Collaborative Research: RI: Small: Modeling and Learning Ethical Principles for Embedding into Group Decision Support Systems

协作研究：RI：小型：建模和学习嵌入群体决策支持系统的道德原则

基本信息

批准号：
2008011
负责人：
Kristen Venable
金额：
$ 16.64万
依托单位：
Florida Institute for Human and Machine Cognition, Inc.
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008011&HistoricalAwards=false
关键词：
Collaborative Research RI Small Modeling

项目摘要

Many settings in everyday life require making decisions by combining the subjective preferences of individuals in a group, such as where to go to eat, where to go on vacation, whom to hire, which ideas to fund, or what route to take. In many domains, these subjective preferences are combined with moral values, ethical principles, or business constraints that are applicable to the decision scenario and are often prioritized over the preferences. The potential conflict of moral values with subjective preferences are keenly felt both when AI systems recommend products to us and when we use AI enabled systems to make group decisions. This research seeks to make AI more accountable by providing mechanisms to bound the decisions that AI systems can make, ensuring that the outcomes of the group decision making process aligns with human values. To achieve the goal of building ethically-bounded, AI-enabled group decision making systems, this project takes inspiration from humans, who often constrain their decisions and actions according to a number of exogenous priorities coming from moral, ethical, or business values. This research project will address the current lack of principled, formal approaches for embedding ethics into AI agents and AI enabled group decision support systems by advancing the state of the art in the safety and robustness of AI agents which, given how broadly AI touches our daily lives, will have broad impact and benefit to society.Specifically, the long-term goal of this project is to establish mathematical and machine learning foundations for embedding ethical guidelines into AI for group decision-making systems. Within the machine ethics field there are two main approaches: the bottom-up approach focused on data-driven machine learning techniques and the top-down approach following symbolic and logic-based formalisms. This project brings these two methodologies closer together through three specific aims. (1) Modeling and Evaluating Ethical Principles: this project will extend principles in social choice theory and fair division using preference models from the literature on knowledge representation and preference reasoning. (2) Learning Ethical Principles From Data: this project will develop novel machine-learning frameworks to learn individual ethical principles and then aggregate them for use in group decision making systems. And finally, (3) Embedding Ethical Principles into Group Decision Support Systems: this project will develop novel frameworks for designing AI-based mechanisms for ethical group decision-making. This research will establish novel methods for the formal and experimental unification of aspects of the top-down or rule-based approach with the bottoms-up or data-based approach for embedding ethics into group decision making systems. The project will also formalize a framework for ethical and constrained reasoning across teams of computational agents.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.

日常生活中的许多环境都需要通过将一个人的主观偏好结合在一个小组中的主观偏好，例如去哪里吃饭，去哪里去度假，要雇用哪些想法或采取什么途径。在许多领域中，这些主观偏好与适用于决策情景的道德价值观，道德原则或业务限制结合在一起，并且通常优先于偏好。当AI系统向我们推荐产品以及使用AI启用AI的系统来做出小组决策时，道德价值与主观偏好的潜在冲突既敏锐地感受到。这项研究试图通过提供机制来限制AI系统可以做出的决策，从而确保小组决策过程与人类价值观保持一致，以使AI更加负责。为了实现建立具有道德支持的，支持AI的团体决策系统的目标，该项目从人类那里汲取灵感，他们经常根据道德，道德或业务价值来限制其决策和行动。该研究项目将通过在AI代理中嵌入道德的原则性，正式方法，并通过在AI代理的安全性和鲁棒性方面推进最新技术来实现AI的决策支持系统，鉴于AI的宽广触及了我们的日常日常活动。生命，将对社会产生广泛的影响和利益。特别是，该项目的长期目标是建立数学和机器学习基础，以将道德准则嵌入AI中以进行小组决策系统。在机器伦理领域中，有两种主要方法：自下而上的方法集中在数据驱动的机器学习技术以及基于象征性和逻辑的形式主义之后的自上而下方法。该项目通过三个特定目的将这两种方法更加接近。（1）建模和评估道德原则：该项目将使用有关知识表示和偏好推理的文献中的偏好模型扩展社会选择理论和公平分裂的原则。（2）从数据中学习道德原则：该项目将开发新颖的机器学习框架来学习个人的道德原则，然后将其汇总用于小组决策系统。最后，（3）将道德原则嵌入小组决策支持系统中：该项目将开发用于设计基于AI的道德团体决策机制的新颖框架。这项研究将建立新的方法，用于对自上而下或基于规则的方法的形式和实验统一，并采用基于自下而上或数据的方法将伦理嵌入到小组决策系统中的方法。该项目还将为跨计算代理团队的道德和受约束推理的框架形式化。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。