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"Philosophy in the Trenches and Laboratory Benches of Science": An Essay by Mel Andrews


White house on hill

From The Philosopher, vol. 111, no. 1 ("Where is Philosophy Going?").

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“The philosophers of science have only ever interpreted science, in various ways. The point, however, is to change it.” – (perversion of) Karl Marx This special issue asks a highly salient meta-philosophical question: what will philosophy contribute to the world in the 21st century? My answer will be one of optimistic realism. As I am a philosopher of science, my answer will be specific to the societal role envisioned for philosophy of science which, in turn, will depend on the societal role foreseen for the sciences.

Philosophy of science and history of philosophy of science today have taken to conceptualising themselves as idle intellectual pursuits conducted from the secluded confines of the ivory tower. But this attitude could not be further from the truth or more harmful. Rather, these disciplines are uniquely poised to help coax science into a position of epistemic success and beneficence in the 21st century. Philosophy has a tremendous responsibility to humanity via intervention on broken scientific practices and broken supportive infrastructures for science. For we appear to be at a time of unprecedented conflict over the epistemic footing and methodological efficacy of science – a debate that is both internal to scientific practice and in the constant negotiation between science and the public (mediated, as ever, by media and by public policy).


Philosophy has a tremendous responsibility to humanity via intervention on broken scientific practices and broken supportive infrastructures for science.

The contested nature of modern science takes on an urgent note when considered in conjunction with the observation that we appear to be existentially dependent on the science of our time more than ever before in history. Climate scientists insist that the next few decades will be crucial for mitigating anthropogenic climate change. Three years into a global pandemic and the efficacy of vaccination and the appropriate level of government responsivity to medical knowledge are still open debates. Half of present-day human labour may be automated in the next 50 years; not only manual labour, but the most intimate outputs of the human mind: art, music, literature. This also includes the automation of the most consequential of human deliberations: AI now has influence over triage in hospitals, sentencing in the judicial system, child welfare, policing, and on and on. I speak only in tips and icebergs. None of the many moral crises and existential threats du jour are inevitable. We face an onslaught of evitable problems. Their evasion rests upon modern science. A weighty burden, if ever there was one. Is present-day science sufficiently epistemically well-situated to be up to the task? This question is difficult to answer in the abstract. Upon what scale does one measure the epistemic success of a science? To what does one compare it? Suffice to say that there is no standard metric for the epistemic purchase of science, no means of independent verification, no reaching past the limits of the material and conceptual instruments we have at our disposal and the inherent constraints of our cognitive apparatus. On this point, and few others, contemporary philosophy of science is in virtually unanimous agreement. If science is to have any epistemic purchase, we have to trust scientists to have some internal sense of the success of their own methods. When scientists come together and raise the alarm bells against some research programme run amok, we heed them (as we should). If scientists report to the world that they are facing a replication crisis and that more than half of an entire field’s results may be utterly uninformative, they are likely correct. There is something Popperian in this. Science is never in a position to know when it is right, but is often right when it judges itself to have been in error. Another thing that we can measure our scientific practices against is the historical record. Science certainly seems to do more for us today than it did in centuries past. Without buying wholesale into progressivist narratives, we can at least say a prayer for the exchange of phlogiston for the kinetic theory of heat, and leeches for penicillin. It seems hardly worth debating that today’s sciences are better epistemically situated on several important metrics. To the sceptic we offer a leech. We can, however, observe that the infrastructure within which science is conducted and its epistemic footing are largely better off today than in previous epochs, while still noting that certain aspects of our present-day scientific procedure are lacking relative to earlier iterations.

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Where scientific enterprises were once carried out under the commission of kings or by the privileged few in their infinite spare time, science today lends the appearance of being an endeavour by the masses and for the masses. We have set about levelling the playing fields of the sciences, made them accessible, undertaken their democratisation. These developments constitute successes; the wresting of science from the hands of society’s elites places science on epistemically firmer ground. For objectivity comes about in the aggregate of countless measurements, at the confluence of diverse perspectives. The infrastructure within which science is conducted, the economic and sociological fabric of science, its modes of dissemination, and its perceived duty to the public all appear to have improved into modernity. However, not all of the shifts of recent history have constituted improvements to science, in either its truth-seeking capacity or its social responsibility. I allude to the industrialisation of science. To the corporatisation, the commodification, and the militarisation of science. To the disciplinary balkanisation of science. To the broad-sweeping prohibition against “theorising” – and by this it is meant any and all forms of sustained critical thought about or questioning of the scientific work.

We have created a science of bureaucrats, of meaningless repetitive tasks, of obsession with a quantitative metrics of productivity that no longer bear any relation to desirable output.

