Collaborative Research: DASS: Policy Design for Holding AI-Supported Systems Accountable

随着社会越来越依赖软件系统，如何负责任地开发此类软件的问题也随之出现。在软件系统的设计中，灵活性和创新必须与保护公众相平衡。设计政策以管理软件对社会影响的监管机构通常很难获得明确的指导，以使软件系统负责并跟上新技术的发展，例如人工智能（AI）的使用增加。软件工程师经常被模糊的指导和没有可衡量的目标所困扰，比如欧盟的“解释权”。虽然澄清指导或法院判决可能最终提供更多可操作的细节，但政策实施是一个缓慢的反应过程，可能会延迟改进的采用。为了协调软件和政策设计，并为负责的软件系统构建积极的政策框架，需要更多关注可解释性，安全性和公平性等质量的指导。本项目将研究政策设计的跨领域原则，特别是在AI支持的系统中，为软件工程师设定相应的义务。结果将有助于利益攸关方有意识地设计政策，为特定领域提供基于证据的指导。该项目将促进政策制定者和软件工程师之间的互动，以了解形成政策目标的能力和社会期望，调查政策维度和所涉及的具体权衡，并将政策与特定的质量保证义务联系起来，这些义务可用于涵盖系统中人工智能和非人工智能部分的监管评估。具体而言，该项目将首先调查政策设计的重要方面（例如，监管的严格性、证据水平、政策的特殊性），使用利益相关者访谈、历史分析和技术分析。然后，它将通过几个实验来评估设计决策如何影响结果，如何相互作用，以及如何导致权衡-这些将被编码和验证为策略设计模式。本研究将结合联合收割机多种研究方法，包括访谈、文献分析、对照实验、原型设计和作者工作坊。将政策设计和监管的社会科学研究与系统设计和质量保证的软件工程研究深度融合。该研究将通过与监管机构的接触在多个社区广泛传播。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

As society is becoming more and more dependent on software systems, questions arise about how such software can be developed responsibly. In the design of software systems, flexibility and innovation must be balanced with safeguarding the public. Regulators who design policy to steward software’s effect on society often have a hard time capturing clear guidance to hold software systems accountable and keeping up with new technologies, such as the increased use of artificial intelligence (AI). Software engineers have often been left with vague guidance and no measurable goals, such as the EU’s “right to explanation.” While clarifying guidance or court decisions may eventually provide more actionable details, policy implementation is a slow, reactive process that can delay adoption of improvements. To reconcile software and policy design and construct a proactive policy framework for accountable software systems, guidance for qualities such as explainability, safety, and fairness requires more attention.This project will investigate cross-domain principles for policy design, setting corresponding obligations for software engineers, particularly in the context of AI-supported systems. Results will help stakeholders to deliberately design policy with evidence-based guidance for a specific domain. The project will facilitate interactions between policy makers and software engineers in understanding capabilities and societal expectations toward forming policy goals, investigate policy dimensions and the concrete tradeoffs involved, and connect the policy to specific quality-assurance obligations that could be used in a regulatory evaluation covering both AI and non-AI parts of the system. Specifically, the project will first investigate the important dimensions for policy design (e.g., strictness of regulation, level of evidence, policy specificity) using stakeholder interviews, historical analysis, and technical analysis. It then will evaluate, with several experiments, how design decisions affect outcomes, interact with each other, and result in tradeoffs -- which will be encoded and validated as policy design patterns. The research will combine multiple research methods, including interviews, literature and document analyses, controlled experiments, prototyping, and writer’s workshops. It will deeply integrate social-science research on policy design and regulation with software-engineering research on system design and quality assurance. The research will be disseminated broadly across multiple communities and through engagement with regulators. Educational material will be developed for multiple courses and shared publicly.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.