海角大神

If a revised rule proposed last week is finalized, the Environmental Protection Agency could soon change how it uses science.

鈥淭ransparency鈥� lies at the heart of the controversial proposal. Initially suggested in 2018, the revised version of 鈥溾�� would mean that, when drafting environmental and public health regulations, the EPA would give preference to research studies for which the underlying datasets and models are publicly available. In the previous draft, all aspects of a scientific study had to be publicly available for research to even be considered.聽A 30-day public comment period opened March 3, and EPA administrators aim to have the rule finalized by May.

Since its conception, the proposed rule has drawn sharp criticism. While supporters assert that it would be a safeguard to ensure trustworthy research, opponents see it as a Trump administration attack on science that co-opts the positive connotations of 鈥渢ransparency鈥� for political aims.聽

Sharing raw data makes sense 鈥渋n the abstract,鈥� says Wendy Wagner, a professor in the University of Texas School of Law, who studies use of science by environmental policymakers. But, she says, there are 鈥渁 lot of steps between that kind of idyllic in the abstract and mandating it as a prerequisite to considering the scientific information.鈥�

Indeed, transparency does hold scientific value. At the same time, with policy around hot-button issues from coronavirus to climate change being guided by scientific research, it鈥檚 vital that both policymakers and the public trust the findings. Transparency might play a role in earning that trust.

A matter of trust

Public trust in science is indeed higher than some headlines might lead you to believe. According to a Pew Research Center conducted in 2019, 86% of Americans surveyed said they had confidence in scientists to act in the public interest 鈥撀燼 number greater than that for most other institutions, and on a par with the military.聽

Transparency does seem to play a role, as the Pew survey also revealed that 57% of surveyed Americans said that open public access to data and independent committee reviews of research would boost their confidence and sense of trust.聽

That鈥檚 not to say that they are actually interested in parsing through that data. Rather, says Cary Funk, director of science and society research at Pew Research Center, it鈥檚 likely an underlying assumption that 鈥渨hen you鈥檙e open and transparent, you don鈥檛 have anything to hide.鈥�

As an abstract concept, transparency gets at one of the main tenets of science: open communication among researchers in a way that allows them essentially to check each other鈥檚 work. But the specifics 鈥� especially when sharing datasets with the public 鈥撀爂et a bit thornier.

For one thing, not all raw data can be released to the public easily. Some data is trade-secret protected. There鈥檚 also an issue of data from study participants, often medical data, that might have too much personally identifiable information and thus requires privacy.聽

That is an especially challenging aspect for many of the public health studies underpinning landmark EPA regulations, such as air quality standards. Critics were quick to point this out during the initial comment period for the 鈥渢ransparency鈥� rule in 2018, and the revision allows such studies to be included, although weighted with less consideration than those for which the data is freely available.

Starting a 鈥渃onversation鈥�

Transparency also doesn鈥檛 just have to mean dumping it all out there for anyone to parse through, says the University of Texas鈥� Professor Wagner. In some ways, it might be less helpful for nonscientists to be able to interpret that data.聽

鈥淵ou do need to know what you鈥檙e doing with your data,鈥� says Dominique Brossard, who co-directs the Science, Media and the Public research group at the University of Wisconsin-Madison. 鈥淵ou need to be trained. You need to be able to actually understand statistics.鈥�

鈥淏ecause at the end of the day,鈥� she says, 鈥測ou can make the data tell a lot of things, you know, in a way that it can be massaged to reach a certain conclusion.鈥�

Furthermore, Professor Wagner says, with some of this data, especially with big data and models, you need computer science expertise, resources, and time.聽

鈥淚t becomes even more of a pay-to-play system as a result of that approach to transparency,鈥� she says. 鈥淎nd, when you have all those advantages, you also can do a lot of mischief with datasets.鈥�

So instead, both Professor Wagner and Professor Brossard suggest a different approach to transparency: a conversation. Rather than saying, here鈥檚 the data for you to explore on your own, they suggest that more trust will come from explaining the process of the research to the public and stakeholders in a clear, honest way. Walking through the research process, the peer-review process, and explaining how independent reviewers were selected, as well as the problems and uncertainties in the results, may build more trust and confidence.

鈥淚n the study of science, one of the big concerns is trust in expertise. And I don鈥檛 think the way you get the trust is to throw downloadable models and datasets at people,鈥� Professor Wagner says. 鈥淭rust is a process.鈥