Desiderata for next generation of ML model serving
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Inference is a significant part of ML software infrastructure. Despite the variety of inference frameworks available, the field as a whole can be considered in its early days. This position paper puts forth a range of important qualities that next generation of inference platforms should be aiming for. We present our rationale for the importance of each quality, and discuss ways to achieve it in practice. We propose to focus on data-centricity as the overarching design pattern which enables smarter ML system deployment and operation at scale.
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Bridging the Disciplinary Gap in Explainable AI: From Abstract Desiderata to Concrete Tasks
The authors introduce a taxonomy with target, functional role, and mode of justification axes plus a framework that decomposes abstract XAI desiderata into concrete benchmarkable tasks via identified dependency structures.
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