mlsanityData to 14 Oct 2026

Contextual Confidence and Generative AI

arXiv 2311.01193 ↗First posted 2 Nov 2023cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Generative AI models perturb the foundations of effective human communication. They present new challenges to contextual confidence, disrupting participants' ability to identify the authentic context of communication and their ability to protect communication from reuse and recombination outside its intended context. In this paper, we describe strategies--tools, technologies and policies--that aim to stabilize communication in the face of these challenges. The strategies we discuss fall into two broad categories. Containment strategies aim to reassert context in environments where it is currently threatened--a reaction to the context-free expectations and norms established by the internet. Mobilization strategies, by contrast, view the rise of generative AI as an opportunity to proactively set new and higher expectations around privacy and authenticity in mediated communication.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 59 of its 166 references that are in the map. First posted on arXiv 2 Nov 2023.

Builds on

59 of 166 references.

References · 59

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  5. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  6. 2016Deep Learning with Differential Privacycited by 2,728
  7. 2020Extracting Training Data from Large Language Modelscited by 2,189
  8. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  9. 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
  10. 2018Datasheets for Datasetscited by 1,386
  11. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,125
  12. 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
38 more
  1. 2021Ethical and social risks of harm from Language Modelscited by 966
  2. 2023A Watermark for Large Language Modelscited by 725
  3. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 629
  4. 2023Textbooks Are All You Need II: phi-1.5 technical reportcited by 565
  5. 2020What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimationcited by 497
  6. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 384
  7. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 384
  8. 2023The Curse of Recursion: Training on Generated Data Makes Models Forgetcited by 356
  9. 2023Studying Large Language Model Generalization with Influence Functionscited by 326
  10. 2023Provable Robust Watermarking for AI-Generated Textcited by 299
  11. 2023TRAK: Attributing Model Behavior at Scalecited by 283
  12. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 281
  13. 2022Datamodels: Predicting Predictions from Training Datacited by 189
  14. 2023On the Reliability of Watermarks for Large Language Modelscited by 189
  15. 2023Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robustcited by 187
  16. 2023Model evaluation for extreme riskscited by 177
  17. 2018The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigationcited by 165
  18. 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claimscited by 165
  19. 2023Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigationscited by 158
  20. 2022Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reductioncited by 138
  21. 2023Auditing large language models: a three-layered approachcited by 137
  22. 2022Training Data Influence Analysis and Estimation: A Surveycited by 135
  23. 2020Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hidingcited by 130
  24. 2023Foundation Models and Fair Usecited by 116
  25. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  26. 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 98
  27. 2023Evaluating the Social Impact of Generative AI Systems in Systems and Societycited by 92
  28. 2023Evading Watermark based Detection of AI-Generated Contentcited by 78
  29. 2022Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governancecited by 67
  30. 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
  31. 2022The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligencecited by 42
  32. 2022Structured access: an emerging paradigm for safe AI deploymentcited by 41
  33. 2023ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principlescited by 33
  34. 2022Scaling up Trustless DNN Inference with Zero-Knowledge Proofscited by 31
  35. 2023Towards best practices in AGI safety and governance: A survey of expert opinioncited by 27
  36. 2023Tools for Verifying Neural Models' Training Datacited by 22
  37. 2023Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?cited by 16
  38. 2021AI Certification: Advancing Ethical Practice by Reducing Information Asymmetriescited by 15

50 most cited of 59 All 59 (JSON)

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Authenticated Delegation and Authorized AI Agentscited by 65