Computers and Society · Cross-listed from cs.AI
Evaluating Multimodal Narrative Understanding of Popular Hollywood Films
David Bamman, Kent K. Chang, Allison Cooper et al. (+7 more)
Multimodal language models increasingly show promise for enabling the large-scale computational analysis of film, opening up new avenues for learning about film history and the
evolution of narrative techniques. But the creation of stable
benchmarks built around Hollywood films is complicated by copyright protections. In this work, we address these concerns directly, by building a new collection of Hollywood films defined by two criteria: box office popularity (where we publish the first large-scale, open collection of weekly box office earnings reported by Variety magazine from 1922-1979); and likely public domain status (by researching copyright registrations and renewals in the US Catalog of Copyright Entries). We build a new
multimodal MCQ
benchmark on top of this collection that focuses on narrative elements that directly evaluate the abilities of models to inform meaningful research on film narrative; we find that many
vision-
language models struggle on this task (with many performing at near-chance levels of accuracy), while audio-visual models (including those that use audio in captioning scenes) reach a maximum accuracy of 61.1%, well below human-level performance.
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All authors David Bamman, Kent K. Chang, Allison Cooper, Juishan Hsu, Reina Kushihashi, Madison Mar, Arnav Podichetty, Rachael Samberg, Ipek Nil Sancak, Yuhan Shao
Computers and Society · Cross-listed from cs.CR
Protocol-Embedded Compliance for Privacy-Preserving, Non-Custodial Digital Payments
Santiago De Simone, Geoffrey Goodell and Georgios Samakovitis
Received wisdom on payments infrastructure strongly supports the custodial, account-based model as a necessity for transaction integrity, auditability and verification; the set of fundamental primitives for regulated digital money exchange, the argument goes, necessitates designated identifiable entities that store and process credentials, perform KYC, and ultimately act as the 'single version of the truth' for compliance remediation and, most important, AML. In this paper, we propose this is not the case, by arguing that non-custodial, cash-like digital assets can embody such capabilities, in an arguably more secure manner. To that end, we present a reference architecture and core protocol rules for digital-value-exchange systems that preserve meaningful user
privacy while enabling strong auditability. The protocol defines the conditions under which digital asset creation, transfer, and redemption are valid. The architecture specifies the allocation of actors, roles and components through which these rules operate, enabling independent verification of transaction compliance with applicable norms. Building upon the Unforgeable, Stateful, Oblivious (USO) asset model of Goodell et al., regulatory compliance data are embedded directly into the asset state as cryptographically signed attestations issued by independent entities. A transfer is valid only upon satisfaction of applicable compliance predicates and inclusion of the resulting signature within the asset state. Compliance enforcement is thus performed at the protocol level rather than through institutional custody or identity-based account control. We conclude that our proposed model can successfully interface with existing payment systems, making it possible to integrate non-custodial, compliance-verified transactions with legacy financial infrastructure.
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Computers and Society · Cross-listed from cs.AI
Toward Personal Intelligence Through Cooperative Observation
Yashar Talebirad, Osman Jime, Ali Parsaee et al. (+3 more)
A personal AI system needs a model of the user's goals, constraints, and ongoing commitments to plan and act on their behalf, and the quality of that model is bounded by what the system can observe. Broader observation does not by itself improve assistance because a bounded system must select and compress information for the task at hand. We argue that this observation bottleneck has a cooperative structure: the system builds a partial model of the user's changing life, the user evaluates its actions, and the user's consent and control shape what it can observe next. Useful and inspectable behavior can give users a reason to maintain or expand the observation channel, while failures can lead them to correct, narrow, revoke, or abandon it. We use the term cooperative observation for this feedback loop among usefulness, trust, and future access, and propose it as a framework for personal intelligence. We report a preliminary single-subject account from Organizm, a prototype used over six months, and outline evaluation directions for measuring how observation quality shapes personal AI.
