Some prominent scientists and philosophers have stated openly that moral and political considerations should influence whether we accept or promulgate scientific theories. This widespread view has significantly influenced the development, and public perception, of intelligence research. Theories related to group differences in intelligence are often rejected a priori on explicitly moral grounds. Thus the idea, frequently expressed by commentators on science, that science is “self-correcting”—that hypotheses are simply abandoned when they are undermined by empirical evidence—may not be correct in all (...) contexts. In this paper, documentation spanning from the early 1970s to the present is collected, which reveals the influence of scientists’ moral and political commitments on the study of intelligence. It is suggested that misrepresenting findings in science to achieve desirable social goals will ultimately harm both science and society. (shrink)

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. (...) We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines. (shrink)

Famously, scientific theories are underdetermined by their evidence. This occurs in the factor analytic model, which is often used to connect concrete data to hypothetical notions. After introducing FA, three general topics are addressed. Underdetermination: the precise reasons why FA is underdetermined illuminates various claims about underdetermination, abduction, and theoretical terms. Uncertainties: FA helps distinguish at least four kinds of uncertainties. The prevailing practice, often encoded in statistical software, is to ignore the most difficult kinds, which are essential to FA's (...) underdetermination. What to do: some suggestions for dealing with these hardest types of uncertainty are offered. (shrink)

Within the context of psychological measurement, realist commitments pervade methodology. Further, there are instances where particular scientific practices and decisions are explicable most plausibly against a background assumption of epistemic realism. That psychometrics is a realist enterprise provides a possible toehold for Stephen Jay Gould’s objections to psychometrics in The Mismeasure of Man and Joel Michell’s charges that psychometrics is a “pathological science.” These objections do not withstand scrutiny. There are no fewer than three activities in ongoing psychometric research which (...) presuppose a commitment to a minimal epistemic realism. Those activities include selecting between different models for representing data, estimating ability in the context of item response theory, and the move to make the individual the fundamental unit of analysis in psychometrics thereby calling for a shift in what sorts of data are evidentially relevant. In none of these activities are the commitments and disregard for evidence that Gould and Michell find objectionable or “pathological.”. (shrink)