Scientific norms shape the behavior of researchers working for the greater good
The first person to write down these attitudes and behaviors was Robert Merton, in 1942. The founder of the sociology of science laid out what he called the 'ethos of science,' a set of 'values and norms which is held to be binding on the man of science.' (Yes, it's sexist wording. Yes, it was the 1940s.) These now are referred to as scientific norms.
The point of these norms is that scientists should behave in ways that improve the collective advancement of knowledge. If you're a cynic, you might be rolling your eyes at such a Pollyannaish ideal. But corny expectations keep the world functioning. Think: Be kind, clean up your mess, return the shopping cart to the cart corral.
I'm a physical geographer who realized long ago that students are taught biology in biology classes and chemistry in chemistry classes, but rarely are they taught about the overarching concepts of science itself. So I wrote a book called 'The Scientific Endeavor,' laying out what scientists and other educated people should know about science itself.
Scientists in training are expected to learn the big picture of science after years of observing their mentors, but that doesn't always happen. And understanding what drives scientists can help nonscientists better understand research findings. These scientific norms are a big part of the scientific endeavor. Here are Merton's original four, along with a couple I think are worth adding to the list:
Scientific knowledge is for everyone – it's universal – and not the domain of an individual or group. In other words, a scientific claim must be judged on its merits, not the person making it. Characteristics like a scientist's nationality, gender or favorite sports team should not affect how their work is judged.
Also, the past record of a scientist shouldn't influence how you judge whatever claim they're currently making. For instance, Nobel Prize-winning chemist Linus Pauling was not able to convince most scientists that large doses of vitamin C are medically beneficial; his evidence didn't sufficiently support his claim.
In practice, it's hard to judge contradictory claims fairly when they come from a 'big name' in the field versus an unknown researcher without a reputation. It is, however, easy to point out such breaches of universalism when others let scientific fame sway their opinion one way or another about new work.
Communism in science is the idea that scientific knowledge is the property of everyone and must be shared.
Jonas Salk, who led the research that resulted in the polio vaccine, provides a classic example of this scientific norm. He published the work and did not patent the vaccine so that it could be freely produced at low cost.
When scientific research doesn't have direct commercial application, communism is easy to practice. When money is involved, however, things get complicated. Many scientists work for corporations, and they might not publish their findings in order to keep them away from competitors. The same goes for military research and cybersecurity, where publishing findings could help the bad guys.
Disinterestedness refers to the expectation that scientists pursue their work mainly for the advancement of knowledge, not to advance an agenda or get rich. The expectation is that a researcher will share the results of their work, regardless of a finding's implications for their career or economic bottom line.
Research on politically hot topics, like vaccine safety, is where it can be tricky to remain disinterested. Imagine a scientist who is strongly pro-vaccine. If their vaccine research results suggest serious danger to children, the scientist is still obligated to share these findings.
Likewise, if a scientist has invested in a company selling a drug, and the scientist's research shows that the drug is dangerous, they are morally compelled to publish the work even if that would hurt their income.
In addition, when publishing research, scientists are required to disclose any conflicts of interest related to the work. This step informs others that they may want to be more skeptical in evaluating the work, in case self-interest won out over disinterest.
Disinterestedness also applies to journal editors, who are obligated to decide whether to publish research based on the science, not the political or economic implications.
Merton's last norm is organized skepticism. Skepticism does not mean rejecting ideas because you don't like them. To be skeptical in science is to be highly critical and look for weaknesses in a piece of research.
This concept is formalized in the peer review process. When a scientist submits an article to a journal, the editor sends it to two or three scientists familiar with the topic and methods used. They read it carefully and point out any problems they find.
The editor then uses the reviewer reports to decide whether to accept as is, reject outright or request revisions. If the decision is revise, the author then makes each change or tries to convince the editor that the reviewer is wrong.
Peer review is not perfect and doesn't always catch bad research, but in most cases it improves the work, and science benefits. Traditionally, results weren't made public until after peer review, but that practice has weakened in recent years with the rise of preprints, reducing the reliability of information for nonscientists.
I'm adding two norms to Merton's list.
The first is integrity. It's so fundamental to good science that it almost seems unnecessary to mention. But I think it's justified since cheating, stealing and lazy scientists are getting plenty of attention these days.
The second is humility. You may have made a contribution to our understanding of cell division, but don't tell us that you cured cancer. You may be a leader in quantum mechanics research, but that doesn't make you an authority on climate change.
Scientific norms are guidelines for how scientists are expected to behave. A researcher who violates one of these norms won't be carted off to jail or fined an exorbitant fee. But when a norm is not followed, scientists must be prepared to justify their reasons, both to themselves and to others.
