Interacting with Machines: Can an Artificially Intelligent Agent Be a Partner?

Abstract

In the past decade, the fields of machine learning and artificial intelligence (AI) have seen unprecedented developments that raise human-machine interactions (HMI) to the next level. Smart machines, i.e., machines endowed with artificially intelligent systems, have lost their character as mere instruments. This, at least, seems to be the case if one considers how humans experience their interactions with them. Smart machines are construed to serve complex functions involving increasing degrees of freedom, and they generate solutions not fully anticipated by humans. Consequently, their performances show a touch of action and even autonomy. HMI is therefore often described as a sort of “cooperation” rather than as a mere application of a tool. Some authors even go as far as subsuming cooperation with smart machines under the label of partnership, akin to cooperation between human agents sharing a common goal. In this paper, we explore how far the notion of shared agency and partnership can take us in our understanding of human interaction with smart machines. Discussing different topoi related to partnerships in general, we suggest that different kinds of “partnership” depending on the form of interaction between agents need to be kept apart. Building upon these discussions, we propose a tentative taxonomy of different kinds of HMI distinguishing coordination, collaboration, cooperation, and social partnership.

Postdigital Duoethnography: An Inquiry into Human-Artificial Intelligence Synergies

“…This is in line with the idea of the human mind as constantly being extended into the material world (M. Jones 2023). Therefore, it is possible for humans to produce valuable and original knowledge by using AI as a partner in a meaningful interaction (Schmidt and Loidolt 2023).…”

“Quasi-Metacognitive Machines: Why We Don’t Need Morally Trustworthy AI and Communicating Reliability is Enough”

“…As introduced above, many machine learning applications are already quasi-metacognitive in the manner described here, and so the demand of developing reliable AI is already met in many cases. What is left to understand is how to turn reliable AIs into reliable quasi-partners (only "quasi" because genuine partnership arguably requires joint responsibility; see Schmidt and Loidolt (2023) for a relevant taxonomy of partnerships), so that we might make sense of how it can be deployed to miti-1 3 gate the problem of vulnerability for all relevant stakeholders. As a baseline enabling condition for partnering with AI (however simulated), it helps to recognize that AI advice is often constitutive of the final decision, such that the final decision ought to be regarded as a species of collective decision and the AI regarded as a contributor to this decision.…”