Behavioural artificial intelligence: an agenda for systematic empirical studies of artificial inference
0301 basic medicine
bepress|Arts and Humanities|Philosophy
150
bepress|Arts and Humanities|Philosophy|Philosophy of Science
SocArXiv|Arts and Humanities
02 engineering and technology
100
Social and Behavioral Sciences
Science and Technology Studies
SocArXiv|Arts and Humanities|Philosophy
SocArXiv|Arts and Humanities|Philosophy|Philosophy of Science
Philosophy of Science
Philosophy
03 medical and health sciences
bepress|Social and Behavioral Sciences
0202 electrical engineering, electronic engineering, information engineering
SocArXiv|Social and Behavioral Sciences
Arts and Humanities
bepress|Social and Behavioral Sciences|Science and Technology Studies
SocArXiv|Social and Behavioral Sciences|Science and Technology Studies
bepress|Arts and Humanities
DOI:
10.1007/s00146-019-00928-5
Publication Date:
2019-12-18T09:04:05Z
AUTHORS (2)
ABSTRACT
Artificial Intelligence (AI) receives attention in media as well as in academe and business. In media coverage and reporting, AI is predominantly described in contrasted terms, either as the ultimate solution to all human problems or the ultimate threat to all human existence. In academe, the focus of computer scientists is on developing systems that function, whereas philosophy scholars theorize about the implications of this functionality for human life. In the interface between technology and philosophy there is, however, one imperative aspect of AI yet to be articulated: How do intelligent systems make inferences? We use the overarching concept “Artificial Intelligent Behaviour” which would include both cognition/processing and judgment/behaviour. We argue that due to the complexity and opacity of Artificial Inference, one needs to initiate systematic empirical studies of artificial intelligent behavior similar to what has previously been done to study human cognition, judgment and decision making. This will provide valid knowledge, outside of what current computer science methods can offer, about the judgments and decisions made by intelligent systems. Moreover, outside academe – in the public as well as the private sector – expertise in epistemology, critical thinking and reasoning are crucial to ensure human oversight of the artificial intelligent judgments and decisions that are made, because only competent human insight into AI-inference processes will ensure accountability. Such insights require systematic studies of AI-behaviour founded on the natural sciences and philosophy, as well as the employment of methodologies from the cognitive and behavioral sciences.
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