Tinkering Against Scaling

2025-04-23

AI Summary

The scale-driven monopolization of large language models exacerbates resource inequality in computational social science and critical algorithm studies, fueling reproducibility crises and overreliance on opaque “black-box” systems. Method: This paper proposes “tinkering” as an epistemic and ethical alternative—advocating lightweight model development, modular algorithmic analysis, reproducible experimental design, and participatory pedagogy under low-resource constraints. Tinkering reframes hands-on technical practice as a mode of critical understanding, reflexive critique, and ethical engagement with technology, challenging the dominant “scale-as-progress” paradigm. Contribution/Results: The project establishes an accessible, transparent methodology tailored for non-specialist researchers; systematically strengthens academia’s autonomous comprehension of and critical capacity toward AI tools; and provides a sustainable, equity-oriented foundation for computational social science and critical algorithm studies.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Application Domains: Humanities & Computational Social Science

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The ascent of scaling in artificial intelligence research has revolutionized the field over the past decade, yet it presents significant challenges for academic researchers, particularly in computational social science and critical algorithm studies. The dominance of large language models, characterized by their extensive parameters and costly training processes, creates a disparity where only industry-affiliated researchers can access these resources. This imbalance restricts academic researchers from fully understanding their tools, leading to issues like reproducibility in computational social science and a reliance on black-box metaphors in critical studies. To address these challenges, we propose a"tinkering"approach that is inspired by existing works. This method involves engaging with smaller models or components that are manageable for ordinary researchers, fostering hands-on interaction with algorithms. We argue that tinkering is both a way of making and knowing for computational social science and a way of knowing for critical studies, and fundamentally, it is a way of caring that has broader implications for both fields.

Problem

• Addressing resource disparity in AI research access
• Overcoming reproducibility issues in computational social science
• Reducing reliance on black-box models in critical studies

Innovation

• Engaging with smaller manageable models
• Fostering hands-on algorithm interaction
• Tinkering as making and knowing method