{"id":15680,"date":"2025-07-31T19:07:12","date_gmt":"2025-07-31T09:07:12","guid":{"rendered":"https:\/\/rationalemagazine.com\/?p=15680"},"modified":"2025-07-31T19:07:12","modified_gmt":"2025-07-31T09:07:12","slug":"when-ai-audits-the-scholarly-record-what-will-happen-to-public-trust-in-science","status":"publish","type":"post","link":"https:\/\/rationalemagazine.com\/index.php\/2025\/07\/31\/when-ai-audits-the-scholarly-record-what-will-happen-to-public-trust-in-science\/","title":{"rendered":"When AI audits the scholarly record, what will happen to public trust in science?"},"content":{"rendered":"

Self-correction is fundamental to science. One of its most important forms is\u00a0peer review<\/a>, when anonymous experts scrutinise research before it is published. This helps safeguard the accuracy of the written record.<\/p>\n

Yet problems slip through. A range of grassroots and institutional initiatives work to identify problematic papers, strengthen the peer-review process, and clean up the scientific record through retractions or journal closures. But these efforts are imperfect and resource intensive.<\/p>\n

Soon, artificial intelligence (AI) will be able to supercharge these efforts. What might that mean for public trust in science?<\/p>\n

In recent decades, the digital age and disciplinary diversification have sparked an explosion in the number of scientific papers being published, the number of journals in existence, and the influence of\u00a0for-profit publishing<\/a>.<\/p>\n

This has opened the doors for exploitation. Opportunistic ‘paper mills’<\/a>\u00a0sell quick publication with minimal review to academics desperate for credentials, while publishers generate substantial profits through\u00a0huge article-processing fees<\/a>.<\/p>\n

Corporations have also seized the opportunity to fund low-quality research and ghostwrite papers intended to distort the weight of evidence, influence public policy and alter public opinion in favour of their products.<\/p>\n

These ongoing challenges highlight the insufficiency of peer review as the primary guardian of scientific reliability. In response, efforts have sprung up to bolster the integrity of the scientific enterprise.<\/p>\n

Retraction Watch<\/a>\u00a0actively tracks withdrawn papers and other academic misconduct. Academic sleuths and initiatives such as\u00a0Data Collada<\/a>\u00a0identify manipulated data and figures.<\/p>\n

Investigative journalists expose corporate influence. A new field of meta-science (science of science) attempts to measure the processes of science and to uncover biases and flaws.<\/p>\n

Not all bad science has a major impact, but some certainly does. It doesn\u2019t just stay within academia; it often seeps into public understanding and policy.<\/p>\n

In a recent\u00a0investigation<\/a>, we examined a widely-cited safety review of the herbicide glyphosate, which appeared to be independent and comprehensive. In reality,\u00a0documents produced during legal proceedings against Monsanto<\/a>\u00a0revealed that the paper had been ghostwritten<\/a>\u00a0by Monsanto employees and published in a journal with\u00a0ties to the tobacco industry<\/a>.<\/p>\n

Even after this was exposed, the paper continued to shape citations, policy documents and Wikipedia pages worldwide.<\/p>\n

When problems like this are uncovered, they can make their way into public conversations, where\u00a0they are not necessarily perceived as triumphant acts of self-correction<\/a>. Rather, they may be taken as proof that something is rotten in the state of science. This \u201cscience is broken<\/a>\u201d narrative\u00a0undermines public trust<\/a>.<\/p>\n

Until recently, technological assistance in self-correction was mostly limited to plagiarism detectors. But things are changing. Machine-learning services such as\u00a0ImageTwin<\/a>\u00a0and\u00a0Proofig<\/a>\u00a0now scan millions of figures for signs of duplication, manipulation and AI generation.<\/p>\n

Natural language processing tools flag \u201ctortured phrases<\/a>\u201d \u2013 the telltale word salads of paper mills. Bibliometric dashboards such as one by\u00a0Semantic Scholar<\/a>\u00a0trace whether papers are cited in support or contradiction.<\/p>\n

AI \u2013 especially agentic, reasoning-capable models increasingly\u00a0proficient in mathematics<\/a>\u00a0and logic \u2013 will soon uncover more subtle flaws.<\/p>\n

For example, the\u00a0Black Spatula Project<\/a>\u00a0explores the ability of the latest AI models to check published mathematical proofs at scale, automatically identifying algebraic inconsistencies that eluded human reviewers. Our own work mentioned above also substantially relies on large language models to process large volumes of text.<\/p>\n

