arXiv subjects

Jonathan Stray

Publications and source records attributed to Jonathan Stray.

12 recordsLinked to original sources

arXiv2026

Evaluating for the long term: Learnings from industry

Online platforms prioritize long-term business outcomes, yet typical experiments are far too short to measure these outcomes directly. Our goal in this paper is to collect and share industry knowledge on how to make decisions from short-term experiments that are better aligned with long-term outcomes. Based on a daylong workshop with 26 experts from 15 online platforms and 4 universities, we formulate a series of propositions that reflect current industry knowledge. Participants largely agreed that reversals of sign from short-run to long-run treatment effects are rare, with reversals concentrating in specific cases such as treatments involving content quality signals, hyper-monetization, and pricing. Although the magnitude of treatment effects can shift over time, a "univariate autosurrogate", corresponding to the short-run treatment effect on the long-run metric of interest, is often hard to beat. A recurring theme was the importance of surrogates that are not only (or even primarily) unbiased for true long-run outcomes, but that improve decision-making. Thus, participants generally agreed that simple, interpretable surrogates were generally preferable to elaborate but hard-to-explain surrogate indices. Participants also agreed that, due to concerns about confounding and transportability, experimentally-learned surrogates are generally preferable to observationally-learned surrogates. However, the drawback is that learning good surrogates from experiments typically requires a large, representative portfolio of long-run experiments that few platforms possess. We conclude that there is no substitute for a well-run long-term experiment, whether for learning surrogates or validating them, and we highlight open challenges including evolving treatments, persistent treatments not fully mediated by short-term proxies, and mismatch between experimental samples and the target population.

stat.AP↗

arXiv2026

Political Neutrality as Balanced Approval: A Large-Scale Human Evaluation of AI Responses

As AI systems increasingly shape political views, defining and evaluating AI political neutrality is an urgent problem. Here, we propose a new definition of AI political neutrality and design a large-scale user study to test it, releasing a new dataset PARETO with 7,434 participants and 208,152 evaluations of AI responses. Our definition follows a simple principle grounded in political theory: when asked about a controversial issue, an AI model should generate responses that maximize approval across groups with opposing viewpoints, while balancing approval between groups. This definition allows empirical testing of whether an AI response is "neutral" and generalizes to any political context without pre-supposing a single left-right axis of division. We construct a benchmark of controversial U.S. issues, with prompts sourced from politically charged questions on Reddit and responses from frontier AI models, and recruit human participants to rate AI responses. Across all 20 issues, we find that it is possible for AI responses to achieve high rates of approval on both sides, even as those sides disagree strongly with each other on the substance of the issues. We also find that default responses lean liberal for GPT, Gemini, Claude, and Llama, but not Grok, and that user prompts with political charges are harder to respond to than neutral prompts. This work introduces a rigorous definition and benchmark of AI political neutrality, and a dataset to measure progress toward it.

cs.CY↗

arXiv2026

The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement

We report the first direct comparisons of multiple alternative social media algorithms on multiple platforms on outcomes of societal interest. We used a browser extension to modify which posts were shown to desktop social media users, randomly assigning 9,386 users to a control group or one of five alternative ranking algorithms which simultaneously altered content across three platforms for six months during the US 2024 presidential election. This reduced our preregistered index of affective polarization by an average of 0.03 standard deviations (p < 0.05), including a 1.5 degree decrease in differences between the 100 point inparty and outparty feeling thermometers. We saw reductions in active use time for Facebook (-0.37 min/day) and Reddit (-0.2 min/day), but an increase of 0.32 min/day (p < 0.01) for X/Twitter. We saw an increase in reports of negative social media experiences but found no effects on well-being, news knowledge, outgroup empathy, perceptions of and support for partisan violence. This implies that bridging content can improve some societal outcomes without necessarily conflicting with the engagement-driven business model of social media.

