Two Heads Are Better Than One: A Dimension Space for Unifying Human and Artificial Intelligence in Shared Control

Cimolino, Gabriele; Graham, T. C. Nicholas

doi:10.1145/3491102.3517610

Cited by 9 publications

(3 citation statements)

References 60 publications

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“…Another mitigating measure may involve having a partner during monitoring tasks, whose presence alone would theoretically halve the rate of inattentional blindness. In any case, designing interactions will rely on an accurate definition of the shared control dimensions in which a human and AI partner collaboratively control a system [35]. While many such collaborative systems exist (e.g., digital games, telerobotics, surgery, lane-keeping assisted driving), remotely operated and autonomous vessels are distinct in that they place humans in a safety-critical, remotely supervisory role.…”

Section: Discussionmentioning

confidence: 99%

Gorillas on a boat: Inattentional blindness during supervisory control of autonomous vessels

Veitch,

Alsos

2023

J. Phys.: Conf. Ser.

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When focused on a task, unexpected visual stimuli can go by unnoticed. Inattentional blindness may be problematic for supervisory controllers of autonomous vessels, because this role specifically relies on identifying unexpected stimuli in case intervention is needed. In a simulation-based experiment (n=32), we show that 50% of participants did not perceive an unexpected visual stimulus (a gorilla passenger) when assigned the task of supervisory controller for an autonomous ferry. Additionally, eye-tracking showed that 12 of the 16 players who did not report the gorilla in fact gazed directly at it. Results also showed no correlation to skillset (gamer or navigator) nor to vigilance (5- or 30-minute scenarios); they did, however, show positive correlation to multitasking (1 or 3 supervised vessels). We discuss implications for designing work tasks for supervisory control of remotely operated and autonomous vessels.

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Section: Discussionmentioning

confidence: 99%

Gorillas on a boat: Inattentional blindness during supervisory control of autonomous vessels

Veitch,

Alsos

2023

J. Phys.: Conf. Ser.

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“…Thus, it is important to find the appropriate method to deliver explanations to users. Generally, there are two approaches, manualtrigger (i.e., initiated by users) and auto-trigger (i.e., initiated by the system) [57,235]. On the one hand, researchers have found that explanations should not always be presented to users, because they can introduce unnecessary cognitive load and become overwhelming for non-expert end-users [41,51,183,205,215].…”

Section: When To Explain?mentioning

confidence: 99%

XAIR: A Framework of Explainable AI in Augmented Reality

Yu²,

Jonker

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses when, what, and how to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR's utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.

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“…To effectively orchestrate human and AI work, researchers need to understand the respective roles of human and AI, and how they will interact with one another, because it will directly affect whether humans decide to take AI advice (Cimolino and Graham, 2022). Usually, if AI aims to assist high-stake decision-making tasks, such as recidivism prediction (Veale, Van Kleek and Binns, 2018) and medical treatments (Cai, Winter, Steiner, Wilcox and Terry, 2019), considerations of risk and trust will be important factors for people to adopt such AI assistants (Lai, Chen, Liao, Smith-Renner and Tan, 2021).…”

Section: Distributing Work Between Human and Ai For Mixed-initiative ...mentioning

confidence: 99%

The State of Human-centered NLP Technology for Fact-checking

Das,

Liu,

Kovatchev

et al. 2023

Preprint

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Misinformation threatens modern society by promoting distrust in science, changing narratives in public health, heightening social polarization, and disrupting democratic elections and financial markets, among a myriad of other societal harms. To address this, a growing cadre of professional fact-checkers and journalists provide high-quality investigations into purported facts. However, these largely manual efforts have struggled to match the enormous scale of the problem. In response, a growing body of Natural Language Processing (NLP) technologies have been proposed for more scalable fact-checking. Despite tremendous growth in such research, however, practical adoption of NLP technologies for fact-checking still remains in its infancy today.In this work, we review the capabilities and limitations of the current NLP technologies for fact-checking. Our particular focus is to further chart the design space for how these technologies can be harnessed and refined in order to better meet the needs of human fact-checkers. To do so, we review key aspects of NLP-based fact-checking: task formulation, dataset construction, modeling, and human-centered strategies, such as explainable models and human-in-the-loop approaches. Next, we review the efficacy of applying NLP-based fact-checking tools to assist human fact-checkers. We recommend that future research include collaboration with fact-checker stakeholders early on in NLP research, as well as incorporation of human-centered design practices in model development, in order to further guide technology development for human use and practical adoption. Finally, we advocate for more research on benchmark development supporting extrinsic evaluation of human-centered fact-checking technologies.

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Two Heads Are Better Than One: A Dimension Space for Unifying Human and Artificial Intelligence in Shared Control

Cited by 9 publications

References 60 publications

Gorillas on a boat: Inattentional blindness during supervisory control of autonomous vessels

Gorillas on a boat: Inattentional blindness during supervisory control of autonomous vessels

XAIR: A Framework of Explainable AI in Augmented Reality

The State of Human-centered NLP Technology for Fact-checking

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