mlsanityData to 14 Oct 2026

Sociotechnical Implications of Generative Artificial Intelligence for Information Access

arXiv 2405.11612 ↗First posted 19 May 2024cs.IR cs.AIcited by 6

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Abstract (arXiv)

Robust access to trustworthy information is a critical need for society with implications for knowledge production, public health education, and promoting informed citizenry in democratic societies. Generative AI technologies may enable new ways to access information and improve effectiveness of existing information retrieval systems but we are only starting to understand and grapple with their long-term social implications. In this chapter, we present an overview of some of the systemic consequences and risks of employing generative AI in the context of information access. We also provide recommendations for evaluation and mitigation, and discuss challenges for future research.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 69 of its 223 references that are in the map. First posted on arXiv 19 May 2024.

Builds on

69 of 223 references.

References · 69

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38 more
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50 most cited of 69 All 69 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 5.

6 papers.

  1. 2024Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Modelscited by 15
  2. 2026Understanding Risk and Dependency in AI Chatbot Use from User Discoursecited by 2
  3. 2026Information Access of the Oppressed: Freirean Design for Emancipatory Information Accesscited by 0
  4. 2026From SERPs to Sound: How Search Engine Result Pages and AI-generated Podcasts Interact to Influence User Attitudes on Controversial Topicscited by 0
  5. 2026From Noise to Order: Learning to Rank via Denoising Diffusioncited by 0
  6. 2026Report on CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS)cited by 0