Agents often exert influence when interacting with humans and non-human agents. However, the ethical status of such influence is often unclear. In this paper, we present the SHAPE framework, which lists reasons why influence may be unethical. We draw on literature from descriptive and moral philosophy and connect it to machine learning to help guide ethical considerations when developing algorithms with potential influence. Lastly, we explore mechanisms for governing algorithmic systems that influence people, inspired by mechanisms used in journalism, human subject research, and advertising.

翻译：智能体在与人类及非人类智能体交互时常常施加影响，然而此类影响的伦理地位往往不明确。本文提出SHAPE框架，系统列举影响可能违背伦理的缘由。我们借鉴描述性哲学与道德哲学文献，将其与机器学习相联系，以指导开发具有潜在影响的算法时的伦理考量。最后，受新闻业、人类受试者研究及广告领域机制的启发，我们探索了治理影响人类行为的算法系统的机制。