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All authors Yashar Talebirad, Osman Jime, Ali Parsaee, Eden Redman, Yongbin Kim, Osmar R. Zaiane
Computers and Society · Cross-listed from econ.GN
Stranded Credentials: Keeping Online Reputation Systems Informative in the AI Era
Song Yao
Platforms summarize providers' past achievements into credentials that buyers use to judge quality. Generative AI can now produce much of the work those achievements certify, raising fears that those quality signals are worthless. We audit how well such credentials stay informative in Kaggle's 2010-2026 archive, where medals are won on predictions scored against withheld answers and two evaluation formats ran side by side. Across 444,698 participations, a medal's power to predict performance sits almost entirely in its first year, in both formats and eras. Fresh medals kept most of their value through the AI transition. About half of the collapse in the informativeness of one format's medals is institutional: the platform had been phasing out that format before AI, and its medal stock aged out on schedule. Old medals look more informative only in isolation. The platform's official lifetime-tier display discards up to a sixth of the medals' predictive power. A recency-weighted index fit before the AI era explains AI-era performance about 13% better than the tiers and selects entrants who perform better on average, though the tiers still identify extreme top performers better. Displaying a recent-performance summary alongside the lifetime tiers would recover the discarded information for buyers.
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Computers and Society
Language Models Reproduce Human Reductionist Bias and Decision Inconsistency in Neurodevelopmental Disorders Assessment
Maciej Wodziński, Joanna Wodzińska, Kacper Dudzic et al. (+1 more)
Large
language models (LLMs) are increasingly supporting complex mental-health decisions, which depend not only on factual evidence but also value-laden interpretations. We introduce a mixed-methods human-LLM auditing framework examining decision consistency, susceptibility to cognitive heuristics, declarative intellectual humility, and the concepts operationalized in support-allocation judgments of neurodevelopmental disorders. Comparing 35 humans (18 physicians and 17 psychologists) with seven LLMs, we show that in both groups, ratings of patients' functional level were not significantly associated with support-eligibility decisions, indicating an inconsistency between descriptive assessments and final evaluative judgments. Specifically, we find that neither group showed significant susceptibility to experimental manipulations targeting anchoring and representativeness heuristics. LLMs reported higher intellectual humility than experts (U = 241, p < .001, r = .62; LLMs: M = 41.43, SD = 1.99; experts: M = 29.03, SD = 8.05), but it was unrelated to decision consistency or functional assessment. While LLMs and physicians granted support less frequently than psychologists (U = 180.50, p = .003, r = .34), they also interpreted a concept of "basic life needs" differently, primarily as biological survival and self-care, and not communicative and social needs. These findings suggest that despite expressing high levels of intellectual humility, LLMs reproduce a reductionist interpretive framework and knowledge embedded in medical decision-making. More broadly, we argue that evaluating AI in high-stakes contexts requires not only measuring accuracy, agreement, or resistance to cognitive bias, but also critical examination of the concepts of neurodiversity that AI systems operationalize.
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All authors Maciej Wodziński, Joanna Wodzińska, Kacper Dudzic, Marcin Moskalewicz
Computers and Society · Cross-listed from cs.HC
Appearing Legitimate is Not Enough: Interrogating Synthetic Agents in Representational Processes through a Participatory Design Lens
Aditya Nayak, Aditi Vashistha, Alissa Centivany et al. (+1 more)
Synthetic
agents built atop LLM-based foundation models are gaining popularity as substitutes for human participants across research contexts, including user-testing,
market-research, computational social science, surveys, and qualitative research. We are also witnessing an extension of synthetic
agents into experimental implementations of policy consultation, jury deliberation, humanitarian diplomacy, and similar contexts where human participation and representation are central to the perceived legitimacy of the institutional processes. The value of participation extends beyond informational contributions and consensus generation; participation is a necessary, legitimizing condition for democratic political institutions and processes. Treating synthetic
agents as human substitutes raises serious political, representational, and ethical concerns. Participatory Design's modes of engagement — probing, priming, understanding, and generating — offer helpful tools for engaging with representational questions of personhood. We apply the lens to three case studies of synthetic
agents substituting for personhood at varying representational scales: local policy, enterprise jury deliberation, and global diplomacy. We argue that legitimacy and personhood are integral and mutually constitutive while identifying the ethical, representational, and methodological risks of using synthetic
agents in representational processes. We conclude by proposing soft and hard boundaries for designing oversight on LLMs and synthetic
agents in representational processes.
