Increasing Transparency to Design Inclusive Conversational Agents (CAs): Perspectives and Open Issues
Isabela Motta, Manuela Quaresma · 2023
Artificial Intelligence (AI)-based Conversational Agents (CAs) have a great potential to include marginalized and vulnerable populations. However, some issues still make these interfaces exclusive for some users. This paper proposes to discuss how increasing CAs’ transparency can contribute to these systems’ inclusiveness and indicates open issues that must be addressed to make AI-based CAs more transparent and inclusive. We argue that adding more guidance to users on how CAs work, what they can do, and how they may be operated might alleviate older adults’ misperceptions about functioning and privacy that hamper CAs’ adoption, facilitate its usage for people with impairments, and help identify possible prejudicial biases. As challenges, researchers and practitioners should investigate how to determine appropriate levels of transparency through personalization, produce human-centered knowledge on transparency, and study new methods, tools, and processes to support CA development that considers inclusiveness.