Responsible Human-Centered Artificial Intelligence for the Cognitive Enhancement of Knowledge Workers

Abstract

AbstractOver the past decade, the demand for high-performing knowledge workers (KWs) has grown at an unprecedented rate and shows no signs of slowing. Researchers, designers, engineers, and executives are examples of KWs that perform non-routine, creative work. The work outcomes of KWs as individuals, teams, and organizations play a vital role in the global economy and quality of life. One of the most significant challenges KWs face is balancing stressors on their cognitive and emotional well-being while seeking high productivity. Human cognitive enhancement proposes improving human abilities to acquire and generate knowledge and understand the world. Our cognitive enhancement application for KWs, called the Flow Choice Architecture (FCA), senses their cognitive and affective states, adds context, and recommends appropriate nudges to maximize their healthy flow time. This study provides insights into how FCA implements Human-Centered Design and Responsible Artificial Intelligence (RAI) principles as an interactive AI-powered application that promotes healthy flow performance during knowledge work. FCA applied the RAI tools from Microsoft’s Human-AI eXperience Toolkit to evaluate FCA-specific scenarios. By defining FCA as a hybrid recommendation system and conversational AI agent, we found the following categories of human-AI failure scenarios in FCA: input errors, trigger errors, delimiter errors, and response generation errors. We recommend simulating these errors and undesirable behaviors to improve the design of explainable nudges, meaningful metrics, and well-tuned triggers. The outcome of this RAI evaluation was a robust FCA system design that meets the needs of KWs and enhances their capability to thrive and flourish at work. KeywordsKnowledge workerCognitive enhancementResponsible artificial intelligence