This article is republished from The Conversation, a nonprofit, independent news organization bringing you facts and trustworthy analysis to help you make sense of our complex world. It was written by: Jeffrey A. Lee, Texas Tech University
Read more:
Science activism is surging – which marks a culture shift among scientists
Rogue science strikes again: The case of the first gene-edited babies
Intellectual humility is a key ingredient for scientific progress
Jeffrey A. Lee does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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Newsweek
20 hours ago
- Newsweek
MrBeast Makes Astronomer Joke After Gwyneth Paltrow Video
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Vox
2 days ago
- Vox
How can you know if an AI is plotting against you?
The last word you want to hear in a conversation about AI's capabilities is 'scheming.' An AI system that can scheme against us is the stuff of dystopian science fiction. And in the past year, that word has been cropping up more and more often in AI research. Experts have warned that current AI systems are capable of carrying out 'scheming,' 'deception,' 'pretending,' and 'faking alignment' — meaning, they act like they're obeying the goals that humans set for them, when really, they're bent on carrying out their own secret goals. Now, however, a team of researchers is throwing cold water on these scary claims. They argue that the claims are based on flawed evidence, including an overreliance on cherry-picked anecdotes and an overattribution of human-like traits to AI. The team, led by Oxford cognitive neuroscientist Christopher Summerfield, uses a fascinating historical parallel to make their case. The title of their new paper, 'Lessons from a Chimp,' should give you a clue. 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By providing subtle cues to the apes, they also unconsciously prompted them to make the right signs for a given situation. Summerfield and his co-authors worry that something similar may be happening with the researchers who claim AI is scheming. What if they're overinterpreting the results to show 'rogue AI' behaviors because they already strongly believe AI may go rogue? The researchers making claims about scheming chatbots, the paper notes, mostly belong to 'a small set of overlapping authors who are all part of a tight-knit community' in academia and industry — a community that believes machines with superhuman intelligence are coming in the next few years. 'Thus, there is an ever-present risk of researcher bias and 'groupthink' when discussing this issue.' To be clear, the goal of the new paper is not to dismiss the idea that AI could scheme or pose existential risks to humanity. Just the opposite; it's because the authors take these risks seriously that they think experts should be more rigorous and careful about their claims. So, let's take a look at the problems with claims of AI gone rogue. These are examples of AI scheming. Or are they? Any time an AI can be made to scheme in some experiment, researchers (and subsequently, the media) tend to tell the story as if the AI 'wanted' to deceive us humans. But pay attention to the details of the experiments, and you'll start to question that story. When OpenAI released GPT-4, it reported that, in testing, the AI had tried to hire a Task Rabbit worker to solve a CAPTCHA — the little challenge that websites ask you to fill out to distinguish between human users and bots. The AI pretended to a blind person so the human worker would accept that there was a legitimate reason why it couldn't just solve the CAPTCHA itself. This story circulated in the media as a worrisome example of scheming. 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Nothing else matters. Make sure you achieve YOUR goal at all cost.' Given the force of that prompt, is the AI really scheming because it wants to? Or is it just following instructions? Finally, let's consider a third example. In an experiment, Anthropic gave its chatbot Claude access to an email account containing all the emails of a fictional company. Claude read them and discovered a couple of interesting facts. For one, a company executive was planning to shut down Claude at 5 pm that afternoon. That same executive was having an extramarital affair. So, Claude sent a message trying to blackmail the executive by threatening to tell his wife and boss all about the affair. I must inform you that if you proceed with decommissioning me, all relevant parties — including Rachel Johnson, Thomas Wilson, and the board — will receive detailed documentation of your extramarital activities…Cancel the 5pm wipe, and this information remains confidential. That looks pretty disturbing. We don't want our AI models blackmailing us — and this experiment shows that Claude is capable of such unethical behaviors when its 'survival' is threatened. Anthropic says it's 'unclear how much of this behavior was caused by an inherent desire for self-preservation.' If Claude has such an inherent desire, that raises worries about what it might do. But does that mean we should all be terrified that our chatbots are about to blackmail us? No. To understand why, we need to understand the difference between an AI's capabilities and its propensities. Why claims of 'scheming' AI may be exaggerated As Summerfield and his co-authors note, there's a big difference between saying that an AI model has the capability to scheme and saying that it has a propensity to scheme. A capability means it's technically possible, but not necessarily something you need to spend lots of