论文标题

基于出处的多代理信息分析的解释

Provenance-Based Interpretation of Multi-Agent Information Analysis

论文作者

Friedman, Scott, Rye, Jeff, LaVergne, David, Thomsen, Dan, Allen, Matthew, Tunis, Kyle

论文摘要

分析软件工具和工作流的能力,复杂性,数量和规模正在增加,工作流的完整性一如既往地重要。具体而言,我们必须能够检查分析工作流程的过程,以评估(1)结论的信心,(2)所涉及的操作的风险和偏见,(3)结论对来源和代理的结论的敏感性,(4)各种来源和代理的影响和相关性,以及支持结论的来源的多样性。我们提出了一种方法,该方法与代理商的宣传和代理人的评估和证据联系(在我们的小说潜水本体论中表达)一起跟踪代理人的出处。 Prov-O和潜水共同实现了信心和反事实反驳的动态传播,以改善人机信任和分析完整性。我们展示了为与该出处的用户互动开发的代表性软件,并讨论了采用此类方法的组织的关键需求。我们使用基于交互式Web的信息验证UI在多代理分析方案中演示了所有这些评估。

Analytic software tools and workflows are increasing in capability, complexity, number, and scale, and the integrity of our workflows is as important as ever. Specifically, we must be able to inspect the process of analytic workflows to assess (1) confidence of the conclusions, (2) risks and biases of the operations involved, (3) sensitivity of the conclusions to sources and agents, (4) impact and pertinence of various sources and agents, and (5) diversity of the sources that support the conclusions. We present an approach that tracks agents' provenance with PROV-O in conjunction with agents' appraisals and evidence links (expressed in our novel DIVE ontology). Together, PROV-O and DIVE enable dynamic propagation of confidence and counter-factual refutation to improve human-machine trust and analytic integrity. We demonstrate representative software developed for user interaction with that provenance, and discuss key needs for organizations adopting such approaches. We demonstrate all of these assessments in a multi-agent analysis scenario, using an interactive web-based information validation UI.

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