In the 1970s, we conceived of a rule explanation as supplying the causal and social context that justifies a rule, an objective documentation for why a rule is correct. Today we would call such descriptions post-hoc design rationales, not proving the rules? correctness, but providing a means for later interpreting why the rule was written and facilitating later improvements.

Fundamental assumptions behind qualitative modelling are critically considered and some inherent problems in that modelling approach are outlined. The problems outlined are due to the assumption that a sufficient set of symbols representing the fundamental features of the physical world exists. That assumption causes serious problems when modelling continuous systems. An alternative for intelligent system building for cases not suitable for qualitative modelling is proposed. The proposed alternative combines neural networks and quantitative modelling.

Theoretical commentaries on AI often operate as a metadiscourse on the way in which science represents itself to a wider public. The sciences and humanities do the same kind of work but in different fields that encourage them to talk about their work differently: science refers to a natural world that does not talk back, and the humanities refer continually to a world with communicative people in it. This paper suggests that much AI commentary is misconceived because it models itself (...) on the way that science represents itself, rather than on the actual practice of science.AI theorists have become increasingly worried about the lack of evaluation in AI, the lack of reflexivity, and the lack of contact with society. Frequently these writers turn to concepts of tacit knowledge to work through these worries. In doing so they are recognising the problem of AI's second-order representation of science and trying to deal with it. However, this recognition of a problem with the representations of science simply turns back to the legitimation crisis of Western politics where many commentators use science precisely as a ‘model’ for western political institutions. They do so because science is one of the few areas of knowledge where it has been legitimate to use plausible methodology for representation that allows for arbitrary designations of authority as well as parallel systems of different authority. However, the plausible rejects any control on reflexivity, assumes an ethnocentric club culture and does not address social context.It is in this sense that the problems of legitimation in political liberalism are similar to those of legitimation in sciences, both are rooted in their uses of representation. AI's link with the representation of science places it in the heart of this debate about legitimacy. This paper suggests that AI does need to learn about reflexivity and that it might well do so by looking at the recent work on experimentation and representation by historians of science, and by looking to the debates about representation by historians of science, and by looking to the debates about representation within the humanities. However, reflexivity may not be enough. Devising rules of thumb for the appropriate halting of reflexivity, is also needed to address social context and take action. (shrink)