How does human behavior change in the presence of AI systems that interact with large groups of people at scale? And how do algorithms change in return? From OpenClaw to the Instagram algorithm, technologists, policymakers, and courts continue to ask this question – with serious implications for society.

In these debates, one of the big mysteries is whether science can help explain human-AI behavior—and if so, how. On one hand, small changes in code, policy, and algorithm personalization can lead to big changes in algorithm behavior (Matias 2023). As a result, some scientists have suggested that we need continuous monitoring and testing of systems that are hard to predict (Dai et al 2025). On the other hand, we have courts and governments imposing design codes that are thought to produce general population protections from algorithmic harms (Iyer 2023). In the last year, a series of new studies have shown that different designs can have divergent effects, making it hard to impose one-size-fits-all regulations (Piccardi et al 2025, Stray et al 2025).

If changes in algorithms or behavior can change collective outcomes, we’re in for a more complex, more hopeful future of social ecologies

Just how universal are claims about humans and AI? Within the quantitative research on AI behavior, there is an unresolved tension between work on bias and work on effects. As Avriel Epps and I have written in a paper under review, a huge body of scholarship has shown that AI systems consistently treat people differently based on context and identity. Yet much of the large-scale scholarship has tended to look for average treatment effects across large populations and groups. While these views are not incompatible, they often lead people to conflicting suggestions for action.

There’s another problem: much of the science behind the study of AI in society also assumes that large corporations are the natural, rightful operators of AI systems. When researchers study population-level effects, they treat AI as something that is done to people, rather than something people can own, shape and control. That doesn’t have to be true. The Wikimedia movement and online community operators from Reddit to the Bluesky have long developed, tested, and operated their own AI systems. Growing movements are excited to re-wild the Internet. As a movement of nations, organizations, philanthropies, and communities are taking up a vision of people over profit in AI.

Research designs often treat AI as something that is done to people, rather than something people can own, shape and control

That’s why the universality question matters – if social AI always leads down the same path, then governing them is scientifically simple (if hard to organize politically). But if, as CAT Lab’s research has shown in some cases, changes in collective behavior or algorithms can make things different (Matias 2023), we’re in for a more complex, more hopeful future of social ecologies that we tend together with algorithms for the common good.

This year, CAT Lab will be announcing a set of micro-grants that will support scholars to work alongside communities to develop scientific understanding about collective human-algorithm behavior. We’re calling it the “HAB” Challenge – short for “human algorithm behavior.”

Each project we’re supporting will work with historical data about people’s interactions with an algorithm that shapes the community’s collective life – such as the popularity algorithms on Reddit, bots on Wikipedia, or other automated systems. In each case, scholars will collaborate with community partners to define outcome variables and questions that matter to them before undertaking a statistical forecasting challenge.

In many ways, this project looks like scientific challenges under the “Common Task” framework like the Netflix Prize or the Fragile Families Challenge. Unlike those challenges however, HAB teams will not be evaluated with a leaderboard on a single task defined by our team. Instead our project is focused on understanding ecosystem diversity. To set this up, multiple scholars will work with different communities on similar tech platforms. At the end of the project, we will ask teams to cross-validate their models and measurements with different communities. To increase the scientific reliability of the projects, CAT Lab will serve as the data controller and parcel out training data while preserving hold-out data for final tests.

Once all analyses are complete, we plan to jointly publish a paper that shares collective findings. We will also support scholars to publish research in your respective fields.

Improving Online Environments with Forecasting Models

This project focuses on forecasting metrics of importance to communities. In our conversations over the years, community leaders have often expressed interest in “early warning systems” that can let them know in advance when it might be important to organize an intervention. A classic example involves moments when large influxes of attention from recommender systems drive increases in harassment, something we have seen in some reddit communities. 

Prior work on forecasting has focused on maximizing revenue and engagement, neither of which are guaranteed to create healthy digital environments. But forecasting has other uses. In environment science, we have been excited about Dynamic Conservation, where forecasting models help the farming industry and conservation work together. California, for example, is using bird forecasts to reconcile migration patterns with the needs of farmers along the Pacific Flyway. Our colleagues at Cornell AgriTech are also using predictive models to help growers reduce pesticides by deploying them only when they’re needed. We think similar methods could help communities actively adapt to patterns in the behavior of humans, bots, and agents.

