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Building trustworthy autonomous systems is challenging for many reasonsbeyond simply trying to engineer agents that 'always do the right thing.' Thereis a broader context that is often not considered within AI and HRI: that theproblem of trustworthiness is inherently socio-technical and ultimatelyinvolves a broad set of complex human factors and multidimensionalrelationships that can arise between agents, humans, organizations, and evengovernments and legal institutions, each with their own understanding anddefinitions of trust. This complexity presents a significant barrier to thedevelopment of trustworthy AI and HRI systems---while systems developers maydesire to have their systems 'always do the right thing,' they generally lackthe practical tools and expertise in law, regulation, policy and ethics toensure this outcome. In this paper, we emphasize the "fuzzy" socio-technicalaspects of trustworthiness and the need for their careful consideration duringboth design and deployment. We hope to contribute to the discussion oftrustworthy engineering in AI and HRI by i) describing the policy landscapethat must be considered when addressing trustworthy computing and the need forusable trust models, ii) highlighting an opportunity for trustworthy-by-designintervention within the systems engineering process, and iii) introducing theconcept of a "policy-as-a-service" (PaaS) framework that can be readily appliedby AI systems engineers to address the fuzzy problem of trust during thedevelopment and (eventually) runtime process. We envision that the PaaSapproach, which offloads the development of policy design parameters andmaintenance of policy standards to policy experts, will enable runtime trustcapabilities intelligent systems in the wild.

Towards a Policy-as-a-Service Framework to Enable Compliant, Trustworthy AI and HRI Systems in the Wild