Machine-learning models are becoming increasingly prevalent in our lives, for instance assisting in image-classification or decision-making tasks. Consequently, the reliability of these models is of critical importance and has resulted in the development of numerous approaches for validating and verifying their robustness and fairness. However, beyond such specific properties, it is challenging to specify, let alone check, general functional-correctness expectations from models. In this paper, we take inspiration from specifications used in formal methods, expressing functional-correctness properties by reasoning about $k$ different executions, so-called $k$-safety properties. Considering a credit-screening model of a bank, the expected property that "if a person is denied a loan and their income decreases, they should still be denied the loan" is a 2-safety property. Here, we show the wide applicability of $k$-safety properties for machine-learning models and present the first specification language for expressing them. We also operationalize the language in a framework for automatically validating such properties using metamorphic testing. Our experiments show that our framework is effective in identifying property violations, and that detected bugs could be used to train better models.
翻译:机器学习模式在我们的生活中越来越普遍,例如协助图像分类或决策任务。因此,这些模式的可靠性至关重要,并导致制定了许多验证和核实其稳健性和公正性的方法。然而,除了这些具体特性之外,我们很难具体说明,更不用说检查,模型对功能纠正的一般期望。在本文中,我们从正式方法中所使用的规格中汲取灵感,通过推理不同处决的金额(即所谓的美元安全性能)来表达功能更正性。考虑到银行的信用筛选模式,预期“如果一个人得不到贷款和收入减少,他们仍应该被剥夺贷款”的财产是2安全性财产。这里我们展示了美元安全性能对于机器学习模式的广泛适用性,并提出了表达这些特性的第一个规格语言。我们还在使用变形测试自动验证这些特性的框架中运用了语言。我们的实验表明,我们的框架在查明侵犯财产行为方面是有效的,被检测到的错误可以用来培训更好的模型。