconsistent with Turing’s 1948 and 1952 statements about intelligence, and fail to explain the design of his game. I argue instead for a response-dependence interpretation. This interpretation has implications for Turing’s view of free will: I argue that Turing’s writings suggest a new form of free will compatibilism, which I call response-dependence compatibilism. The philosophical implications of rethinking Turing’s test go yet further. It is assumed by numerous theorists that Turing anticipated the computational theory of mind. On the contrary, I argue, his remarks on intelligence and free will lead to a new objection to computationalism.

Recent work in social robotics, which is aimed both at creating an artificial intelligence and providing a test-bed for psychological theories of human social development, involves building robots that can learn from ‘face-to-face’ interaction with human beings — as human infants do. The building-blocks of this interaction include the robot’s ‘expressive’ behaviours, for example, facial-expression and head-and-neck gesture. There is here an ideal opportunity to apply Wittgensteinian conceptual analysis to current theoretical and empirical work in the sciences. Wittgenstein’s philosophical psychology is sympathetic to embodied and situated Artificial Intelligence (see Proudfoot, 2002, 2004b), and his discussion of facial-expression is remarkably modern. In this chapter, I explore his approach to facial-expression, using smiling as a representative example, and apply it to the canonical interactive face robot, Cynthia Breazeal’s Kismet (see e.g. Breazeal, 2009, 2002). I assess the claim that Kismet has expressive behaviours, with the aim of generating philosophical insights for AI.

Turing was probably the first person to advocate the pursuit of robotics as a route to Artificial Intelligence and Wittgenstein the first to argue that, without the appropriate history, no machine could be intelligent. Wittgenstein anticipated much recent theorizing about the mind, including aspects of connectionist theo- ries of mind and the situated cognition approach in AI. Turing and Wittgenstein had a wary respect for each other and there is significant overlap in their work, in both the philosophy of mathematics and the philosophy of AI. Both took (what would now be called) an externalist stance with respect to machine intelligence. But whereas Turing was concerned only with behaviour, Wittgenstein emphasized in addition history and environment. I show that Wittgenstein's externalist analysis of psychological capacities entails that most, even all, future "artificially intelligent" computers and robots will not use language, possess concepts, or reason. The ar- gument tells, not against AI, but only against AI's traditional and romantic goal of building an artificial "res cogitans" - as first embraced by Turing and now ex- emplified in the work of Brooks and others on cognitive robotics. This argument supports the stance of the growing number of AI researchers whose aim is to pro- duce, not thinking and understanding machines, but high-performance "advanced information processing systems."

In this article the central philosophical issues concerning human-level artificial intelligence (AI) are presented. AI largely changed direction in the 1980s and 1990s, concentrating on building domain-specific systems and on sub-goals such as self-organization, self-repair, and reliability. Computer scientists aimed to construct intelligence amplifiers for human beings, rather than imitation humans. Turing based his test on a computer-imitates-human game, describing three versions of this game in 1948, 1950, and 1952. The famous version appears in a 1950 article in Mind, ‘Computing Machinery and Intelligence’ (Turing 1950). The interpretation of Turing's test is that it provides an operational definition of intelligence (or thinking) in machines, in terms of behavior. ‘Intelligent Machinery’ sets out the thesis that whether an entity is intelligent is determined in part by our responses to the entity's behavior. Wittgenstein frequently employed the idea of a human being acting like a reliable machine. A ‘living reading-machine’ is a human being or other creature that is given written signs, for example Chinese characters, arithmetical symbols, logical symbols, or musical notation, and who produces text spoken aloud, solutions to arithmetical problems, and proofs of logical theorems. Wittgenstein mentions that an entity that manipulates symbols genuinely reads only if he or she has a particular history, involving learning and training, and participates in a social environment that includes normative constraints and further uses of the symbols.