University of Michigan, Ann Arbor

Jeudi 18 janvier, 15:30-16:30, Salle Z-330, Pavillon Claire-McNicoll

Cette présentation sera en anglais.

Traditional approaches to moral development have emphasized
the explicit teaching of norms, e.g., via parental instruction, or the
acquisition of behavioral dispositions by “social learning”, e.g., via
infant imitation and modeling of observed behaviors, or progression
through a fixed set of developmental “stages”. But what if we understood
moral learning as closer to causal learning and the development of
commonsense physics? Developmental evidence suggests that infants early
on begin to model their physical environment and its possibilities
(Gopnik & Schulz, 2004), using observation but receiving very limited
explicit instruction or external reinforcement. Similarly, there is
evidence that infants early on begin learning a kind of commonsense
psychology that enables them to model others’ behavior in terms of
intentional states, once again, using observation but very limited
explicit instruction or external reinforcement (Wellman, 2014). These
internal models enable infants to interact reasonably successfully with
their physical and social environment even if they are unable to
articulate the causal or psychological principles involved—the knowledge
underlying these capacities is therefore generalizable despite being
implicit, and so is spoken of as intuitive. Internal models are not
limited to causal and predictive information, however, but also appear
to encode evaluative information, including evaluation of possible
actions or third-party social interactions for such features as
helpfulness, harm, knowledgeability, and trustworthiness (Hamlin et al.,
2011; Doebel & Koenig, 2013). When combined with an implicit capacity to
empathically simulate the mental states of others, these evaluative
capacities can underwrite a kind of intuitive learning of commonsense
morality. Such learning occurs without much explicit instruction in
moral principles, yet with a capacity to generalize and with some degree
of moral autonomy—so that by age 3-4, infants will resist conforming to
imposed rules that involve harm or unfairness toward others (Turiel, 2002).

To be genuinely intelligent, artificial systems will need to possess the
kinds of intuitive knowledge involved in commonsense physics and
psychology. And to be both autonomous and trustworthy, artificial
systems will need to be able to evaluate situations, actions, and agents
in the terms of such categories of commonsense morality as helpfulness,
harm, knowledgeability, and trustworthiness. Deep learning approaches
suggest how intuitive knowledge of the kind involved in predictive
learning might be acquired and represented, without being “programmed
in” or explicitly taught. How might further developments of these
approaches make possible the acquisition of intuitive evaluative
knowledge of the kind involved in commonsense epistemic or moral assessment?

Biographie :

Peter Railton is the G.S. Kavka Distinguished University
Professor in the Department of Philosophy at the University of Michigan,
Ann Arbor. His main research has been in ethics and the philosophy of
science, focusing especially on questions about the nature of
objectivity, value, norms, and explanation. Recently, he has also begun
working in aesthetics, moral psychology, and the theory of action, and
on the bearing of empirical research in these areas. Among his
publications are Facts, Values, and Norms (Cambridge), a collection of
some of his papers in ethics and meta-ethics, and Homo Prospectus
(Oxford), a joint project with psychology and neuroscience on basic
mental architecture. He has been a visiting professor at Berkeley and
Princeton, and he has received fellowships from the ACLS, the NEH, and
the Guggenheim Foundation. He is a former President of the American
Philosophical Association (Central Division) and is a Fellow of the
American Academy of Arts and Sciences.

Venez nombreux !