Found a great paper (that I should already have known about, of course): Keil, A.P., Edwards, J.K. You are smarter than you think: (super) machine learning in context. Eur J Epidemiol33, 437–440 (2018). https://doi.org/10.1007/s10654-018-0405-9
Here are some brief thoughts on this really enjoyable article, which I would recommend to philosophers of science, medicine, and epidemiology looking for interesting leads on the interaction between epidemiology and ML – as well as to the target audience, epidemiologists.
Here are some very brief, unfiltered thoughts.
Keil and Edwards discuss an approach, “super learning”, that assembles the results of a bundle of different methods and returns the best (as defined by a user-specified, but objective, measure). In an example, they show how adding a method to that bundle can result in a worse result. Philosophically, this resonates with familiar facts about non-deductive reasoning, namely that as you add information, you can “break” and inference, whereas adding information to the premise set of a deductive argument does not invalidate the inference provided the additional information is consistent with what’s already there. Not sure what to make of the resonance yet, but it reminds me of counterexamples to deductive-nomological explanation – which is like ML in being formal.
They point out that errors like this are reasonably easy for humans to spot, and conclude: “We should be cautious, however, that the billions of years of evolution and experience leading up to current levels of human intelligence is not ignored in the context of advances to computing in the last 30 years.” I suppose my question would be whether all such errors are easy for humans to spot, or whether only the ones we spot are easy to spot. Here, there is a connection with the general intellectual milieu around Kahneman and Tversky’s work on biases. We are indeed honed by evolution, but this leads us to error outside of our specific domain, and statistical reasoning is one well-documented error zone for intuitive reasoning. I’m definitely not disagreeing with their scepticism about formal approaches, but I’m urging some even-handed scepticism about our intuitions. Where the machine and the human disagree, it seems to me a toss-up who, if either, is right.
The assimilation of causal inference to a prediction problem is very useful and one I’ve also explored. It deserves wider appreciation among just about everyone. What would be nice is to see more discussion about prediction under intervention, which, according to some, are categorically different from other kinds. Will machine learning prove capable of making predictions about what will happen under interventions? If so, will this yield causal knowledge as a matter of definition, or could the resulting predictions be generated in a way that is epistemically opaque? Interventionism in philosophy, causal inference in epidemiology, and the “new science of cause and effect” might just see their ideas put to empirical test, if epidemiology picks up ML approaches in coming years. An intervention-supporting predictive algorithm that does not admit of a ready causal interpretation would force a number of books to be rewritten. Of course, according to those books, it should be impossible; but the potency of a priori reasoning about causation is, to say the least, disputed.
Excited to be giving these thoughts their first outing, in what I hope will be my considered philosophical paper on the thoughts I’ve been having during 2020. The event is open and you can join here: https://bit.ly/3lnxPci
Latest from our ongoing research project at the Institute for the Future of Knowledge with the Center for Global Development. We are looking at indirect health effects of lockdown, meaning the effects on things other than COVID-19. But in the process, we couldn’t help but notice the direct effects too – or rather, their absence…
The Sowerby Philosophy of Medicine Project at King’s College London invite attendees to a one-day online conference exploring theory and practice of teaching philosophy as part of the medical curriculum. This event is free, open to the public and all are welcome! Registered attendees will receive an access link shortly prior to the event’s scheduled start time. Please register by 8:30 AM on the 15th of September.
10:00 – 11:15
Juliette Ferry-Danini (Paris) – “Considerations from the French experience: Why teaching philosophy should not mean humanising doctors.”
11:15 – 11:30
11:30 – 12:45
Alexander Broadbent (Johannesburg) – “‘Either philosophy can make the difference between life and death, or it has no place in medical education.’ Discuss.”
12:45 – 13:45
13:45 – 15:00
Raffaela Campaner (Bologna) – “What philosophical approaches in medical education? Theoretical and empirical issues.”
15:00 – 15:15
15:15 – 16:30
Jonathan Fuller (Pittsburgh) – “Philosophy of medicine as a core discipline for learning the theory of medicine.”
16:30 – 17:00
Concluding remarks: Alexander Bird (King’s/Cambridge)
Lockdown was never right for Africa. Half the population is 19 or under, highlighted in this report; and known prior to COVID, of course. On the cost side of the balance sheet, other risks are massively dominant over that posed by COVID-19. Living conditions mean that suppression was never achievable in any case. Costs of lockdown were obviously going to be horrific, because recession means starvation in contexts of poverty. What a mess for those countries that did lock down. And those that didn’t seem to be doing fine, COVID-wise: e.g. Malawi, whose supreme court prevented the government from locking down.
Aside from all that, it’s clear that there’s a great deal of uncertainty about why some places get hit so much harder than others by COVID-19. Sweden is held up as being hit hard, and blamed; but that ignores the fact that many other European countries that did lock down were hit a lot harder. Why? I favour the following theory: we don’t know.
Epistemic humility in all matters relating to medicine is always appropriate.