The Emerging Landscape of Explainable Automated Planning &amp; Decision Making

Chakraborti, Tathagata; Sreedharan, Sarath; Kambhampati, Subbarao

doi:10.24963/ijcai.2020/669

Cited by 68 publications

(52 citation statements)

References 40 publications

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“…This understanding can then support various goals for people to seek explanations, such as to assess AI capability, trustworthiness and fairness, to diagnose and improve the model, to better control or work with AI, and to discover new knowledge [8,39,44]. This wave of XAI work has focused dominantly on ML models, although explainbility of other types of AI systems such as planning [20], multi-agent systems [48], etc. are receiving increasing attention.…”

Section: Explainable Aimentioning

confidence: 99%

Questioning the AI: Informing Design Practices for Explainable AI User Experiences

Liao

Gruen

Miller

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

398

202

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A pervasive design issue of AI systems is their explainability-how to provide appropriate information to help users understand the AI.The technical field of explainable AI (XAI) has produced a rich toolbox of techniques. Designers are now tasked with the challenges of how to select the most suitable XAI techniques and translate them into UX solutions. Informed by our previous work studying design challenges around XAI UX, this work proposes a design process to tackle these challenges. We review our and related prior work to identify requirements that the process should fulfill, and accordingly, propose a Question-Driven Design Process that grounds the user needs, choices of XAI techniques, design, and evaluation of XAI UX all in the user questions. We provide a mapping guide between prototypical user questions and exemplars of XAI techniques, serving as boundary objects to support collaboration between designers and AI engineers. We demonstrate it with a use case of designing XAI for healthcare adverse events prediction, and discuss lessons learned for tackling design challenges of AI systems.CCS Concepts: • Human-centered computing → Interaction design process and methods; • Computing methodologies → Artificial intelligence.

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Section: Explainable Aimentioning

confidence: 99%

Questioning the AI: Informing Design Practices for Explainable AI User Experiences

Liao

Gruen

Miller

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

398

202

View full text Add to dashboard Cite

show abstract

“…Explainable planning can play a vital role in supporting users and improving their experiences when they interact with autonomous systems in complex decision-making procedures [131]. According to [132], depending on the stakeholder, the process may involve the translation of the agent's plans into easily understandable forms, and the design of the user interfaces that facilitate this understanding.…”

Section: Planningmentioning

confidence: 99%

Explanations in Autonomous Driving: A Survey

Omeiza

Webb

Jirotka

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ blackbox AI models to enable vehicles to perceive their environments and make driving decisions with little or no input from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations. The assessment of the compliance of AVs to these acceptance requirements can be facilitated through the provision of explanations for AVs' behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have 'seen', done, and might do in environments in which they operate.In this paper, we provide a comprehensive survey of the existing body of work around explainable autonomous driving. First, we open with a motivation for explanations by highlighting and emphasising the importance of transparency, accountability, and trust in AVs; and examining existing regulations and standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and elicit their explanation requirements for AV. Third, we provide a rigorous review of previous work on explanations for the different AV operations (i.e., perception, localisation, planning, control, and system management). Finally, we identify pertinent challenges and provide recommendations, such as a conceptual framework for AV explainability. This survey aims to provide the fundamental knowledge required of researchers who are interested in explainability in AVs.

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“…Explaining AI applications, especially those involving Machine Learning (ML) ( Holzinger, 2018 ), and Deep Neural Networks (DNN) ( Angelov and Soares, 2020 ; Booth et al, 2021 ), is howbeit still an ongoing effort, due to the high complexity and sophistication of the processes in place (e.g., data handling, algorithm tuning, etc.) as well as the wide range of AI systems such as recommendation systems ( Zhang and Chen, 2020 ), human-agent systems ( Rosenfeld and Richardson, 2019 ), planning systems ( Chakraborti et al, 2020 ), multi-agent systems ( Alzetta et al, 2020 ), autonomous systems ( Langley et al, 2017 ), or robotic systems ( Anjomshoae et al, 2019 ; Rotsidis et al, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

IEEE P7001: A Proposed Standard on Transparency

et al. 2021

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This paper describes IEEE P7001, a new draft standard on transparency of autonomous systems1. In the paper, we outline the development and structure of the draft standard. We present the rationale for transparency as a measurable, testable property. We outline five stakeholder groups: users, the general public and bystanders, safety certification agencies, incident/accident investigators and lawyers/expert witnesses, and explain the thinking behind the normative definitions of “levels” of transparency for each stakeholder group in P7001. The paper illustrates the application of P7001 through worked examples of both specification and assessment of fictional autonomous systems.

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The Emerging Landscape of Explainable Automated Planning & Decision Making

Cited by 68 publications

References 40 publications

Questioning the AI: Informing Design Practices for Explainable AI User Experiences

Questioning the AI: Informing Design Practices for Explainable AI User Experiences

Explanations in Autonomous Driving: A Survey

IEEE P7001: A Proposed Standard on Transparency

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