Who’s Responsible If a Scientist’s Work Is Used for Harm?
Are scientists morally responsible for the uses of their work? To some extent, yes. Scientists are responsible for both the uses that they intend with their work and for some of the uses they don't intend. This is because scientists bear the same moral responsibilities that we all bear, and we are all responsible for the ends we intend to help bring about and for some (but not all) of those we don't.
To not think about plausible unintended effects is to be negligent -- and to recognize, but do nothing about, such effects is to be reckless.
It should be obvious that the intended outcomes of our work are within our sphere of moral responsibility. If a scientist intends to help alleviate hunger (by, for example, breeding new drought-resistant crop strains), and they succeed in that goal, they are morally responsible for that success, and we would praise them accordingly. If a scientist intends to produce a new weapon of mass destruction (by, for example, developing a lethal strain of a virus), and they are unfortunately successful, they are morally responsible for that as well, and we would blame them accordingly. Intention matters a great deal, and we are most praised or blamed for what we intend to accomplish with our work.
But we are responsible for more than just the intended outcomes of our choices. We are also responsible for unintended but readily foreseeable uses of our work. This is in part because we are all responsible for thinking not just about what we intend, but also what else might follow from our chosen course of action. In cases where severe and egregious harms are plausible, we should act in ways that strive to prevent such outcomes. To not think about plausible unintended effects is to be negligent -- and to recognize, but do nothing about, such effects is to be reckless. To be negligent or reckless is to be morally irresponsible, and thus blameworthy. Each of us should think beyond what we intend to do, reflecting carefully on what our course of action could entail, and adjusting our choices accordingly.
It is this area, of unintended but readily foreseeable (and plausible) impacts, that often creates the most difficulty for scientists. Many scientists can become so focused on their work (which is often demanding) and so focused on achieving their intended goals, that they fail to stop and think about other possible implications.
Debates over "dual-use" research exemplify these concerns, where harmful potential uses of research might mean the work should not be pursued, or the full publication of results should be curtailed. When researchers perform gain-of-function research, pushing viruses to become more transmissible or more deadly, it is clear how dangerous such work could be in the wrong hands. In these cases, it is not enough to simply claim that such uses were not intended and that it is someone else's job to ensure that the materials remain secure. We know securing infectious materials can be error-prone (recall events at the CDC and the FDA).
In some areas of research, scientists are already worrying about the unintended possible downsides of their work.
Further, securing viral strains does nothing to secure the knowledge that could allow for reproducing the viral strain (particularly when the methodologies and/or genetic sequences are published after the fact, as was the case for H5N1 and horsepox). It is, in fact, the researcher's moral responsibility to be concerned not just about the biosafety controls in their own labs, but also which projects should be pursued (Will the gain in knowledge be worth the possible downsides?) and which results should be published (Will a result make it easier for a malicious actor to deploy a new bioweapon?).
We have not yet had (to my knowledge) a use of gain-of-function research to harm people. If that does happen, those who actually released the virus on the public will be most blameworthy–-intentions do matter. But the scientists who developed the knowledge deployed by the malicious actors may also be held blameworthy, especially if the malicious use was easy to foresee, even if it was not pleasant to think about.
In some areas of research, scientists are already worrying about the unintended possible downsides of their work. Scientists investigating gene drives have thought beyond the immediate desired benefits of their work (e.g. reducing invasive species populations) and considered the possible spread of gene drives to untargeted populations. Modeling the impacts of such possibilities has led some researchers to pull back from particular deployment possibilities. It is precisely such thinking through both the intended and unintended possible outcomes that is needed for responsible work.
The world has gotten too small, too vulnerable for scientists to act as though they are not responsible for the uses of their work, intended or not. They must seek to ensure that, as the recent AAAS Statement on Scientific Freedom and Responsibility demands, their work is done "in the interest of humanity." This requires thinking beyond one's intentions, potentially drawing on the expertise of others, sometimes from other disciplines, to help explore implications. The need for such thinking does not guarantee good outcomes, but it will ensure that we are doing the best we can, and that is what being morally responsible is all about.
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.