Specification Overfitting in Artificial Intelligence
arxiv(2024)
摘要
Machine learning (ML) and artificial intelligence (AI) approaches are often
criticized for their inherent bias and for their lack of control,
accountability, and transparency. Consequently, regulatory bodies struggle with
containing this technology's potential negative side effects. High-level
requirements such as fairness and robustness need to be formalized into
concrete specification metrics, imperfect proxies that capture isolated aspects
of the underlying requirements. Given possible trade-offs between different
metrics and their vulnerability to over-optimization, integrating specification
metrics in system development processes is not trivial. This paper defines
specification overfitting, a scenario where systems focus excessively on
specified metrics to the detriment of high-level requirements and task
performance. We present an extensive literature survey to categorize how
researchers propose, measure, and optimize specification metrics in several AI
fields (e.g., natural language processing, computer vision, reinforcement
learning). Using a keyword-based search on papers from major AI conferences and
journals between 2018 and mid-2023, we identify and analyze 74 papers that
propose or optimize specification metrics. We find that although most papers
implicitly address specification overfitting (e.g., by reporting more than one
specification metric), they rarely discuss which role specification metrics
should play in system development or explicitly define the scope and
assumptions behind metric formulations.
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