The Model Mastery Lifecycle: A Framework for Designing Human-AI Interaction

• URL: http://arxiv.org/abs/2408.12781v1
• Date: Fri, 23 Aug 2024 01:00:32 GMT
• Title: The Model Mastery Lifecycle: A Framework for Designing Human-AI Interaction
• Authors: Mark Chignell, Mu-Huan Miles Chung, Jaturong Kongmanee, Khilan Jerath, Abhay Raman,
• Abstract summary: The utilization of AI in an increasing number of fields is the latest iteration of a long process. There is an urgent need for methods to determine how AI should be used in different situations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The utilization of AI in an increasing number of fields is the latest iteration of a long process, where machines and systems have been replacing humans, or changing the roles that they play, in various tasks. Although humans are often resistant to technological innovation, especially in workplaces, there is a general trend towards increasing automation, and more recently, AI. AI is now capable of carrying out, or assisting with, many tasks that used to be regarded as exclusively requiring human expertise. In this paper we consider the case of tasks that could be performed either by human experts or by AI and locate them on a continuum running from exclusively human task performance at one end to AI autonomy on the other, with a variety of forms of human-AI interaction between those extremes. Implementation of AI is constrained by the context of the systems and workflows that it will be embedded within. There is an urgent need for methods to determine how AI should be used in different situations and to develop appropriate methods of human-AI interaction so that humans and AI can work together effectively to perform tasks. In response to the evolving landscape of AI progress and increasing mastery, we introduce an AI Mastery Lifecycle framework and discuss its implications for human-AI interaction. The framework provides guidance on human-AI task allocation and how human-AI interfaces need to adapt to improvements in AI task performance over time. Within the framework we identify a zone of uncertainty where the issues of human-AI task allocation and user interface design are likely to be most challenging.

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