My Future with My Chatbot A Scenario-Driven, User-Centric Approach to Anticipating AI Impacts

Open Access
Authors
Publication date 2024
Book title ACM FAccT '24
Book subtitle Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency : June 3rd-6th 2024, Rio de Janeiro, Brazil
ISBN (electronic)
  • 9798400704505
Event 2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2024
Pages (from-to) 2071-2085
Number of pages 15
Publisher New York: The Association for Computing Machinery
Organisations
  • Faculty of Law (FdR) - Institute for Information Law (IViR)
Abstract

As a general purpose technology without a concrete pre-defined purpose, personal chatbots can be used for a whole range of objectives, depending on the personal needs, contexts, and tasks of an individual, and so potentially impact a variety of values, people, and social contexts. Traditional methods of risk assessment are confronted with several challenges: the lack of a clearly defined technology purpose, the lack of clearly defined values to orient on, the heterogeneity of uses, and the difficulty of actively engaging citizens themselves in anticipating impacts from the perspective of their individual lived realities. In this article, we leverage scenario writing at scale as a method for anticipating AI impact that is responsive to these challenges. The advantages of the scenario method are its ability to engage individual users and stimulate them to consider how chatbots are likely to affect their reality and so collect different impact scenarios depending on the cultural and societal embedding of a heterogeneous citizenship. Empirically, we tasked 106 US-based participants to write short fictional stories about the future impact (whether desirable or undesirable) of AI-based personal chatbots on individuals and society and, in addition, ask respondents to explain why these impacts are important and how they relate to their values. In the analysis process, we map those impacts and analyze them in relation to socio-demographic as well as AI-related attitudes of the scenario writers. We show that our method is effective in (1) identifying and mapping desirable and undesirable impacts of AI-based personal chatbots, (2) setting these impacts in relation to values that are important for individuals, and (3) detecting socio-demographic and AI-attitude related differences of impact anticipation.

Document type Conference contribution
Language English
Published at https://doi.org/10.1145/3630106.3659026
Other links https://www.scopus.com/pages/publications/85196626745
Downloads
3630106.3659026 (Final published version)
Permalink to this page
Back