Stakeholders in Explainable AI

Preece, Alun; Harborne, Dan; Braines, Dave; Tomsett, Richard; Chakraborty, Supriyo

doi:10.48550/arxiv.1810.00184

Cited by 42 publications

(59 citation statements)

References 7 publications

(19 reference statements)

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“…A starting point to address these challenges is to map out the design space of XAI and develop frameworks that account for people's diverse explainability needs. Many have summarized common user groups that demand explainability and what they would use AI explanations for [11,49,83]:…”

Section: Diverse Explainability Needs Of Ai Stakeholdersmentioning

confidence: 99%

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

Liao¹,

Varshney²

2021

Preprint

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Book Chapter Draft 10/2021) As a technical sub-field of artificial intelligence (AI), explainable AI (XAI) has produced a vast collection of algorithms, providing a toolbox for researchers and practitioners to build XAI applications. With the rich application opportunities, explainability has moved beyond a demand by data scientists or researchers to comprehend the models they are developing, to become an essential requirement for people to trust and adopt AI deployed in numerous domains. However, explainability is an inherently human-centric property and the field is starting to embrace human-centered approaches. Human-computer interaction (HCI) research and user experience (UX) design in this area are becoming increasingly important. In this chapter, we begin with a high-level overview of the technical landscape of XAI algorithms, then selectively survey our own and other recent HCI works that take human-centered approaches to design, evaluate, provide conceptual and methodological tools for XAI. We ask the question "what are human-centered approaches doing for XAI " and highlight three roles that they play in shaping XAI technologies by helping navigate, assess and expand the XAI toolbox: to drive technical choices by users' explainability needs, to uncover pitfalls of existing XAI methods and inform new methods, and to provide conceptual frameworks for human-compatible XAI.

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Section: Diverse Explainability Needs Of Ai Stakeholdersmentioning

confidence: 99%

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

Liao¹,

Varshney²

2021

Preprint

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“…Accordingly, one of the reflection points is the audience and stakeholders involved in XAI. Preece et al (2018) attribute the fact that there is no consensus for explainability and interpretability to the fact that different stakeholder communities have to deal with them, and further depict where this perspective overlap and where not. Moreover, Samek, Wiegand & Muller (2017) identify as reasons for needing XAI the following: system verification, learning from the system, and compliance to legislation.…”

Section: Related Researchmentioning

confidence: 99%

On Explainable AI Solutions for Targeting in Cyber Military Operations

Maathuis

2022

iccws

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Nowadays, it is hard to recall a domain, system, or problem that does not use, embed, or could be tackled through AI. From early stages of its development, its techniques and technologies were successfully implemented by military forces for different purposes in distinct military operations. Since cyberspace represents the last officially recognized operational battlefield, it also offers a direct virtual setting for implementing AI solutions for military operations conducted inside or through it. However, planning and conducting AI-based cyber military operations are actions still in the beginning of development. Thus, both practitioner and academic dedication isrequired since the impact of their use could have significant consequences which requires that the output of such intelligent solutions is explainable to the engineers developing them and also to their users e.g., military decision makers. Hence, this article starts by discussing the meaning of explainable AI in the context of targeting in military cyber operations, continues by analyzing the challenges of embedding AI solutions (e.g., intelligent cyber weapons) in different targeting phases, and is structuring them in corresponding taxonomies packaged in a design framework. It does that by crossing the targeting process focusing on target development, capability analysis, and target engagement. Moreover, this research argues that especially in such operations carried out in silence and at incredible speed, it is of major importance that the military forces involved are aware of the following. First, the decisions taken by theintelligent systems embedded. Second, are not only aware, but also able to interpret the results obtained from the AI solutions in a proper, effective, and efficient way. From there, this research draws possible technological and humanoriented methods that facilitate the successful implementation of XAI solutions for targeting in military cyber operations.

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“…Typical categorizations of stakeholders are based on their role in an organization [3,9,12,16], their machine learning experience [18] or a combination of the two [15]. Also for the categorization of the stakeholder needs regarding explainability, different propositions are made in the literature.…”

Section: Understanding Stakeholder Needsmentioning

confidence: 99%

“…Some authors mention possible high-level goals of explainability, such as model debugging, monitoring etc. [3] or revealing (un)known (un)knowns [12]. Langer et al [9] provide a list of more detailed needs such as privacy, fairness, legal compliance, etc.…”

Section: Understanding Stakeholder Needsmentioning

confidence: 99%

How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice

Vermeire¹,

Laugel²,

Renard³

et al. 2021

Preprint

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Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness. Various and significantly different algorithmic methods to provide this explainability have been introduced in the field, but the existing literature in the machine learning community has paid little attention to the stakeholder whose needs are rather studied in the human-computer interface community. Therefore, organizations that want or need to provide this explainability are confronted with the selection of an appropriate method for their use case. In this paper, we argue there is a need for a methodology to bridge the gap between stakeholder needs and explanation methods. We present our ongoing work on creating this methodology to help data scientists in the process of providing explainability to stakeholders. In particular, our contributions include documents used to characterize XAI methods and user requirements (shown in Appendix), which our methodology builds upon.

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Stakeholders in Explainable AI

Cited by 42 publications

References 7 publications

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

On Explainable AI Solutions for Targeting in Cyber Military Operations

How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice

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