Given full-text access and sufficient computing power, these systems could soon enable a global audit of the scholarly record. A comprehensive audit will likely find some outright fraud and a much larger mass of routine, journeyman work with garden-variety errors.<\/p>\n

These ongoing challenges highlight the insufficiency of peer review as the primary guardian of scientific reliability. In response, efforts have sprung up to bolster the integrity of the scientific enterprise.<\/strong><\/p><\/blockquote>\n

We do not know yet how prevalent fraud is, but what we do know is that an awful lot of scientific work is inconsequential. Scientists know this; it\u2019s much discussed that a good deal of\u00a0published work is never<\/a>\u00a0or\u00a0very rarely cited<\/a>.<\/p>\n

To outsiders, this revelation may be as jarring as uncovering fraud, because it collides with the image of dramatic, heroic scientific discovery that populates university press releases and trade press treatments.<\/p>\n

What might give this audit added weight is its AI author, which may be seen as (and may in fact be) impartial and competent, and therefore reliable.<\/p>\n

As a result, these findings will be vulnerable to exploitation in disinformation campaigns, particularly since\u00a0AI is already being used to that end<\/a>.<\/p>\n

Safeguarding public trust requires redefining the scientist\u2019s role in more transparent, realistic terms. Much of today\u2019s research is incremental, career\u2011sustaining work rooted in education, mentorship and public engagement.<\/p>\n

If we are to be honest with ourselves and with the public, we must abandon the incentives that pressure universities and scientific publishers, as well as scientists themselves, to\u00a0exaggerate the significance of their work<\/a>. Truly ground-breaking work is rare. But that does not render the rest of scientific work useless.<\/p>\n

A more\u00a0humble<\/a>\u00a0and\u00a0honest portrayal of the scientist<\/a>\u00a0as a contributor to a collective, evolving understanding will be more robust to AI-driven scrutiny than the myth of science as a parade of individual breakthroughs.<\/p>\n

A sweeping, cross-disciplinary audit is on the horizon. It could come from a government watchdog, a think tank, an anti-science group or a corporation seeking to undermine public trust in science.<\/p>\n

Scientists can already anticipate what it will reveal. If the scientific community prepares for the findings \u2013 or better still, takes the lead \u2013 the audit could inspire a disciplined renewal. But if we delay, the cracks it uncovers may be misinterpreted as fractures in the scientific enterprise itself.<\/p>\n

Science has never derived its strength from infallibility. Its credibility lies in the willingness to correct and repair. We must now demonstrate that willingness publicly, before trust is broken.<\/p>\n

 <\/p>\n

This article was originally published on<\/em>\u00a0The Conversation<\/a>. It was co-authored by Alexander Kaurov, PhD Candidate in Science and Society, Te Herenga Waka, Victoria University of Wellington.<\/strong><\/p>\n

Photo by Teslariu Mihai<\/a> on Unsplash.<\/strong><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Self-correction is fundamental to science. One of its most important forms is\u00a0peer review, when anonymous experts scrutinise research before it<\/p>\n","protected":false},"author":832,"featured_media":15681,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[65],"tags":[371],"coauthors":[791],"class_list":["post-15680","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-science-health","tag-science"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/rationalemagazine.com\/index.php\/wp-json\/wp\/v2\/posts\/15680","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rationalemagazine.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rationalemagazine.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rationalemagazine.com\/index.php\/wp-json\/wp\/v2\/users\/832"}],"replies":[{"embeddable":true,"href":"https:\/\/rationalemagazine.com\/index.php\/wp-json\/wp\/v2\/comments?post=15680"}],"version-history":[{"count":2,"href":"https:\/\/rationalemagazine.com\/index.php\/wp-json\/wp\/v2\/posts\/15680\/revisions"}],"predecessor-version":[{"id":15683,"href":"https:\/\/rationalemagazine.com\/index.php\/wp-json\/wp\/v2\/posts\/15680\/revisions\/15683"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rationalemagazine.com\/index.php\/wp-json\/wp\/v2\/media\/15681"}],"wp:attachment":[{"href":"https:\/\/rationalemagazine.com\/index.php\/wp-json\/wp\/v2\/media?parent=15680"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rationalemagazine.com\/index.php\/wp-json\/wp\/v2\/categories?post=15680"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rationalemagazine.com\/index.php\/wp-json\/wp\/v2\/tags?post=15680"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/rationalemagazine.com\/index.php\/wp-json\/wp\/v2\/coauthors?post=15680"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}