A Brief Example: Modeling Ecological Dynamics in Human Algorithm Behavior

Since the HAB project is focused on understanding human-algorithm behavior as ecosystems, the microgrants will support longitudinal modeling of historical data in communities with detailed logs of humans and automated systems. We also need those settings to have information about multiple distinct communities, cultures, or stakeholders. For example, CAT Lab has already collected a decade of continuous, longitudinal data about popularity algorithms across Reddit and how people have responded to them in thousands of different communities.

To illustrate what a challenge might look like, we had a conversation with our longtime partners at r/science, who have been collecting data with us on Reddit’s algorithms since 2016. This almost 20 year old community discusses new scientific research among contributors that can exceed twenty thousand in a single week. The community’s leaders care about reaching a wide audience with new findings, fostering meaningful discussion across a range of fields, and ensuring a safe, supportive environment for conversation. In our conversations, community leaders were most interested in the relationship between social media algorithms, information diversity, and the community’s ability to attract and retain new contributors. So we created a series of forecasting models to explain how those factors change together over time. 

The needs and goals of r/science are very different from other groups we’ve worked with over the years such as r/BlackPeopleTwitter, a group by and for Black conversation on Reddit (Frey 2026), or r/AskHistorians, who host questions and answers with historians (Gilbert 2020). These communities have very different behavior dynamics and very different relationships with social media algorithms and content moderation. For example, AskHistorians was promoted twice by Reddit’s HOT algorithm in a single year, while r/science was promoted multiple times per day. 

To study newcomer retention, co-lead Bastian Greshake Tzovaras and I developed competing models for r/science using the same data to see how they would compare. We cross-validated those models on future data from r/science and then tested them in AskHistorians to see how well they generalized. Bastian used machine learning models with a theory-free approach, while I built more classic regression models in conversation with prior published research. 

In a world of algorithm determinism, we would expect to see similar results for both communities. What we actually found was more interesting. While r/science and AskHistorians experience similar variance in newcomer retention, it was more predictable in r/science. In a community widely-popularized by Reddit’s algorithms, simply knowing the number of newcomers in a given week was enough to make reliable forecasts of how many people would still be commenting a month later. That was not as true in AskHistorians, where many other factors (such as the number of discussions per week) appear to play a role.

Across all models in both communities, we did see one commonality, something anticipated in a 2006 article by Elisabeth Joyce and Bob Kraut: periods where the community replied more to other people’s comments tended to have higher newcomer retention.

What did we learn from this? In the HAB project, we plan to value both the general and the specific, in conversation with the communities whose data we consider. If we learn any patterns of collective human-algorithm behavior that apply in most social contexts, we will value the rare general discoveries they represent. But we will also look for patterns that make some settings more unique, requiring the intimate care that only communities can offer.

While the ultimate projects will likely look quite different from this example and could well be on platforms other than Reddit, we hope this example helps illustrate the core elements of projects we are supporting with microgrants:

  • Longitudinal forecasting of human-algorithm behavior
  • Community input into measurements and priorities
  • Critical cross-validation of community-based models to multiple contexts
  • A commitment to science – linking your project to conversations about reproducible and/or general knowledge in your field, which could be Communication, Psychology, Sociology, HCI, or other related socio-technical contexts. Critical perspectives are welcome.
  • An openness to learn from differences/heterogeneity as well as similarities
  • A commitment to growing and communicating human understanding within science and with community partners, not just maximizing model fit

AI Policy: We are open to machine learning based measurements if reproducible measures can be reliably validated in context (so for example, no proprietary models). For more guidance on this, see our latest paper in Reviews Psychology (McLoughlin et al 2026). We are also open to projects that integrate data from multiple platforms (for example, integrating data from chatbots and search engines) to understand these dynamics.

1. Create community metrics together 2. Forecast outcomes 3. Compare models between communities

Proposing Communities and Platforms for Partnership

Later this fall, CAT Lab and our collaborators at Global Voices will announce a call for proposals. In the meantime, we’re finalizing the review committee and confirming datasets and community partners.

If your community or platform is interested to join the challenge, we would love to hear from you! We are especially looking for contexts where:

  • there are multiple identifiable communities, languages, or subcultures on the same platform interacting with an ecosystem of similar algorithms
  • data over time is available to research in a way that can be openly studied with safeguards for ethics, privacy, and community consent
  • there are enough observations that statistically valid research is possible

Acknowledgments

I am deeply grateful to Bastian and Ed for collaboration on the data analysis and theory for this post. We are grateful to the many communities who have put input into the ideas behind this project over the years, as well as to the Templeton World Charity Foundation and Ford Foundation for supporting this work.

We are also grateful to Daniele Turturici for publishing the beautiful CC BY-NC-ND 4.0 header image to this post.

References