A Normative Framework for Assessing the Information Curation Algorithms of the Internet.

Area of Science:

• Digital Media Studies
• Algorithmic Governance
• Information Science

Background:

• Algorithms curate online information, significantly influencing user exposure.
• Understanding algorithmic impact on societal values is critical.
• Existing assessments lack a comprehensive normative framework.

Purpose of the Study:

• To propose a normative framework for assessing information curation algorithms.
• To evaluate algorithms at both individual and systemic levels.
• To guide future research and inform policy interventions.

Main Methods:

• Developed a two-tiered assessment framework: individual and systemic.
• Individual assessment criteria: user interest alignment, accuracy, and "agency hacking" (over-appealing content).
• Systemic assessment criteria: civic effects (e.g., polarization), distributional/discriminatory effects, and anticompetitive platform advantages.

Main Results:

• The framework provides a structured approach to algorithm assessment.
• Identifies key individual-level concerns: relevance, accuracy, and user self-regulation.
• Highlights systemic risks: political polarization, discrimination, and market dominance.

Conclusions:

• The proposed framework offers a robust tool for evaluating information curation algorithms.
• It facilitates a deeper understanding of algorithmic societal impacts.
• Aims to empower policymakers and researchers to foster a healthier information ecosystem.