The infrastructure of our science echoes the economic systems and the governmental and societal structures we inhabit. We have created a science of bureaucrats, of meaningless repetitive tasks, of obsession with a quantitative metrics of productivity that no longer bear any relation to desirable output. Mainstream science echoes corporate or governmental structure, a system so radically hierarchicalised that every level is blind to the machinations of every other and no one is in possession of a complete understanding of what is being done. And this lack of understanding only works to keep the paper mill churning. No one is permitted to inquire too deeply into the purpose or efficacy of the tasks set before them or their place in the scope of the system at large. The scientist once constructed bespoke tools for the task at hand. Conceiving of a problem, a question to pose to nature, the scientist conceived ways to solve it, inventing along their way instruments of measure, scientific concepts, and tools of formal analysis. These tools are now literally and figuratively mass-produced. When we invent tools, be they material technologies or conceptual tools, and set about their mass-production, we enable ever more people to go ever further than previous generations have.


Certainly it is a net benefit that millions of individuals possess microscopes where only a few brief centuries ago there were five. When we invent a tool for the empirical task at hand, though, it is in general the case that it is an appropriate instrument for the task, that we understand how it works, and that we are fluent in its appropriate usage. Contrast this model to the way in which p-values are utilised across broad swathes of the psychological and biological sciences, along with areas of medicine and engineering. A p-value reflects a confidence level that a given empirically-observed result is of “statistical significance” – that is, sufficiently unlikely to have been observed by chance alone. There are manifest concerns with the use of p-values in nearly any experimental context in which it is put to use. A one-size-fits-all generic p-value is utilised reflexively across entire scientific fields today. The story goes that someone quite a long time ago invented a procedure that was of questionable value even within the limited range of functions it was designed to fulfil. Now it is repeated everywhere without consideration of its appropriateness or ability to justify its use. The so-called replication crisis has come about, in large part, because generations of behavioural scientists have been instructed in assembly-line procedures rather than modes of questioning and critical analysis. A scientist’s job once required frequently stepping back from their work to consider its implications and evaluate its success. Theorising, methodology, critique, and ethical considerations once all fell on the shoulders of scientists. These days, however, there are no longer jobs in the sciences for scholars so sceptical or critical-minded or operating at this level of abstraction.

Philosophers are selected for and trained in the kind of abstract critical reasoning that is problematically absent from the sciences today, and, more importantly, they are encouraged to utilise it.

These exclusionary developments within the sciences have given birth to new pursuits for philosophers. Where once philosophies birthed sciences, now sciences birth philosophies. The more formal and abstract work of evolutionary biology has been sloughed off to the new discipline of philosophy of biology; theoretical physicists no longer find a home in physics departments but in philosophy departments; philosophy of psychology, cognitive science, neuroscience, mind, and action have supplanted the theoreticians amongst the brain and behavioural sciences. Philosophers are selected for and trained in the kind of abstract critical reasoning that is problematically absent from the sciences today, and, more importantly, they are encouraged to utilise it. Every laboratory needs to have someone who understands broadly and intimately what is going on and who is empowered to ask questions, to stand back from the laboratory bench or the telescope or the command line and ask: What are we really trying to determine here, and to what end? Will it be of benefit or detriment to mankind? Are the assumptions and idealisations that we have made along the way warranted? Where do our driving theories, models, and representations of the phenomena under study stand in relation to rival or complementary conceptualisations? How are we best to interpret and contextualise the results of our modelling or experimental work? Are our instruments of measurement and analysis, our conceptual repertoire, up to the task at hand?

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Every laboratory needs a philosopher. The philosopher’s role, however, is not only to philosophise on behalf of scientists, but to re-educate scientists in how to philosophise for themselves. Being able to effectively serve this function requires of the philosopher an intimate knowledge of the scientific research programme at hand, the history of the field, as well as rival frameworks. It requires mathematical fluency on a par with, or greater than, that of the scientist. It requires not always deferring to the scientist in their assessment of the legitimacy of their methods or their reconstruction of their justification.


Perhaps you say that science has seldom, historically, been a force for liberatory and egalitarian ideals. This does not alter the fact that it can, and, indeed, must now serve this role. And philosophy must be its aid.



Mel Andrews is a visiting research associate at Carnegie Mellon University (machine learning) and an instructor and doctoral candidate at the University of Cincinnati (philosophy of science). Their research centres on the philosophy of science of mathematical modelling and machine learning. Website: mel-andrews.com

Twitter: @bayesianboy

 

From The Philosopher, vol. 111, no. 1 ("Where is Philosophy Going?").

If you enjoyed reading this, please consider becoming a patron or making a small donation.

We are unfunded and your support is greatly appreciated.

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