Trustworthy Human-Centered Automation Through Explainable AI and High-Fidelity Simulation

Hayes, Bradley; Moniz, Michael

doi:10.1007/978-3-030-51064-0_1

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…Computer scientists are focusing on developing algorithmic transparency and glass-box machine learning models (Abdul et al, 2018 ; Hayes and Moniz, 2021 ). On the other hand, researchers have been proposing design guidelines and considerations to build explainable interfaces and increase AI literacy among users (Amershi et al, 2019 ; Liao et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, studies have shown that digital assistants influence users' perceived control during an interaction, often resulting in disappointment with the online recommendation (André et al, 2018) and experience a psychological reactance when they perceive that their freedom is reduced (Lee and Lee, 2009). Multiple studies have also explored how AI impacts trust in machines and have provided approaches to repair trust in human-machine interactions (de Visser et al, 2018;Kulms and Kopp, 2019;Hayes and Moniz, 2021). Yet, there is a lack of a comprehensive look at autonomy perception or reactance encompassing various factors such as trust, control, freedom of choice, and decision-making support.…”

Section: Understanding Autonomy and Reactancementioning

confidence: 99%

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Exploring Peoples' Perception of Autonomy and Reactance in Everyday AI Interactions

et al. 2021

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Applications using Artificial Intelligence (AI) have become commonplace and embedded in our daily lives. Much of our communication has transitioned from human–human interaction to human–technology or technology-mediated interaction. As technology is handed over control and streamlines choices and decision-making in different contexts, people are increasingly concerned about a potential threat to their autonomy. In this paper, we explore autonomy perception when interacting with AI-based applications in everyday contexts using a design fiction-based survey with 328 participants. We probed if providing users with explanations on “why” an application made certain choices or decisions influenced their perception of autonomy or reactance regarding the interaction with the applications. We also looked at changes in perception when users are aware of AI's presence in an application. In the social media context, we found that people perceived a greater reactance and lower sense of autonomy perhaps owing to the personal and identity-sensitive nature of the application context. Providing explanations on “why” in the navigation context, contributed to enhancing their autonomy perception, and reducing reactance since it influenced the users' subsequent actions based on the recommendation. We discuss our findings and the implications it has for the future development of everyday AI applications that respect human autonomy.

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Section: Discussionmentioning

confidence: 99%

Section: Understanding Autonomy and Reactancementioning

confidence: 99%

Exploring Peoples' Perception of Autonomy and Reactance in Everyday AI Interactions

et al. 2021

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“…The authors in [251] proposed guidelines for using XAI techniques and simulations using XR for secured humanrobot interactions. The authors suggested that the proliferation of high-fidelity VR-based simulation environments will result in the reduction of barriers in cataloging and performing postmortems in operations by robotics that may result in the characterization of more rigorous behavior of autonomous system behavior and promote the adaption of explainable techniques in their controllers.…”

Section: ) How Xai Can Helpmentioning

confidence: 99%

Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges

Wang,

Qureshi,

Miralles-Pechuán

et al. 2024

IEEE Open J. Commun. Soc.

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Around 2020, 5G began its commercialization journey, and discussions about the nextgeneration networks (such as 6G) emerged. Researchers predict that 6G networks will have higher bandwidth, coverage, reliability, energy efficiency, and lower latency, and will be an integrated "humancentric" network system powered by artificial intelligence (AI). This 6G network will lead to many real-time automated decisions, ranging from network resource allocation to collision avoidance for self-driving cars. However, there is a risk of losing control over decision-making due to the high-speed, data-intensive AI decision-making that may go beyond designers' and users' comprehension. To mitigate this risk, explainable AI (XAI) methods can be used to enhance the transparency of the black-box AI decisionmaking process. This paper surveys the application of XAI towards the upcoming 6G age, including 6G technologies (such as intelligent radio and zero-touch network management) and 6G use cases (such as industry 5.0). Additionally, the paper summarizes the lessons learned from recent attempts and outlines important research challenges in applying XAI for 6G use cases soon.

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“…Similarly, if 6G is enabled with XAI, the virtual assistants can provide accurate information to the customers. For instance, [203] proposed guidelines for using XAI techniques and simulations using XR for secured human-robots interactions.…”

Section: • Virtual Assistants For Dynamic Customer Experiencesmentioning

confidence: 99%

“…[173] Proposed a SHAP-based backpropagation deep explainer method, that produces a interpretable model for emergency control applications in smart grids. [203] Proposed guidelines for using XAI techniques and simulations using XR for secured human-robots interactions. [233] Maximizing Explainability with SF-Lasso and Selective Inference for Video and Picture Ads [234] Attentive capsule network for click-through rate and conversion rate prediction in online advertising [212] Proposed an XAI solution using DL and Semantic Web technologies for flood monitoring [208] Proposed a SHAP-based method to interpret the outputs of a multilayer perceptron for buildingdamage assessment.…”

Section: H Summary Of the Xai Impact On 6g Applications And Technical...mentioning

confidence: 99%

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Wang¹,

Qureshi²,

Miralles-Pechuaán³

et al. 2021

Preprint

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When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG) 1 , promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.

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Trustworthy Human-Centered Automation Through Explainable AI and High-Fidelity Simulation

Cited by 4 publications

References 12 publications

Exploring Peoples' Perception of Autonomy and Reactance in Everyday AI Interactions

Exploring Peoples' Perception of Autonomy and Reactance in Everyday AI Interactions

Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

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