What is new with Artificial Intelligence? Human–agent interactions through the lens of social agency

Abstract

In this article, we suggest that the study of social interactions and the development of a “sense of agency” in joint action can help determine the content of relevant explanations to be implemented in artificial systems to make them “explainable.” The introduction of automated systems, and more broadly of Artificial Intelligence (AI), into many domains has profoundly changed the nature of human activity, as well as the subjective experience that agents have of their own actions and their consequences – an experience that is commonly referred to as sense of agency. We propose to examine the empirical evidence supporting this impact of automation on individuals’ sense of agency, and hence on measures as diverse as operator performance, system explicability and acceptability. Because of some of its key characteristics, AI occupies a special status in the artificial systems landscape. We suggest that this status prompts us to reconsider human–AI interactions in the light of human–human relations. We approach the study of joint actions in human social interactions to deduce what key features are necessary for the development of a reliable sense of agency in a social context and suggest that such framework can help define what constitutes a good explanation. Finally, we propose possible directions to improve human–AI interactions and, in particular, to restore the sense of agency of human operators, improve their confidence in the decisions made by artificial agents, and increase the acceptability of such agents.

Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis

“…48 Therefore, by adding the XAI technology to ML and DL models, the use of AI in healthcare will become more reliable and acceptable. [49][50][51] In addition, before application, the public should be educated on the principles of the medical AI system, including how it works.…”

“…For example, in a study involving categorising tuberculosis diagnoses through deep learning chest radiographs, researchers used heat maps to show areas of increased activation of deep learning networks that could be inferred to be important for diagnosis 48. Therefore, by adding the XAI technology to ML and DL models, the use of AI in healthcare will become more reliable and acceptable 49–51. In addition, before application, the public should be educated on the principles of the medical AI system, including how it works.…”

Should Artificial Intelligent Agents be Your Co-author? Arguments in Favour, Informed by ChatGPT

“…For example, in the health domain AI is as good or better at identifying anomalies in some medical imaging than humans, such as detecting pneumonia via chest X-rays, diagnosing tuberculosis via radiography, or accurately classifying breast cancer in mammograms (Coppola et al, 2021; Jha et al, 2022; Khamparia et al, 2021). Researchers have argued that at present ‘AI has a certain degree of decision-making autonomy’ (Pagliari et al, 2022, p. 13), suggesting that it could create knowledge independently. Indeed, ‘some believe that AI will soon be inventing and creating things in ways that make it impossible to identify the human intellectual input in the final invention or work.…”