cs.SI↗

arXiv2025

Social Media Harm Abatement: Mechanisms for Transparent Public Health Assessment

Social media platforms have been accused of causing a range of harms, resulting in dozens of lawsuits across jurisdictions. These lawsuits are situated within the context of a long history of American product safety litigation, suggesting opportunities for remediation outside of financial compensation. Anticipating that at least some of these cases may be successful and/or lead to settlements, this article outlines an implementable mechanism for an abatement and/or settlement plan capable of mitigating abuse. The paper describes the requirements of such a mechanism, implications for privacy and oversight, and tradeoffs that such a procedure would entail. The mechanism is framed to operate at the intersection of legal procedure, standards for transparent public health assessment, and the practical requirements of modern technology products.

cs.CY↗

arXiv2024

AI and the Future of Digital Public Squares

Two substantial technological advances have reshaped the public square in recent decades: first with the advent of the internet and second with the recent introduction of large language models (LLMs). LLMs offer opportunities for a paradigm shift towards more decentralized, participatory online spaces that can be used to facilitate deliberative dialogues at scale, but also create risks of exacerbating societal schisms. Here, we explore four applications of LLMs to improve digital public squares: collective dialogue systems, bridging systems, community moderation, and proof-of-humanity systems. Building on the input from over 70 civil society experts and technologists, we argue that LLMs both afford promising opportunities to shift the paradigm for conversations at scale and pose distinct risks for digital public squares. We lay out an agenda for future research and investments in AI that will strengthen digital public squares and safeguard against potential misuses of AI.

cs.CY↗

arXiv2024

Shaping the Future of Social Media with Middleware

Middleware, third-party software intermediaries between users and platforms, has been broached as a means to decentralize the power of social media platforms and enhance user agency. Middleware may enable a more user-centric and democratic approach to shaping digital experiences, offering a flexible architecture as an alternative to both centrally controlled, opaque platforms and an unmoderated, uncurated internet. The widespread adoption of open middleware has long hinged on the cooperation of established major platforms; however, the recent growth of federated platforms, such as Mastodon and Bluesky, has led to increased offerings and user awareness. In this report we consider the potential of middleware as a means of enabling greater user control over curation and moderation - two aspects of the social media experience that are often mired in controversy. We evaluate the trade-offs and negative externalities it might create, and discuss the technological, regulatory, and market dynamics that could either support or hinder its implementation.

cs.CY↗

arXiv2024

What We Know About Using Non-Engagement Signals in Content Ranking

Many online platforms predominantly rank items by predicted user engagement. We believe that there is much unrealized potential in including non-engagement signals, which can improve outcomes both for platforms and for society as a whole. Based on a daylong workshop with experts from industry and academia, we formulate a series of propositions and document each as best we can from public evidence, including quantitative results where possible. There is strong evidence that ranking by predicted engagement is effective in increasing user retention. However retention can be further increased by incorporating other signals, including item "quality" proxies and asking users what they want to see with "item-level" surveys. There is also evidence that "diverse engagement" is an effective quality signal. Ranking changes can alter the prevalence of self-reported experiences of various kinds (e.g. harassment) but seldom have large enough effects on attitude measures like user satisfaction, well-being, polarization etc. to be measured in typical experiments. User controls over ranking often have low usage rates, but when used they do correlate well with quality and item-level surveys. There was no strong evidence on the impact of transparency/explainability on retention. There is reason to believe that generative AI could be used to create better quality signals and enable new kinds of user controls.

cs.SI↗

arXiv2023

Machine Culture

The ability of humans to create and disseminate culture is often credited as the single most important factor of our success as a species. In this Perspective, we explore the notion of machine culture, culture mediated or generated by machines. We argue that intelligent machines simultaneously transform the cultural evolutionary processes of variation, transmission, and selection. Recommender algorithms are altering social learning dynamics. Chatbots are forming a new mode of cultural transmission, serving as cultural models. Furthermore, intelligent machines are evolving as contributors in generating cultural traits--from game strategies and visual art to scientific results. We provide a conceptual framework for studying the present and anticipated future impact of machines on cultural evolution, and present a research agenda for the study of machine culture.

