arXiv subjects

Mahasweta Chakraborti

Publications and source records attributed to Mahasweta Chakraborti.

8 recordsLinked to original sources

arXiv2026

Patterns in the Transition From Founder-Leadership to Community Governance of Open Source

Open digital public infrastructure needs community management to ensure accountability, sustainability, and robustness. Yet open-source projects often rely on centralized decision-making, and the determinants of successful community management remain unclear. We analyze 637 GitHub repositories to trace transitions from founder-led to shared governance. Specifically, we document trajectories to community governance by extracting institutional roles, actions, and deontic cues from version-controlled project constitutions GOVERNANCE .md. With a semantic parsing pipeline, we cluster elements into broader role and action types. We find roles and actions grow, and regulation becomes more balanced, reflecting increases in governance scope and differentiation over time. Rather than shifting tone, communities grow by layering and refining responsibilities. As transitions to community management mature, projects increasingly regulate ecosystem-level relationships and add definition to project oversight roles. Overall, this work offers a scalable pipeline for tracking the growth and development of community governance regimes from open-source software's familiar default of founder-ownership.

cs.CY↗

arXiv2025

The Hidden AI Race: Tracking Environmental Costs of Innovation

The past decade has seen a massive rise in the popularity of AI systems, mainly owing to the developments in Gen AI, which has revolutionized numerous industries and applications. However, this progress comes at a considerable cost to the environment as training and deploying these models consume significant computational resources and energy and are responsible for large carbon footprints in the atmosphere. In this paper, we study the amount of carbon dioxide released by models across different domains over varying time periods. By examining parameters such as model size, repository activity (e.g., commits and repository age), task type, and organizational affiliation, we identify key factors influencing the environmental impact of AI development. Our findings reveal that model size and versioning frequency are strongly correlated with higher emissions, while domain-specific trends show that NLP models tend to have lower carbon footprints compared to audio-based systems. Organizational context also plays a significant role, with university-driven projects exhibiting the highest emissions, followed by non-profits and companies, while community-driven projects show a reduction in emissions. These results highlight the critical need for green AI practices, including the adoption of energy-efficient architectures, optimizing development workflows, and leveraging renewable energy sources. We also discuss a few practices that can lead to a more sustainable future with AI, and we end this paper with some future research directions that could be motivated by our work. This work not only provides actionable insights to mitigate the environmental impact of AI but also poses new research questions for the community to explore. By emphasizing the interplay between sustainability and innovation, our study aims to guide future efforts toward building a more ecologically responsible AI ecosystem.

cs.CY↗

arXiv2025

A Human Behavioral Baseline for Collective Governance in Software Projects

We study how open source communities describe participation and control through version controlled governance documents. Using a corpus of 710 projects with paired snapshots, we parse text into actors, rules, actions, and objects, then group them and measure change with entropy for evenness, richness for diversity, and Jensen Shannon divergence for drift. Projects define more roles and more actions over time, and these are distributed more evenly, while the composition of rules remains stable. These findings indicate that governance grows by expanding and balancing categories of participation without major shifts in prescriptive force. The analysis provides a reproducible baseline for evaluating whether future AI mediated workflows concentrate or redistribute authority.

cs.CL↗

arXiv2024

Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure

The rapid scaling of AI has spurred a growing emphasis on ethical considerations in both development and practice. This has led to the formulation of increasingly sophisticated model auditing and reporting requirements, as well as governance frameworks to mitigate potential risks to individuals and society. At this critical juncture, we review the practical challenges of promoting responsible AI and transparency in informal sectors like OSS that support vital infrastructure and see widespread use. We focus on how model performance evaluation may inform or inhibit probing of model limitations, biases, and other risks. Our controlled analysis of 7903 Hugging Face projects found that risk documentation is strongly associated with evaluation practices. Yet, submissions (N=789) from the platform's most popular competitive leaderboard showed less accountability among high performers. Our findings can inform AI providers and legal scholars in designing interventions and policies that preserve open-source innovation while incentivizing ethical uptake.

cs.HC↗

arXiv2024

NLP4Gov: A Comprehensive Library for Computational Policy Analysis

Formal rules and policies are fundamental in formally specifying a social system: its operation, boundaries, processes, and even ontology. Recent scholarship has highlighted the role of formal policy in collective knowledge creation, game communities, the production of digital public goods, and national social media governance. Researchers have shown interest in how online communities convene tenable self-governance mechanisms to regulate member activities and distribute rights and privileges by designating responsibilities, roles, and hierarchies. We present NLP4Gov, an interactive kit to train and aid scholars and practitioners alike in computational policy analysis. The library explores and integrates methods and capabilities from computational linguistics and NLP to generate semantic and symbolic representations of community policies from text records. Versatile, documented, and accessible, NLP4Gov provides granular and comparative views into institutional structures and interactions, along with other information extraction capabilities for downstream analysis.

cs.HC↗

arXiv2023

Do We Run How We Say We Run? Formalization and Practice of Governance in OSS Communities

Open Source Software (OSS) communities often resist regulation typical of traditional organizations. Yet formal governance systems are being increasingly adopted among communities, particularly through non-profit mentor foundations. Our study looks at the Apache Software Foundation Incubator program and 208 projects it supports. We assemble a scalable, semantic pipeline to discover and analyze the governance behavior of projects from their mailing lists. We then investigate the reception of formal policies among communities, through their own governance priorities and internalization of the policies. Our findings indicate that while communities observe formal requirements and policies as extensively as they are defined, their day-to-day governance focus does not dwell on topics that see most formal policy-making. Moreover formalization, be it dedicating governance focus or adopting policy, has limited association with project sustenance.

cs.HC↗

arXiv2022

Deconstructing written rules and hierarchy in peer produced software communities

We employ recent advances in computational institutional analysis and NLP to investigate the systems of authority that are reflected in the written policy documents of the ASF. Our study to decipher the effective similarities or departures of the ASF model from conventional software companies reveals evidence of both flat and bureaucratic governance in a peer production set up, suggesting a complicated relationship between business-based theories of administrative hierarchy and foundational principles of the OSS movement.

cs.CY↗

arXiv2021

Insta-VAX: A Multimodal Benchmark for Anti-Vaccine and Misinformation Posts Detection on Social Media

Sharing of anti-vaccine posts on social media, including misinformation posts, has been shown to create confusion and reduce the publics confidence in vaccines, leading to vaccine hesitancy and resistance. Recent years have witnessed the fast rise of such anti-vaccine posts in a variety of linguistic and visual forms in online networks, posing a great challenge for effective content moderation and tracking. Extending previous work on leveraging textual information to understand vaccine information, this paper presents Insta-VAX, a new multi-modal dataset consisting of a sample of 64,957 Instagram posts related to human vaccines. We applied a crowdsourced annotation procedure verified by two trained expert judges to this dataset. We then bench-marked several state-of-the-art NLP and computer vision classifiers to detect whether the posts show anti-vaccine attitude and whether they contain misinformation. Extensive experiments and analyses demonstrate the multimodal models can classify the posts more accurately than the uni-modal models, but still need improvement especially on visual context understanding and external knowledge cooperation. The dataset and classifiers contribute to monitoring and tracking of vaccine discussions for social scientific and public health efforts in combating the problem of vaccine misinformation.

cs.CL↗