Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design

Abstract

Artificial Intelligence (AI) plays an increasingly important role in improving HCI and user experience. Yet many challenges persist in designing and innovating valuable human-AI interactions. For example, AI systems can make unpredictable errors, and these errors damage UX and even lead to undesired societal impact. However, HCI routinely grapples with complex technologies and mitigates their unintended consequences. What makes AI different? What makes human-AI interaction appear particularly difficult to design? This paper investigates these questions. We synthesize prior research, our own design and research experience, and our observations when teaching human-AI interaction. We identify two sources of AI's distinctive design challenges: 1) uncertainty surrounding AI's capabilities, 2) AI's output complexity, spanning from simple to adaptive complex. We identify four levels of AI systems. On each level, designers encounter a different subset of the design challenges. We demonstrate how these findings reveal new insights for designers, researchers, and design tool makers in productively addressing the challenges of human-AI interaction going forward.

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

“…Long et al [ 205 ] attempts to define learner-centered AI and figure out design considerations. Yang et al [ 206 ] explore insights for designers and researchers to address challenges in human–AI interaction.…”

Rapid Assisted Visual Search

“…Traditional user-centered design methods are challenging to apply to human-AI interactions, partly due to the predictive component being central to the overall experience and a lack of viable low-fidelity prototyping methods to simulate realistic prediction behaviour [11,39]. Few works present high fidelity design concepts or user interface patterns that are explicitly useful over a more extensive range of human-AI interactive systems and associated tasks.…”

“…Successful human-AI interaction is, however, difficult to design due to the need to balance trade-offs between exploiting AI capabilities and mitigating its imperfections [39]. Designers report being hampered by a lack of design patterns, prototyping methods and examples of user interfaces matching AI capabilities to user value [11,37,38].…”

Brain-inspired model for decision-making in the selection of beneficial information patterns among signals received by an unpredictable information-development environment

“…To some extent, the occurred problem has been mitigated by executing artificial intelligence (AI) in industrial operations [1]. However, current AI systems are flawed by algorithm-complexity, unpredictable behaviors, and difficulty working at abstract levels [2,3]. They lack the ability to adapt to changing environments, generalize knowledge, and do causal reasoning [2,4].…”

“…However, current AI systems are flawed by algorithm-complexity, unpredictable behaviors, and difficulty working at abstract levels [2,3]. They lack the ability to adapt to changing environments, generalize knowledge, and do causal reasoning [2,4]. They cannot generalize acquired knowledge for any further changes in the environment that transcend their limitations [5].…”