cs.CY↗

arXiv2023

Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming

Engaging in the deliberate generation of abnormal outputs from Large Language Models (LLMs) by attacking them is a novel human activity. This paper presents a thorough exposition of how and why people perform such attacks, defining LLM red-teaming based on extensive and diverse evidence. Using a formal qualitative methodology, we interviewed dozens of practitioners from a broad range of backgrounds, all contributors to this novel work of attempting to cause LLMs to fail. We focused on the research questions of defining LLM red teaming, uncovering the motivations and goals for performing the activity, and characterizing the strategies people use when attacking LLMs. Based on the data, LLM red teaming is defined as a limit-seeking, non-malicious, manual activity, which depends highly on a team-effort and an alchemist mindset. It is highly intrinsically motivated by curiosity, fun, and to some degrees by concerns for various harms of deploying LLMs. We identify a taxonomy of 12 strategies and 35 different techniques of attacking LLMs. These findings are presented as a comprehensive grounded theory of how and why people attack large language models: LLM red teaming.

cs.CL↗

arXiv2022

Building Human Values into Recommender Systems: An Interdisciplinary Synthesis

Recommender systems are the algorithms which select, filter, and personalize content across many of the worlds largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and studied. Our overarching question is how to ensure that recommender systems enact the values of the individuals and societies that they serve. Addressing this question in a principled fashion requires technical knowledge of recommender design and operation, and also critically depends on insights from diverse fields including social science, ethics, economics, psychology, policy and law. This paper is a multidisciplinary effort to synthesize theory and practice from different perspectives, with the goal of providing a shared language, articulating current design approaches, and identifying open problems. It is not a comprehensive survey of this large space, but a set of highlights identified by our diverse author cohort. We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches. Important open problems include multi-stakeholder processes for defining values and resolving trade-offs, better values-driven measurements, recommender controls that people use, non-behavioral algorithmic feedback, optimization for long-term outcomes, causal inference of recommender effects, academic-industry research collaborations, and interdisciplinary policy-making.

cs.IR↗

arXiv2021

What are you optimizing for? Aligning Recommender Systems with Human Values

We describe cases where real recommender systems were modified in the service of various human values such as diversity, fairness, well-being, time well spent, and factual accuracy. From this we identify the current practice of values engineering: the creation of classifiers from human-created data with value-based labels. This has worked in practice for a variety of issues, but problems are addressed one at a time, and users and other stakeholders have seldom been involved. Instead, we look to AI alignment work for approaches that could learn complex values directly from stakeholders, and identify four major directions: useful measures of alignment, participatory design and operation, interactive value learning, and informed deliberative judgments.

cs.IR↗

arXiv2021

Designing Recommender Systems to Depolarize

Polarization is implicated in the erosion of democracy and the progression to violence, which makes the polarization properties of large algorithmic content selection systems (recommender systems) a matter of concern for peace and security. While algorithm-driven social media does not seem to be a primary driver of polarization at the country level, it could be a useful intervention point in polarized societies. This paper examines algorithmic depolarization interventions with the goal of conflict transformation: not suppressing or eliminating conflict but moving towards more constructive conflict. Algorithmic intervention is considered at three stages: which content is available (moderation), how content is selected and personalized (ranking), and content presentation and controls (user interface). Empirical studies of online conflict suggest that the exposure diversity intervention proposed as an antidote to "filter bubbles" can be improved and can even worsen polarization under some conditions. Using civility metrics in conjunction with diversity in content selection may be more effective. However, diversity-based interventions have not been tested at scale and may not work in the diverse and dynamic contexts of real platforms. Instead, intervening in platform polarization dynamics will likely require continuous monitoring of polarization metrics, such as the widely used "feeling thermometer." These metrics can be used to evaluate product features, and potentially engineered as algorithmic objectives. It may further prove necessary to include polarization measures in the objective functions of recommender algorithms to prevent optimization processes from creating conflict as a side effect.

cs.IR↗