CHS: Medium: Collaborative Research: Charting a Research Agenda in Artificial Intelligence - Mediated Communication

Stanford University, Stanford CA

Investigators

Abstract

Artificial Intelligence (AI) algorithms are increasingly augmenting interpersonal communication. What used to be Computer-Mediated Communication (CMC) increasingly involves AI-Mediated Communication (AI-MC): interpersonal communication not simply transmitted by technology but augmented --- or even generated --- by algorithms to achieve specific outcomes. While some simple forms of AI-MC are already prevalent, recent advances in Natural Language Processing provide new directions for augmenting communication online by, for example, modifying texts to include more formal language or enhancing resumes to make them more professional. The advances are not limited to text: increasingly, photos and videos can be automatically manipulated with AI, leading to deep fakes, in which people are shown to act or behave in ways that they never did. Indeed, if a communication is mediated, AI can potentially modify, augment, or even generate the message. AI-MC is therefore likely to have a profound effect on how we communicate, greatly complicating our understanding of technology-mediated human interactions. The project will inform the development of systems that implement AI-mediated communication in a socially desirable and ethically responsible manner. The technical objectives of this project are to develop a framework that charts how AI-MC will impact cyber-human systems research and inform the design of AI-MC technologies. The project will provide some of the first investigations in key areas of AI-MC: 1) the design and perception of AI-MC systems; 2) the potential impact of AI-MC on communication dynamics; 3) the impact of AI-MC on social-psychological dynamics of online self-presentation, with a focus on impression formation and trust, including malicious contexts; and 4) understanding ethical concerns and opportunities around issues like bias, manipulation, and transparency in AI-MC technologies. These objectives will be accomplished through a series of novel empirical studies employing approaches including computational, behavioral, and qualitative methods. These activities will include online and lab experiments that examine behavioral processes and outcomes associated with various forms of AI-MC, the design and development of an AI-MC research platform, as well as a qualitative study of developers, engineers, and designers working with AI-MC systems. In addition, this project will build on the public attention and intrigue around AI to offer design and technology workshops to K-12 students in New York City public schools, using AI-MC to connect the ideas of AI to technologies students use in their everyday lives. 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.