Failures of Automation: Misunderstanding the Hierarchy of Evidence
Folks who are into methodological blood sports would be hard pressed to find a more engaging fight than that between the Cochraneists and the Clinicians over oseltamivir (Tamiflu). You may or may not remember the rush to stockpile this anti-viral drug after the bird flu pandemic of 2005.
The stockpiling came on the back of problematic marketing spin from the drug’s manufacturer, Roche, (which included burying negative studies) that overplayed Tamiflu’s effectiveness and underplayed side effects.
This pharmaceutical skullduggery was challenged when a group of researchers at the Cochrane Collaboration conducted a systematic review of all the available studies into the drug and determined that it was basically useless.
There was some high dudgeon in response to the methodology of this systematic review from infectious disease clinicians. Some of them derisively refer to the epistemic flaws of the Cochraneists as, 'Methodolatry'. This is an allusion to the Evidence Based Medicine movement's supposed elevation of the Hierarchy of Evidence to a divine status that elides the complexities of clinical experience and human frailties in using the Hierarchy.
To cut a long story short, the aggregate data from the Cochrane systematic review could not definitively demonstrate sufficient Tamiflu’s benefits outweighing the potential harms….
Meanwhile, the eminent gentlemen over at Science Based Medicine were adamant that on the ground, anti-flu drugs are a crucial weapon in the arsenal against a deadly disease and possibly a pandemic.
So... whom to believe?
The collators of data because data doesn't lie, right?
Or the skeptical clinicians, because skeptical clinicians are inured against various biases, right?
The source of my facetiousness should be obvious.
Clearly clinical expertise is useless without knowledge gleaned from randomised controlled trials. Meanwhile, randomised controlled trials can throw up perverse results for all sorts of reasons, and are in and of themselves useless without interpretation by people who have clinical expertise.
Indeed, this fight is a classic example of a failure to understand the epistemic mechanics of automation.
Automation may *feel like* the removal of human inputs from a process - whether that process involves manufacturing, computing, or analysing evidence - but the truth about automation is that it is always, at its source a human product. And as we well know, humans are fallible. So the processes by which automation is designed may themselves be flawed.
The clearest example of this is racist algorithms. An algorithm - an automated set of rules for the purposes of problem solving - may feel like an objective truth, but its parameters are designed by humans. And if those parameters are racist, then the way in which an algorithm will go about sorting a problem will be racist even if fully automated, with human involvement removed at the end point. To get really basic, if an input is racist, then the output will be racist too.
Automation is beloved of any individual or industry valuing standardisation and efficiency divorced from the vicissitudes of human folly, yet human folly can be inherent in any automated process.
The Hierarchy of Evidence is exactly this: it’s a tool created to attempt to counter the inherent biases in human - specifically, clinical - judgement, and it constructs a framework for evaluating evidentiary heft that is supposedly divorced from judgement calls that might be influenced by human bias.
Tying this back to Tamiflu, as much as the Evidence Based Medicine brigade like to think that their mechanisms are sufficient counter to human bias, the reality is, sets of decisions around a systematic review’s methodology still need to be made by people - what data to include versus what data to exclude, being chief among them.
The biggest failures of automation occur when automation itself is elevated as an ideal and framed as beyond human frailties - like bias, for example.
And the clinicians over at Science Based Medicine believe a fair bit of bias was involved in the unusually stringent inclusion criteria for the Tamiflu systematic review.
More broadly, a pretty obvious objection to the elevation of the Hierarchy of Evidence beyond clinician experience, is known in the trade as GIGO - Garbage in, Garbage Out. A systematic review can only be as good as the evidence it is reviewing.
If a case report is on the lowest rung of the hierarchy, and a systematic review is on the highest, the Hierarchy of Evidence by itself has no procedural mechanism for valuing a lack of conflict of interest behind a case report, and devaluing a meta-analysis comprising studies which may be riddled with conflicts of interest.
And in the specific case of Tamiflu, there is an accusation of BIBO - bias in, bias out!
But there is a further, deeper objection to over-reliance on this hierarchy.
The Hierarchy exists in an imaginary epistemic ecosystem in which all information - good and bad - is accessible. We know, however, that this is not the case - that countless trials returning 'uninteresting' results die a quiet death in the bottom of a researcher's desk drawer.
This is not to say that clinicians are always right either. Medical history is replete with doctors' fidelity to butchery and other harmful practices that were only stopped when well designed trials demonstrated the harms outweighing the benefits.
As always, in any issue dealing with complexity - and there is no more complex system known to us than the human body and brain - there is still no substitute for the emergent phenomenon that is human intelligence.
Human intelligence is needed to create the automation process and human intelligence is needed to parse automation's results.
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