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Current and future AI systems suffer from insufficient predictability, undermining trust, obscuring accountability, increasing loss-of-control risks, and raising safety concerns.
Method: This work introduces âPredictable AIâ as a novel paradigm that prioritizes predictability over raw performanceâestablishing it as the foundational prerequisite for trustworthiness, controllability, alignment, and safety. We formally define AI predictability for the first time, decomposing its core components, trade-off mechanisms, and the predictabilityâeffectiveness boundary. We specify prediction targets, candidate predictors, and evaluation dimensions, thereby delineating a distinct research direction independent of conventional AI evaluation frameworks. Through formal modeling, conceptual analysis, and cross-scenario reasoning, we construct a multi-dimensional trade-off framework and a foundational theoretical system.
Contribution: The work provides original theoretical foundations and practical guidance for developing AI systems that are both predictable and effective, clarifying technical pathways and interdisciplinary connections.
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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.