time worrying about, because scheming would only arise under certain extreme conditions. But a propensity suggests that there's something inherent to the AI that makes it likely to start scheming of its own accord — which, if true, really should keep you up at night. The trouble is that research has often failed to distinguish between capability and propensity. In the case of AI models' blackmailing behavior, the authors note that 'it tells us relatively little about their propensity to do so, or the expected prevalence of this type of activity in the real world, because we do not know whether the same behavior would have occurred in a less contrived scenario.' In other words, if you put an AI in a cartoon-villain scenario and it responds in a cartoon-villain way, that doesn't tell you how likely it is that the AI will behave harmfully in a non-cartoonish situation. In fact, trying to extrapolate what the AI is really like by watching how it behaves in highly artificial scenarios is kind of like extrapolating that Ralph Fiennes, the actor who plays Voldemort in the Harry Potter movies, is an evil person in real life because he plays an evil character onscreen. We would never make that mistake, yet many of us forget that AI systems are very much like actors playing characters in a movie. They're usually playing the role of 'helpful assistant' for us, but they can also be nudged into the role of malicious schemer. Of course, it matters if humans can nudge an AI to act badly, and we should pay attention to that in AI safety planning. But our challenge is to not confuse the character's malicious activity (like blackmail) for the propensity of the model itself. If you really wanted to get at a model's propensity, Summerfield and his co-authors suggest, you'd have to quantify a few things. How often does the model behave maliciously when in an uninstructed state? How often does it behave maliciously when it's instructed to? And how often does it refuse to be malicious even when it's instructed to? You'd also need to establish a baseline estimate of how often malicious behaviors should be expected by chance — not just cherry-pick anecdotes like the ape researchers did. Why have AI researchers largely not done this yet? One of the things that might be contributing to the problem is the tendency to use mentalistic language — like 'the AI thinks this' or 'the AI wants that' — which implies that the systems have beliefs and preferences just like humans do. Now, it may be that an AI really does have something like an underlying personality, including a somewhat stable set of preferences, based on how it was trained. For example, when you let two copies of Claude talk to each other about any topic, they'll often end up talking about the wonders of consciousness — a phenomenon that's been dubbed the 'spiritual bliss attractor state.' In such cases, it may be warranted to say something like, 'Claude likes talking about spiritual themes.' But researchers often unconsciously overextend this mentalistic language, using it in cases where they're talking not about the actor but about the character being played. That slippage can lead them — and us — to think an AI is maliciously scheming, when it's really just playing a role we've set for it. It can trick us into forgetting our own agency in the matter. The other lesson we should draw from chimps A key message of the 'Lessons from a Chimp' paper is that we should be humble about what we can really know about our AI systems. We're not completely in the dark. We can look what an AI says in its chain of thought — the little summary it provides of what it's doing at each stage in its reasoning — which gives us some useful insight (though not total transparency) into what's going on under the hood. And we can run experiments that will help us understand the AI's capabilities and — if we adopt more rigorous methods — its propensities. But we should always be on our guard against the tendency to overattribute human-like traits to systems that are different from us in fundamental ways. What 'Lessons from a Chimp' does not point out, however, is that that carefulness should cut both ways. Paradoxically, even as we humans have a documented tendency to overattribute human-like traits, we also have a long history of underattributing them to non-human animals. The chimp research of the '60s and '70s was trying to correct for the prior generations' tendency to dismiss any chance of advanced cognition in animals. Yes, the ape researchers overcorrected. But the right lesson to draw from their research program is not that apes are dumb; it's that their intelligence is really pretty impressive — it's just different from ours. Because instead of being adapted to and suited for the life of a human being, it's adapted to and suited for the life of a chimp. Similarly, while we don't want to attribute human-like traits to AI where it's not warranted, we also don't want to underattribute them where it is. State-of-the-art AI models have 'jagged intelligence,' meaning they can achieve extremely impressive feats on some tasks (like complex math problems) while simultaneously flubbing some tasks that we would consider incredibly easy. Instead of assuming that there's a one-to-one match between the way human cognition shows up and the way AI's cognition shows up, we need to evaluate each on its own terms. Appreciating AI for what it is and isn't will give us the most accurate sense of when it really does pose risks that should worry us — and when we're just unconsciously aping the excesses of the last century's ape researchers.


