Hacker would remind us that the central questions are not answered by science/scientists if only because they are enmeshed in conceptual confusion of a very basic kind. It would be, for example, far better to speak of the mind, and that in a way not reducible to brain states or functions. “Consciousness science” makes questionable assumptions or rests on eminently arguable presuppositions about human nature, personal identity, our mental lives and the associated myriad capacities and capabilities (including the nature of action and agency in the case of human animals or persons; mind is but a façon de parler for speaking of uniquely human powers and their exercise). Finally, I am one of those who think depression, be it commonplace or clinical, is not necessarily a disease (any more than alcoholism) in a biomedical sense (although sometimes it is tied to organic maladies of one kind or another, and pharmacology may provide some relief for its more severe manifestations or symptoms), while dementia can be, and has been, more clearly defined in biomedical terms and thus is not, in your words or sense, fundamentally a “disorder of consciousness” except perhaps somehow figuratively. (Anecdotally, I routinely interact with a woman roughly my age with, at least for now, mild dementia, and I cannot imagine a good reason to warrant describing her affliction as a ‘disorder of consciousness’). And I do not think any ethic(s) need depend on more knowledge of the neural bases of conscious states of awareness if only because that could be said to be only a necessary (basis) yet not sufficient condition of what is meant by “conscious states” (that something has a neural basis can only go so far when it comes to explanation, meaning, and so forth).

That said, my concern about funding was specific, whereas you raise it in domains outside the target or remit of my original and principal concern. However, my interest in the instant case is part of more generalized concerns in which our case could be said to illustrate or exemplify the proverbial big picture in the natural sciences and sciences surrounding the latest technologies, those having to do with changes in the conduct of science since, roughly, the last quarter of the 20th century with the emergence of what the late physicist John Ziman called “post-academic” science, i.e., that which effectively replaced an “academic” science captured to a greater or lesser degree by the ethos of Mertonian norms (after Robert Merton): “prescriptions, proscriptions, preferences and permissions” he believed scientists came to feel bound to [in part, a result of training/socialization/peer pressure] as intrinsic to the disciplinary character of their research. This scientific ethos or ethic was more or less captured by five fundamental norms or regulative principles: Communalism, Universalism, Disinterestedness, Originality, and Skepticism (‘CUDOS’). These are no doubt idealized, and there were, invariably, violations or exceptions ranging from minor to gross of these norms, at least in some sciences, but their intent and function can be appreciated in contrast to the post-academic world of science (the term itself presumes some continuity with what preceded it); in Ziman’s words, “In less than a generation we have witnessed a radical, irreversible, worldwide transformation in the way that science is organized, managed and performed.” Thus,

“[A] norm of utility is being injected into every joint of the research culture. Discoveries are evaluated commercially before they have been validated scientifically. [….] Scientists themselves are seldom in a good position to assess the utility of their work, so expert peer review is enlarged into ‘merit review’ by non-specialist ‘users.’ … [A]s researchers become more dependent on project grants, the ‘Matthew Effect’ is enhanced. Competition for real money takes precedence over competition for scientific credibility as the driving force of science. With so many researchers relying completely on research grants or contracts for their personal livelihood, winning these become an end in itself. Research groups are transformed into small business enterprises. The metaphorical forum of scientific opinion is turned into an actual market in research services.”

Post-academic or what I would call hyper-technological industrialized science, by contrast,

“contravenes these norms at almost every point. [….] Very schematically, industrial science is Proprietary, Local, Authoritarian, Commissioned, and Expert. It produces proprietary knowledge that is not necessarily made public. It is focused on local technical problems rather than on general understanding. Industrial researchers act under managerial authority rather than as individuals. Their research is commissioned to achieve practical goals, rather than undertaken in the pursuit of knowledge. They are employed as expert problem-solvers, rather than for their personal creativity. It is no accident, moreover, that these attributes spell out ‘PLACE.’ That, rather than ‘CUDOS,’ is what you get for doing good industrial science.”

The following, I think, can help us tease out provocative similarities to what is occurring today in the AI research community:

“Post-academic science is organized on market principles. One of the consequences of this is that the post-academic research project is subordinate to the sphere of influence of bodies with the corresponding material interests. Thus, for example, basic research findings in molecular genetics have potential applications in plant breeding. Agro-chemical firms and farmers are therefore deemed to have a legitimate right to influence the course of this research, from the formulation of projects to the interpretation of outcomes.
 
In general … post-academic natural scientists can usually be trusted to tell ‘nothing but the truth,’ on matters about which they are knowledgeable. But unlike academic scientists, they are not bound to tell ‘the whole truth.’ They are often prevented, in the interests of their employers, clients or patrons, from revealing discoveries or expressing doubts that would put a very different complexion on their testimony. The meaning of what is said is secretly undermined by what is not said. This proprietorial attitude to the results of research has become so familiar that we have forgotten how damaging it is to the credibility of scientists and their institutions.This is one result of the fact that ‘the context of application’ is largely defined by the material interests of bodies outside science.” [This is certainly the case with AI.]

For better on occasion and thus more often for worse,

“the problems that activate post-academic science are often deeply rooted in history, and are typically ‘owned’ by well-established institutions, such as pharmaceutical companies, arms procurement agencies, associations of engineering and medical practitioners, environmental protection commissions, economic advisory councils, and so on. This elaborate social structure is associated with an equally elaborate epistemic structure, where the ‘problem areas’ are differentiated much more arbitrarily, and are often narrow and specialized [despite the well-known fact that many of the issues tackled by science and society demand a ‘transdisciplinary’ approach], than they are in academic science.
 
In short, writes Ziman, we have “increasing subordination to corporate and political interests that do not put a high value on the production of knowledge for the benefit of society at large.” Put differently, philosophers can help science by being not only under-laborers, but philosophers in a more critical, big picture sense, with values and ethical dispositions oriented around sensibilities common to the the greater social, political, and would-be (Liberal-) democratic world (this Liberalism, after J.S. Mill, John Dewey and even John Rawls, is perfectly compatible with socialism). This world is universalizable, thus questions of distributive justice are often unavoidably global.