Big Science Needs to Repent
In a stunning rebuke, 22 scientists write in Nature that science needs humility and transparency.
Once in awhile, Nature prints a Commentary that stands high above its usual fare of political correctness, Darwinism and scientism. One such article appeared on June 24 under the headline, “Five ways to ensure that models serve society: a manifesto.” Almost two dozen authors joined in this manifesto that calls for sweeping changes in the way scientific modeling is done.
What’s the occasion? “Pandemic politics highlight how predictions need to be transparent and humble to invite insight, not blame.” Scientists around the world, up to and including the World Health Organization, have gotten a black eye in recent months over contradictory messages, failed predictions, and political motivations behind some findings. It’s time for repentance and change.
Among the authors is Daniel Sarewitz, one of the more fair-minded and thoughtful commentators at Nature. He has rebuked scientists before, and raised eyebrows about the political bias and poor practices in science. See some of his earlier comments we’ve reported on:
- Myth of Objective Science Busted (15 July 2017). Debunks the myth of science as a “miracle machine.”
- Darwinism Still Corrupts Culture (22 Jan 2017, commentary). Sarewitz attacks “the naive myth of science as a disinterested producer of neutral truths.”
- Big Science Has Corrupted Its Mission (17 Nov 2016). Sarewitz, shocked by Trump’s election, shows his own political bias, but he takes a wait-and-see attitude.
- The Democratic Party Does Not Own Science (7 Dec 2014). “Science should keep out of partisan politics.”
- Is Science a Special Interest Group for One Party? (2 Jan 2013). “Scientists in the United States are often perceived as a Democratic interest group. For science’s sake this has to change, argues Daniel Sarewitz.”
- Climate Change as a Philosophy of Science Case Study (30 May 2012). “Beware the creeping cracks of bias.”
It’s amazing Sarewitz hasn’t been fired yet, but in this month’s Commentary, he is accompanied by 21 others who signed their name to the manifesto. The subject is scientific models: recent models, having given the public reasons to lose trust in science, need reform.
Here we present a manifesto for best practices for responsible mathematical modelling. Many groups before us have described the best ways to apply modelling insights to policies, including for diseases4 (see also Supplementary information). We distil five simple principles to help society demand the quality it needs from modelling.
The five principles are, indeed, simple. Why didn’t scientists think of these before? Isn’t this what Mom and Dad should teach every child? This is like back-to-basics reform school for Big Science.
Mind the assumptions. “Assess uncertainty and sensitivity.” Is there reliable information for the input values?
Mind the hubris. “Complexity can be the enemy of relevance.” Don’t bluff with complex answers.
Mind the framing. “Results from models will at least partly reflect the interests, disciplinary orientations and biases of the developers.” The stakeholders must be involved.
Mind the consequences. “Spurious precision adds to a false sense of certainty.” “Opacity about uncertainty damages trust.”
Mind the unknowns. “Acknowledge ignorance.” When asked by reporters or politicians, be willing to say, “There is no number-answer to your question.”
The authors remind scientists that models are not the same as answers or explanations. They are not robotic answer machines.
Mathematical models are a great way to explore questions. They are also a dangerous way to assert answers. Asking models for certainty or consensus is more a sign of the difficulties in making controversial decisions than it is a solution, and can invite ritualistic use of quantification.
The next closing admonition should call for scientists to fall on their knees. How many times have we said that science requires integrity? The scientific method is not a machine that cranks out knowledge.
Models’ assumptions and limitations must be appraised openly and honestly. Process and ethics matter as much as intellectual prowess. It follows, in our view, that good modelling cannot be done by modellers alone. It is a social activity.
The final word is to be honest about bias. Science requires good character. It requires transparency. It requires humility. It requires responsibility.
We are calling not for an end to quantification, nor for apolitical models, but for full and frank disclosure. Following these five points will help to preserve mathematical modelling as a valuable tool. Each contributes to the overarching goal of billboarding the strengths and limits of model outputs. Ignore the five, and model predictions become Trojan horses for unstated interests and values. Model responsibly.
Suitably rebuked by this sermon, the listening scientists will file out of the sanctuary with bowed heads, and exit the door toward the giant sucking machine that will remove all memory of the admonitions.
Exercise: Take some of the leading political controversies of the day — COVID-19 policy, climate change, transgenderism, Darwinian evolution — and run them through the five recommendations. What survives?
Review: See our June 13 entry, “Big Science Capitulates to Socialist Revolution” and see how well the scientists scored on humility, integrity, and transparency in the cited articles.