Coincidentally, I ended up reading Evolutionary Psychology: Controversies, Questions, Prospects, and Limitations today, and noticed that it makes a number of points that could be interpreted in a similar light: in that humans do not really have a "domain-general rationality", and that instead we have specialized learning and reasoning mechanisms, each of which are carrying out a specific evolutionary purpose and which are specialized for extracting information that's valuable in light of the evolutionary pressures that (used to) prevail. In other words, each of them carries out inferences that are designed to further some specific evolutionary value that helped contribute to our inclusive fitness.

The paper doesn't spell out the obvious implication, since that isn't its topic, but it seems pretty clear to me: since our various learning and reasoning systems are based on furthering specific values, our philosophy has also been generated as a combination of such various value-laden systems, and we can't expect an AI reasoner to develop a philosophy that we'd approve of unless its reasoning mechanisms also embody the same values.

That said, it does suggest a possible avenue of attack on the metaphilosophy issue... figure out exactly what various learning mechanisms we have and which evolutionary purposes they had, and then use that data to construct learning mechanisms that carry out similar inferences as humans do.

Quotes:

Hypotheses about motivational priorities are required to explain empirically discovered phenomena, yet they are not contained within domain-general rationality theories. A mechanism of domain-general rationality, in the case of jealousy, cannot explain why it should be “rational” for men to care about cues to paternity certainty or for women to care about emotional cues to resource diversion. Even assuming that men “rationally” figured out that other men having sex with their mates would lead to paternity uncertainty, why should men care about cuckoldry to begin with? In order to explain sex differences in motivational concerns, the “rationality” mechanism must be coupled with auxiliary hypotheses that specify the origins of the sex differences in motivational priorities. [...]

The problem of combinatorial explosion. Domain-general theories of rationality imply a deliberate cal- culation of ends and a sample space of means to achieve those ends. Performing the computations needed to sift through that sample space requires more time than is available for solving many adaptive problems, which must be solved in real time. Consider a man coming home from work early and discovering his wife in bed with another man. This circumstance typically leads to immediate jealousy, rage, violence, and sometimes murder (Buss, 2000; Daly & Wilson, 1988). Are men pausing to rationally deliberate over whether this act jeopardizes their paternity in future offspring and ultimate reproductive fitness, and then becoming enraged as a consequence of this rational deliberation? The predictability and rapidity of men’s jealousy in response to cues of threats to paternity points to a specialized psychological circuit rather than a response caused by deliberative domain-general rational thought. Dedicated psychological adaptations, because they are activated in response to cues to their corresponding adaptive problems, operate more efficiently and effectively for many adaptive problems. A domain-general mechanism “must evaluate all alternatives it can define. Permutations being what they are, alternatives increase exponentially as the problem complexity increases” (Cosmides & Tooby, 1994, p. 94). Consequently, combinatorial explosion paralyzes a truly domain-general mechanism (Frankenhuis & Ploeger, 2007). [...]

In sum, domain-general mechanisms such as “rationality” fail to provide plausible alternative explanations for psychological phenomena discovered by evolutionary psychologists. They are invoked post hoc, fail to generate novel empirical predictions, fail to specify underlying motivational priorities, suffer from paralyzing combinatorial explosion, and imply the detection of statistical regularities that cannot be, or are unlikely to be, learned or deduced ontogenetically. It is important to note that there is no single criterion for rationality that is independent of adaptive domain. [...]

The term learning is sometimes used as an explana- tion for an observed effect and is the simple claim that something in the organism changes as a consequence of environmental input. Invoking “learning” in this sense, without further specification, provides no additional explanatory value for the observed phenomenon but only regresses its cause back a level. Learning requires evolved psychological adaptations, housed in the brain, that enable learning to occur: “After all, 3-pound cauliflowers do not learn, but 3-pound brains do” (Tooby & Cosmides, 2005, p. 31). The key explanatory challenge is to identify the nature of the underlying learning adaptations that enable humans to change their behavior in functional ways as a consequence of particular forms of environmental input.

Although the field of psychology lacks a complete understanding of the nature of these learning adaptations, enough evidence exists to draw a few reasonable conclu- sions. Consider three concrete examples: (a) People learn to avoid having sex with their close genetic relatives (learned incest avoidance); (b) people learn to avoid eating foods that may contain toxins (learned food aversions); (c) people learn from their local peer group which actions lead to increases in status and prestige (learned prestige criteria). There are compelling theoretical arguments and empirical evidence that each of these forms of learning is best explained by evolved learning adaptations that have at least some specialized design features, rather than by a single all-purpose general learning adaptation (Johnston, 1996). Stated differently, evolved learning adaptations must have at least some content-specialized attributes, even if they share some components. [...]

These three forms of learning—incest avoidance, food aversion, and prestige criteria—require at least some content-specific specializations to function properly. Each op- erates on the basis of inputs from different sets of cues: coresidence during development, nausea paired with food ingestion, and group attention structure. Each has different functional output: avoidance of relatives as sexual partners, disgust at the sight and smell of specific foods, and emulation of those high in prestige. It is important to note that each form of learning solves a different adaptive problem.

There are four critical conclusions to draw from this admittedly brief and incomplete analysis. First, labeling something as “learned” does not, by itself, provide a satisfactory scientific explanation any more than labeling something as “evolved” does; it is simply the claim that environmental input is one component of the causal process by which change occurs in the organism in some way. Second, “learned” and “evolved” are not competing explanations; rather, learning requires evolved psychological mechanisms, without which learning could not occur. Third, evolved learning mechanisms are likely to be more numerous than traditional conceptions have held in psychology, which typically have been limited to a few highly general learning mechanisms such as classical and operant conditioning. Operant and classical conditioning are important, of course, but they contain many specialized adaptive design features rather than being domain general (Ohman & Mineka, 2003). And fourth, evolved learning mechanisms are at least somewhat specific in nature, containing particular design features that correspond to evolved solutions to qualitatively distinct adaptive problems.

I always suspected that natural kinds depended on an underdetermined choice of properties, but I had no idea there was or could be a theorem saying so. Thanks for pointing this out.

Does a similar point apply to Solomonoff Induction? How does the minimum length of the program necessary to generate a proposition, vary when we vary the properties our descriptive language uses?