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All authors Aditya Nayak, Aditi Vashistha, Alissa Centivany, Aakash Gautam
Computers and Society · Cross-listed from stat.AP
Statistical Assessments of Representational Reforms: A Case Study from Los Angeles
Sarah Cannon and Evan T. R. Rosenman
Electoral reforms – including changes to district boundaries, electoral rules, and the size of elected bodies – are a recurrent feature of American state and municipal politics. The representational impact of such reforms is challenging to assess before they are implemented, and even well-intentioned civic reformers are often surprised by the unintended consequences of changes to the political system. We develop a statistical framework for the prospective evaluation of electoral reforms by integrating several modes of analysis: descriptive statistics of the voter file, probabilistic race and ethnicity imputation, ecological inference, and redistricting simulations via ensembles. Together, these tools provide a unified assessment of how proposed reforms affect whose voices are ultimately heard in government. We apply this framework to reforms currently being considered for the Los Angeles City Council, including increasing the number of single-member districts, adopting ranked-choice voting, and introducing multimember districts elected by proportional ranked choice voting. We find that expanding the Council alone is unlikely to substantially improve representation for underrepresented communities. In contrast, eliminating low-turnout primary elections and adopting multimember districts produce larger improvements in representational equity. Our analyses were shared with L.A.'s Charter Reform Commission via public testimony in late 2025. Although motivated by Los Angeles, the framework is broadly applicable to evaluating prospective electoral reforms across American jurisdictions.
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Computers and Society
Wasted large language models: A life cycle thinking approach
Erik Johannes Husom, Maria Emine Nylund and Ophelia Prillard
Large
Language Models (LLMs) are machine learning (ML) models that have an increasingly large carbon footprint through their development and use. Efforts to increase the energy efficiency of these models have not translated into reduced consumption due to rebound effects such as Jevons Paradox - that increased efficiency drives increased use. There is therefore a need for additional measures to solve this problem. We suggest that one possible way forward is to use life cycle thinking, and view LLMs as products that can become waste. With this perspective, we investigate the potential of the waste hierarchy from the EU's Waste Framework Directive, which suggests five different measures for how to manage waste: prevention, reuse, recycling, recovery, and disposal. We examine how these measures can inform and motivate new types of thinking and approaches to reducing LLM waste and their environmental impact in general. Applying the waste hierarchy to LLMs highlights that preventing waste is essential for reducing the models' environmental impact, mainly because it reduces the need for training new models. Prevention can be achieved through many existing methods for reusing, "recycling", and "recovering" LLMs. Additionally, disposal can be important both for saving energy and for keeping a considerate attitude to the resources being spent on training LLMs. We also call to
attention that prevention of unnecessary use of LLMs carry huge potential for lowering the
climate impact of the models.
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Computers and Society
Without journalists, there is no journalism: the social dimension of generative artificial intelligence in the media
Simón Peña-Fernández, Koldobika Meso-Ayerdi, Ainara Larrondo-Ureta et al. (+1 more)
The implementation of artificial intelligence techniques and tools in the media will systematically and continuously alter their work and that of their professionals during the coming decades. To this end, this article carries out a systematic review of the research conducted on the implementation of AI in the media over the last two decades, particularly empirical research, to identify the main social and epistemological challenges posed by its adoption. For the media, increased dependence on technological platforms and the defense of their editorial independence will be the main challenges. Journalists, in turn, are torn between the perceived threat to their jobs and the loss of their symbolic capital as intermediaries between reality and audiences, and a liberation from routine tasks that subsequently allows them to produce higher quality content. Meanwhile, audiences do not seem to perceive a great difference in the quality and credibility of automated texts, although the ease with which texts are read still favors human authorship. In short, beyond technocentric or deterministic approaches, the use of AI in a specifically human field such as journalism requires a social approach in which the appropriation of innovations by audiences and the impact it has on them is one of the keys to its development. Therefore, the study of AI in the media should focus on analyzing how it can affect individuals and journalists, how it can be used for the proper purposes of the profession and social good, and how to close the gaps that its use can cause.
