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ARTAI: An Evaluation Platform to Assess Societal Risk of Recommender Algorithms

arXiv 2409.12396 ↗First posted 19 Sep 2024cs.CY cs.AIcited by 0

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Abstract (arXiv)

Societal risk emanating from how recommender algorithms disseminate content online is now well documented. Emergent regulation aims to mitigate this risk through ethical audits and enabling new research on the social impact of algorithms. However, there is currently a need for tools and methods that enable such evaluation. This paper presents ARTAI, an evaluation environment that enables large-scale assessments of recommender algorithms to identify harmful patterns in how content is distributed online and enables the implementation of new regulatory requirements for increased transparency in recommender systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 14 references that are in the map. First posted on arXiv 19 Sep 2024.

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5 of 14 references.

References · 5

  1. 2019RecSim: A Configurable Simulation Platform for Recommender Systemscited by 91
  2. 2018Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learningcited by 80
  3. 2023A Content-Driven Micro-Video Recommendation Dataset at Scalecited by 43
  4. 2022Estimating and Penalizing Induced Preference Shifts in Recommender Systemscited by 29
  5. 2021Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulationcited by 19

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