mlsanityData to 7 Oct 2026

Predictable Artificial Intelligence

arXiv 2310.06167 ↗First posted 9 Oct 2023cs.AIcited by 8

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Abstract (arXiv)

We introduce the fundamental ideas and challenges of Predictable AI, a nascent research area that explores the ways in which we can anticipate key validity indicators (e.g., performance, safety) of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems, and thus should be prioritised over performance. We formally characterise predictability, explore its most relevant components, illustrate what can be predicted, describe alternative candidates for predictors, as well as the trade-offs between maximising validity and predictability. To illustrate these concepts, we bring an array of illustrative examples covering diverse ecosystem configurations. Predictable AI is related to other areas of technical and non-technical AI research, but have distinctive questions, hypotheses, techniques and challenges. This paper aims to elucidate them, calls for identifying paths towards a landscape of predictably valid AI systems and outlines the potential impact of this emergent field.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 54 of its 153 references that are in the map. First posted on arXiv 9 Oct 2023.

Builds on

54 of 153 references.

References · 54

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38 more
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50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 2.

8 papers.

  1. 2024LLMs achieve adult human performance on higher-order theory of mind taskscited by 59
  2. 2024100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instancescited by 24
  3. 2024The Necessity of AI Audit Standards Boardscited by 9
  4. 202511Plus-Bench: Demystifying Multimodal LLM Spatial Reasoning with Cognitive-Inspired Analysiscited by 8
  5. 2025What should an AI assessor optimise for?cited by 0
  6. 2025RedunCut: Measurement-Driven Sampling and Accuracy Performance Modeling for Low-Cost Live Video Analyticscited by 0
  7. 2026AI Contagion in Social Networkscited by 0
  8. 2026Using profiles of cognitive capability to assess AI suitability for workplace taskscited by 0