Want to Strengthen American Democracy? The Science of Collaboration Can Help
This article is part of the magazine, "The Future of Science In America: The Election Issue," co-published by LeapsMag, the Aspen Institute Science & Society Program, and GOOD.
American politics has no shortage of ailments. Many do not feel like their voice matters amid the money and influence amassed by corporations and wealthy donors. Many doubt whether elected officials and bureaucrats can or even want to effectively solve problems and respond to citizens' needs. Many feel divided both physically and psychologically, and uncomfortable (if not scared) at the prospect of building new connections across lines of difference.
Strengthening American democracy requires countering these trends. New collaborations between university researchers and community leaders such as elected officials, organizers, and nonprofit directors can help. These collaborations can entail everything from informal exchanges to co-led projects.
But there's a catch. They require that people with diverse forms of knowledge and lived experience, who are often strangers, choose to engage with one another. We know that strangers often remain strangers.
That's why a science of collaboration that centers the inception question is vital: When do diverse individuals choose to work together in the first place? How can we design institutions that encourage beneficial collaborations to arise and thrive? And what outcomes can occur?
How Collaborations Between Researchers and Community Leaders Can Help
First consider the feeling of powerlessness. Individual action becomes more powerful when part of a collective. For ordinary citizens, voting and organizing are arguably the two most impactful forms of collective action, and as it turns out there is substantial research on how to increase turnout and how to build powerful civic associations. Collaborations between researchers familiar with that work and organizers and nonprofit leaders familiar with a community's context can be especially impactful.
For example, in 2019, climate organizers with a nonpartisan group in North Carolina worked with a researcher who studies organizing to figure out how to increase volunteer commitment—that is, how to transform volunteers who only attend meetings into leaders who take responsibility for organizing others. Together, they designed strategies that made sense for the local area. Once implemented, these strategies led to a 161% year-over-year increase in commitment. More concretely, dozens of newly empowered volunteers led events to raise awareness of how climate change was impacting the local community and developed relationships with local officials and business owners, all while coming to see themselves as civic leaders. This experience also fed back into the researcher's work, motivating the design of future studies.
Or consider how researchers and local elected officials can collaborate and respond to novel challenges like the coronavirus. For instance, in March 2020, one county in Upstate New York suddenly had to figure out how to provide vital services like internet and health screenings for residents who could no longer visit shuttered county offices. They turned to a researcher who knew about research on mobile vans. Together, they spoke about the benefits and costs of mobile vans in general, and then segued into a more specific conversation about what routings and services would make sense in this specific locale. Their collaboration entailed a few conversations leading up to the county's decision, and in the end the county received helpful information and the researcher learned about new implementation challenges associated with mobile vans.
In April, legislators in another Upstate New York county realized they needed honest, if biting, feedback from local mayors about their response to the pandemic. They collaborated with researchers familiar with survey methodology. County legislators supplied the goals and historical information about fraught county–city relationships, while researchers supplied evidence-based techniques for conducting interviews in delicate contexts. These interviews ultimately revealed mayors' demand for more up-to-date coronavirus information from the county and also more county-led advocacy at the state level.
To be sure, there are many situations in which elected officials' lack of information is not the main hurdle. Rather, they need an incentive to act. Yet this is another situation in which collaborations between university researchers and community leaders focused on evidence-based, context-appropriate approaches to organizing and voter mobilization could produce needed pressure.
This brings me to the third way in which collaborations between researchers and community leaders can strengthen American democracy. They entail diverse people working to develop a common interest by building new connections across lines of difference. This is a core democratic skill that withers in the absence of practice.
In addition to credibility, we've learned that potential collaborators also care about whether others will be responsive to their goals and constraints, understand their point of view, and will be enjoyable to interact with.
The Science of Collaboration
The previous examples have one thing in common: a collaboration actually took place.
Yet that often does not happen. While there are many reasons why collaborations between diverse people should arise we know far less about when they actually do arise.
This is why a science of collaboration centered on inception is essential. Some studies have already revealed new insights. One thing we've learned is that credibility is important, but often not enough. By credibility, I mean that people are more likely to collaborate when they perceive each other to be trustworthy and have useful information or skills to share. Potential collaborators can signal their credibility by, for instance, identifying shared values and mentioning relevant previous experiences. One study finds that policymakers are more interested in collaborating with researchers who will share findings that are timely and locally relevant—that is, the kind that are most useful to them.
In addition to credibility, we've learned that potential collaborators also care about whether others will be responsive to their goals and constraints, understand their point of view, and will be enjoyable to interact with. For instance, potential collaborators can explicitly acknowledge that they know the other person is busy, or start with a question rather than a statement to indicate being interested. One study finds that busy nonprofit leaders are more likely to collaborate with researchers who explicitly state that (a) they are interested in learning about the leaders' expertise, and (b) they will efficiently share what they know. Another study underscores that potential collaborators need to feel like they know how to interact—that is, to feel like they have a "script" for what's appropriate to say during the interaction.
We're also learning that institutions (such as matchmaking organizations) can reduce uncertainty about credibility and relationality, and also help collaborations start off on the right foot. They are a critical avenue for connecting strangers. For instance, brokers can use techniques that increase the likelihood that diverse people feel comfortable sharing what they know, raising concerns, and being responsive to others.
Looking Ahead
A science of collaboration that centers the inception question is helpful on two levels. First, it provides an evidence base for how to effectively connect diverse people to work together. Second, when applied to university researchers and community leaders, it can produce collaborations that strengthen American democracy. Moreover, these collaborations are easily implemented, especially when informal and beginning as a conversation or two (as in the mobile vans example).
Existing research on the science of collaboration has already yielded actionable insights, yet we still have much to learn. For instance, we need to better understand the latent demand. Interviews that ask a wide variety of community leaders and researchers who have not previously collaborated to talk about why doing so might be helpful would be enlightening. They could also be a useful antidote to the narrative of conflict that often permeates discussions about the role of science in American politics.
In addition, we need to learn more about the downstream consequences of these collaborations, such as whether new networks arise that include colleagues of the initial collaborators. Here, it would be helpful to study the work of brokers – how they introduce people to each other, how much they follow up, and the impact of those decisions.
Ultimately, expanding the evidence base of the science of collaboration, and then directly applying what we learn, will provide important new and actionable avenues for strengthening American democracy.
[Editor's Note: To read other articles in this special magazine issue, visit the beautifully designed e-reader version.]
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.