Back in the day, when I was writing papers, it was a grueling, demanding process. I’d spend hours in the darkroom, trying to develop perfect exposures of all the images, and that was after weeks to months with my eyes locked to the microscope. Even worse was the writing; in those days we’d go through the paper word by word, checking every line for typos. We knew that once we submitted it, the reviewers would shred it and the gimlet-eyed editors would scrutinize it carefully before permitting our work to be typeset in the precious pages of their holy journal. It was serious work.

Nowadays, you just tell the computer to write the paper for you and say, fuck it.

That’s the message I get from this paper, Bridging the gap: explainable ai for autism diagnosis and parental support with TabPFNMix and SHAP, which was published in one of the Nature Publishing Group’s lesser journals, Nature Scientific Reports, an open-access outlet. Now I can’t follow the technical details because it’s so far outside my field, but it does declare right there in the title that they have an AI tool for autism diagnosis that is explainable, which implies to me that it generates diagnoses that would be comprehensible to families, right? This claim is also emphasized in the abstract, before it descends into jargon.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that affects a growing number of individuals worldwide. Despite extensive research, the underlying causes of ASD remain largely unknown, with genetic predisposition, parental history, and environmental influences identified as potential risk factors. Diagnosing ASD remains challenging due to its highly variable presentation and overlap with other neurodevelopmental disorders. Early and accurate diagnosis is crucial for timely intervention, which can significantly improve developmental outcomes and parental support. This work presents a novel artificial intelligence (AI) and explainable AI (XAI)-based framework to enhance ASD diagnosis and provide interpretable insights for medical professionals and caregivers…

Great. That sounds like a worthy goal. I’d support that.

Deep in the paper, it explains that…

Keyes et al. critically examined the ethical implications of AI in autism diagnosis, emphasizing the dangers of dehumanizing narratives and the lack of attention to discursive harms in conventional AI ethics. They argued that AI systems must be transparent and interpretable to avoid perpetuating harmful stereotypes and to build trust among clinicians and caregivers.

So why is this Figure 1, the overall summary of the paper?

Overall working of the framework presented as an infographic.

You’d think someone, somewhere in the review pipeline, would have noticed that “runctitional,” “frymbiai,” and “Fexcectorn” aren’t even English words, that the charts are meaningless and unlabeled, that there is a multicolored brain floating at the top left, and that “AUTISM” is illustrated with a bicycle, for some reason? I can’t imagine handing this “explanatory” illustration to a caregiver and seeing the light of comprehension lighting up their eyes, which don’t exist in the faceless figure in the diagram, and perhaps she is more concerned with how her lower limbs have punched through the examining table.

This paper was presumably reviewed. The journal does have instructions for reviewers. There are rules about how reviewers can use AI tools.

Peer reviewers play a vital role in scientific publishing. Their expert evaluations and recommendations guide editors in their decisions and ensure that published research is valid, rigorous, and credible. Editors select peer reviewers primarily because of their in-depth knowledge of the subject matter or methods of the work they are asked to evaluate. This expertise is invaluable and irreplaceable. Peer reviewers are accountable for the accuracy and views expressed in their reports, and the peer review process operates on a principle of mutual trust between authors, reviewers and editors. Despite rapid progress, generative AI tools have considerable limitations: they can lack up-to-date knowledge and may produce nonsensical, biased or false information. Manuscripts may also include sensitive or proprietary information that should not be shared outside the peer review process. For these reasons we ask that, while Springer Nature explores providing our peer reviewers with access to safe AI tools, peer reviewers do not upload manuscripts into generative AI tools.

If any part of the evaluation of the claims made in the manuscript was in any way supported by an AI tool, we ask peer reviewers to declare the use of such tools transparently in the peer review report.

Clearly, those rules don’t apply to authors.

Also, unstated is the overall principle to be used by reviewers: just say, “aww, fuck it” and rubber-stamp your approval.