Predictable Artificial Intelligence
- URL: http://arxiv.org/abs/2310.06167v1
- Date: Mon, 9 Oct 2023 21:36:21 GMT
- Title: Predictable Artificial Intelligence
- Authors: Lexin Zhou, Pablo A. Moreno-Casares, Fernando Mart\'inez-Plumed, John
Burden, Ryan Burnell, Lucy Cheke, C\`esar Ferri, Alexandru Marcoci, Behzad
Mehrbakhsh, Yael Moros-Daval, Se\'an \'O h\'Eigeartaigh, Danaja Rutar, Wout
Schellaert, Konstantinos Voudouris, Jos\'e Hern\'andez-Orallo
- Abstract summary: We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
This paper aims to elucidate the questions, hypotheses and challenges relevant to Predictable AI.
- Score: 67.79118050651908
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce the fundamental ideas and challenges of Predictable AI, a
nascent research area that explores the ways in which we can anticipate key
indicators 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.
While distinctive from other areas of technical and non-technical AI research,
the questions, hypotheses and challenges relevant to Predictable AI were yet to
be clearly described. This paper aims to elucidate them, calls for identifying
paths towards AI predictability and outlines the potential impact of this
emergent field.
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