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

By Qian Yang, Aaron Steinfeld, Carolyn Penstein Rosé, John Zimmerman. 2020

651 citations recorded by the source corpus.

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.

Subfields: Safety Research, Social Psychology, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety Research

1. Percy Liang
2. Michael S. Bernstein
3. Edward Cutrell

In Social Psychology

1. Percy Liang
2. Michael S. Bernstein

In Human-Computer Interaction

1. Carrie J. Cai
2. Cecily Morrison
3. Meredith Ringel Morris
4. Katja Hofmann
5. Percy Liang
6. Michael S. Bernstein
7. Edward Cutrell
8. Tomasz Bednarz

Citations in the corpus

• Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends (2021, methodology): “It is referenced widely in the HCI/ML literature, and authors have attempted to adapt it to cater to ML systems (Yang et al., 2020).”
• Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies (2021, neutral): “theory [91, 103] and affinity diagramming [14, 64, 148, 149].”
• Six Human-Centered Artificial Intelligence Grand Challenges (2023, neutral): “Recent research already reported that many challenges have been encountered in the process of developing HCAI systems (Yang et al., 2020).”
• Generative Agents: Interactive Simulacra of Human Behavior (2023, background): “However, in other application domains, it will be important to follow best practices in human-AI design [5, 107] to understand errors and how they might percolate into the user experience.”
• Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors (2021, background): “[59] discuss, it is difficult to specify an AI experience in advance of having a working prototype trained on data.”