Predictable Artificial Intelligence

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.Comment: 11 pages excluding references, 4 figures, and 2 tables. Paper Under Revie

Training on the Test Set: Mapping the System-Problem Space in AI

Many present and future problems associated with artificial intelligence are not due to its limitations, but to our poor assessment of its behaviour. Our evaluation procedures produce aggregated performance metrics that lack detail and quantified uncertainty about the following question: how will an AI system, with a particular profile \pi, behave for a new problem, characterised by a particular situation \mu? Instead of just aggregating test results, we can use machine learning methods to fully capitalise on this evaluation information. In this paper, we introduce the concept of an assessor model, \hat{R}(r|\pi,\mu), a conditional probability estimator trained on test data. We discuss how these assessors can be built by using information of the full system-problem space and illustrate a broad range of applications that derive from varied inferences and aggregations from \hat{R}. Building good assessor models will change the predictive and explanatory power of AI evaluation and will lead to new research directions for building and using them. We propose accompanying every deployed AI system with its own assessor

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Rethink reporting of evaluation results in AI.

Aggregate metrics and lack of access to results limit understanding