'Put the Car on the Stand': SMT-based Oracles for Investigating Decisions

'Put the Car on the Stand': SMT-based Oracles for Investigating Decisions
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“将汽车搁置”:基于 SMT 的预言机用于调查决策

DOI:
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发表时间:
2023
期刊:
arXiv.org
影响因子:
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通讯作者:
R. Piskac
R. Piskac
中科院分区:
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文献类型:
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作者:
Samuel Judson;Matthew Elacqua;Filip Cano Córdoba;Timos Antonopoulos;Bettina Könighofer;Scott J. Shapiro;R. Piskac

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在危害后,有原则的问责制对于算法决策的值得信赖的设计和治理至关重要。法律理论提供了一种评估罪魁祸首的至关重要方法:将代理人“在看台上”,以对其行为和意图进行盘问。我们表明,在最少的假设下,自动推理可以像在法律事实发现的对抗过程一样严格审问算法行为。我们将责任过程(例如试验或审查委员会)建模为反事实引导的逻辑探索和抽象改进(清晰)循环。我们使用正式的符号执行和满足性模量理论(SMT)的方法来解除有关人类研究者自适应提出的事实和反事实场景中的代理行为的查询。为了这样做,对于决策算法$ \ mathcal {a} $,我们使用符号执行将其逻辑表示为确定理论中的语句$ \ pi $,$ \ texttt {qf_fpbv} $。我们实施我们的框架,并在说明性的车祸场景中演示其实用性。
Principled accountability in the aftermath of harms is essential to the trustworthy design and governance of algorithmic decision making. Legal theory offers a paramount method for assessing culpability: putting the agent 'on the stand' to subject their actions and intentions to cross-examination. We show that under minimal assumptions automated reasoning can rigorously interrogate algorithmic behaviors as in the adversarial process of legal fact finding. We model accountability processes, such as trials or review boards, as Counterfactual-Guided Logic Exploration and Abstraction Refinement (CLEAR) loops. We use the formal methods of symbolic execution and satisfiability modulo theories (SMT) solving to discharge queries about agent behavior in factual and counterfactual scenarios, as adaptively formulated by a human investigator. In order to do so, for a decision algorithm $\mathcal{A}$ we use symbolic execution to represent its logic as a statement $\Pi$ in the decidable theory $\texttt{QF_FPBV}$. We implement our framework and demonstrate its utility on an illustrative car crash scenario.
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