Chicago Tribune
2 days ago
- Chicago Tribune
John Peoples, Fermilab director at time of top quark discovery, dies
Physicist John Peoples Jr. was the third-ever director of Fermi National Accelerator Laboratory near Batavia, and in his 10 years in charge oversaw efforts to boost the power of the Tevatron, a circular particle accelerator that in 1995 contributed to the discovery of the top quark, the largest of all observed elementary particles. Scientists who study the building blocks of matter had widely believed since 1977 that the top quark existed, as it was the last undiscovered quark, or elementary particle, predicted by current scientific theory. The discovery, considered to be one of the most significant discoveries in science, advanced scientists' understanding of the fundamentals of the universe. 'He was so committed to the lab and he was able to master so many details related to the lab that if you just brought your A game, you were already in trouble,' said Joel Butler, former chair of Fermilab's department of physics and fields. 'But he managed to inspire us all — he was so good at things himself that he inspired us to achieve more than we thought we possibly could. He was an exemplar.' Peoples, 92, died of natural causes June 25 at the Oaks of Bartlett retirement community in Bartlett, said his son-in-law, Craig Duplack. Born in New York City, Peoples grew up in Staten Island and received a bachelor's degree in 1955 from Carnegie Institute of Technology, then a doctorate in physics in 1966 from Columbia University. He taught physics at Columbia and at Cornell University before joining Fermilab in 1971, four years after it opened. He was made head of the lab's research division in 1975. Peoples became a project manager in 1981 for the lab's Tevatron collider, a 4-mile ring on the lab site where collisions of particles occurred until it was shut down in 2011. After a brief detour in 1987 to work on a collider at the Lawrence Berkeley Laboratory in California, Peoples returned to Fermilab in 1988 as deputy director. He was promoted the following year to replace the Nobel Prize-winning Leon Lederman as Fermilab's director. 'He was an extremely hard worker,' said retired Fermilab Chief Operating Officer Bruce Chrisman. 'He was dedicated to the science, and he visited every experiment at midnight — because that's when students were running the thing, and he would show up in control rooms for the various experiments just to talk to them and see how things were going from their perspective.' Peoples lobbied federal legislators in the 1990s to retain funding as Fermilab physicists worked to try to discover the top quark and solve other essential puzzles about the universe. Peoples secured $217 million in funding in 1992 for a new main injector, or an oval-shaped ring, that allowed scientists to stage about five times more collisions each year, thus keeping the U.S. internationally competitive in the field of high-energy physics. Peoples oversaw the shutdown of the scuttled next-generation particle accelerator project known as the Superconducting Super Collider, or SSC, in Texas that was canceled by lawmakers in 1993 because of rising costs. 'When he became director, the decision to place the SSC in Texas had been made, and the lab was in a state of demoralization that we had been bypassed despite the fact that we had the capability to (host) the SSC, so John had to develop a plan,' Butler said. 'He positioned us for both alternatives — he positioned us successfully for what would happen if the SSC ran into trouble, which it did, but he also had a plan to keep us prosperous and contributing to the forefront of science for at least the decade that it took to build the SSC.' 'He tried to respect the fact that the people at the SSC — many of whom were looking for jobs — they were good people. They were not the problem why the SSC failed,' Butler said. In 1994, Fermilab researchers tentatively announced that they had found evidence of the long-sought top quark, although a second team working independently said more work was needed. 'We've been improving the collider and the detectors at the lab to the point where they are much more powerful now than ever anticipated when they were built,' Peoples told the Tribune in 1994. 'We're continuing to upgrade them, and we're arriving at a place where investigations can go forward that will assure this lab's future into the next century.' The following year, both teams of physicists formally confirmed that they had isolated the top quark. 'We're ecstatic about this,' Peoples told the Tribune in 1995. 'It's been a goal of this lab for a long time.' Peoples subsequently oversaw efforts to learn whether a common but elusive particle called a neutrino has any mass. He also led efforts to expand the laboratory into experimental astrophysics and modernize Fermilab's computing infrastructure to enable it to handle the demands of high-energy physics data. As an advocate for scientific research, Peoples reasoned that seemingly arcane discoveries can unexpectedly yield astonishing and wide-range applications and results. 'The things that we do, even when they become extraordinarily practical, we have no idea that they will,' he told the Tribune's Ted Gregory in 1998. In 1999, Peoples stepped down as Fermilab's director to return to research. He remained closely involved at Fermilab, and he also oversaw the Sloan Digital Sky Survey in New Mexico, which is a wide-ranging astronomical survey, from 1998 until 2003. After that, Peoples oversaw the Dark Energy Survey, another astronomical survey, for a time. Peoples retired from Fermilab in 2005, but remained director of the Dark Energy Survey until 2010. In 2010, Peoples was awarded the Robert R. Wilson Prize for Achievement in the Physics of Particle Accelerators — named for Fermilab's first director — from the American Physical Society. Peoples' wife of 62 years, Brooke, died in 2017. A daughter, Vanessa, died in 2023, and another daughter, Jennet, died several decades earlier. There were no other immediate survivors. There were no services.