SHAPE: A Framework for Evaluating the Ethicality of Influence

TL;DR

The paper introduces the SHAPE framework to evaluate the ethicality of influence exerted by agents, integrating philosophical insights with machine learning to guide ethical algorithm development and governance.

Contribution

It presents the SHAPE framework, connecting philosophical reasons for unethical influence with practical mechanisms for governing influential algorithms.

Findings

SHAPE identifies key ethical concerns in influence.

Framework guides ethical algorithm development.

Proposes governance mechanisms inspired by journalism and research.

Abstract

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.