The need to move away from agential-AI: Empirical investigations, useful concepts and open issues. Issue 155 (November 2021)

• Record Type: Journal Article
• Authors: Cabitza, Federico; Campagner, Andrea; Simone, Carla
• Is Part Of: International journal of human-computer studies. Issue 155(2021)
• Publication Date: 2021-11
• Subjects: Machine learning -- Intelligent systems -- Artificial intelligence -- Knowledge artifact -- Ba; AI Artificial Intelligence -- CAWI computer assited web interview -- CSCW Computer Supported Cooperative Work -- DSS: decision support system -- ECG electrocardiogram -- HAII Human Interaction with Artificial Intelligence -- HCI Human-Computer Interaction -- IT Information Technology -- KA Knowledge Artifact -- ML Machine Learning -- RfS Request for Service -- XAI Explainable Artificial Intelligence; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2021.102696
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: We propose a novel approach to human interaction with artificial intelligence (HAII). Through 4 case studies we highlight the shortcomings of the mainstream HAII paradigm. We describe a novel perspective on HAII based on Computer-Supported Cooperative Work. We describe functions that AI should embed to support collaborative decision making. Abstract: We propose a novel approach to human interaction with artificial intelligence systems (HAII), alternative to the mainstream dyadic one where humans and AI are seen as interacting agents. Through two quantitative experiments and two qualitative in-field case studies, we show that the mainstream HAII paradigm presents potentially harmful design shortcomings as it can trigger negative dynamics such as automation bias and prejudices. Our proposal, on the other hand, is grounded in the Computer-Supported Cooperative Work literature, in which AI can be conceived as a component of a Knowledge Artifact (KA). This consists of an ecosystem of knowledge creation tools whose goal is to support a Ba (after Nonaka), i.e. a collective of competent decision makers. We highlight the cooperative nature of decision making and the AI functionalities that a KA should embed. These include eXplainable AI solutions, aimed at facilitating appropriation, but also functionalities that enable reasoning in a collaborative setting. Finally, we discuss how moving intelligence and agency from individual agents to the human collective can help to mitigate the shortcomings of dyadic HAII (e.g., deskilling), re-distribute responsibility in critical tasks, and revisit the HAII research agenda to align it with the needs of increasingly wide, heterogeneous and complex teams.