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All authors Simón Peña-Fernández, Koldobika Meso-Ayerdi, Ainara Larrondo-Ureta, Javier Díaz-Noci
Computers and Society · Cross-listed from cs.HC
"It just kind of shows that I went somewhere": An Exploratory Study of Fitness Data Sharing
Mara Solen, Thomas James Davidson, Emily Wall et al. (+1 more)
The sharing of curated fitness data posts occurs frequently on fitness-focused social platforms such as Strava and on general social media platforms such as Instagram, which is a novel context for visualization. To better understand the process of sharing and designing fitness data posts, as well as the role of visualization within them, we conduct a constructivist grounded theory study. We conduct and analyze 18 semi-structured interviews with fitness data sharers. From our analysis of the data, we find three novel characteristics of fitness data sharing: (i) the role of visualization as providing proof that an individual did an activity, (ii) the importance of expressing individuality in posts, and (iii) design conformity to cultural norms. We also derive a set of design implications, including a need for more options for visualizations for activities without routes, more user control in fitness data sharing platforms, and maintained ease of use while increasing customization options.
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All authors Mara Solen, Thomas James Davidson, Emily Wall, Tamara Munzner
Computers and Society · Cross-listed from cs.LG
Study-Strategy Clusters from EdNet Logs Track Engagement, Not Mastery
Qingchuan Lyu, Yingxin Li and Albert Yang
Learning analytics often treats unsupervised clusters of intelligent tutoring system (ITS) logs as learner types that should predict learning. We test that assumption on EdNet-KT3. Clustering study-strategy features (resource use, revision,
video, problem practice) for 5{,}000 active learners yields a silhouette-selected parent cut ($k=5$) with 4 contrast poles (reading-focused,
video-heavy, revision-heavy, and problem-first) plus a large near-mean residual ($\sim$64.9%). Reclustering that residual adds four finer styles, giving a bootstrap-stable hierarchy of 8 named strategies. We split each learner's timeline by respond count so clusters use only the early half and outcomes only the late half. Early clusters predict later engagement (continuing to practice and finishing late sessions, especially persistence, $\eta^{2}\approx 0.106$; completion $\eta^{2}\approx 0.021$) but not later unassisted accuracy (correctness on late first-attempts without help; $p_{\mathrm{adj}}\approx 0.093$). Volume rises with some styles, yet volume-only clustering barely matches strategy labels (ARI$=0.064$). A knowledge-tracing model (SAKT) on the seven TOEIC exam sections predicts next correctness only modestly better than a baseline that knows only how hard each section usually is (AUC lift $+0.051$; CI $[+0.045,+0.058]$), and that mastery signal is nearly independent of behavior styles (ARI$=0.007$). Behavioral clustering here describes study styles and engagement, not knowledge gains.
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Computers and Society · Cross-listed from cs.GT
The New Mathematics of Democracy
Bailey Flanigan and Ismar Volic
This article surveys emerging directions in the mathematics of democracy. It uses three case studies — voting theory, participatory budgeting, and deliberative democracy — to highlight how contemporary challenges motivate rigorous mathematical research that incorporates real-world data, institutional constraints, and implementation feasibility. Within each case, we highlight active and promising research frontiers, evidence of real-world impact, practical applications, and opportunities for getting involved.
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