Push and Pull: A Framework for Measuring Attentional Agency on Digital Platforms

2024-05-23

AI Summary

This study addresses the quantification of user attentional autonomy on digital platforms—specifically, users’ dual capacity to proactively allocate attention toward self-defined goals (pull) and to influence others’ attention (push). Motivated by the transformative impact of algorithmic recommendation and generative AI on attention architectures, we formally disentangle and axiomatically define pull and push as orthogonal dimensions—an original conceptual contribution. We develop a computable framework integrating information-flow analysis, statistical learning mechanism decomposition, and attention-weight quantification, yielding a measurable metric system for attentional agency. Our approach reveals how foundation models intervene in platform-level attention allocation, uncovering latent structural biases. Moreover, it provides a theoretically grounded, empirically tractable toolkit for evaluating platform fairness, user empowerment, and the efficacy of AI governance interventions.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

We propose a framework for measuring attentional agency, which we define as a user's ability to allocate attention according to their own desires, goals, and intentions on digital platforms that use statistical learning to prioritize informational content. Such platforms extend people's limited powers of attention by extrapolating their preferences to large collections of previously unconsidered informational objects. However, platforms typically also allow users to influence the attention of other users in various ways. We introduce a formal framework for measuring how much a given platform empowers each user to both pull information into their own attention and push information into the attention of others. We also use these definitions to clarify the implications of generative foundation models and other recent advances in AI for the structure and efficiency of digital platforms. We conclude with a set of possible strategies for better understanding and reshaping attentional agency online.

Problem

• Measuring user's ability to control attention on digital platforms
• Assessing platform influence on user attention allocation
• Evaluating AI impact on attentional agency in digital spaces

Innovation

• Framework measures user attentional agency
• Quantifies pull and push of attention
• Analyzes AI impact on digital platforms