XAlgo: a Design Probe of Explaining Algorithms’ Internal States via Question-Answering

Rebanal, Juan; Combitsis, Jordan; Tang, Yuqi; Chen, Xiang

doi:10.1145/3397481.3450676

Cited by 15 publications

(14 citation statements)

References 44 publications

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“…and was trained on a dataset of children's storybooks annotated by educational experts for supporting interactive storytelling. Another category of relevant work is on interactive question-answering systems that seek to retrieve answers to questions that users ask (e.g., [61,70]). These systems focus on answer retrieval (instead of question generation) and therefore have quite different goals from our work.…”

Section: Systems For Question-answer Generationmentioning

confidence: 99%

StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement

Zhang,

Xu,

Wang

et al. 2022

Preprint

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Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions. While recent advances made AI generation of questions from stories possible, the fully-automated approach excludes parent involvement, disregards educational goals, and underoptimizes for child engagement. Informed by need-finding interviews and participatory design (PD) results, we developed StoryBuddy, an AI-enabled system for parents to create interactive storytelling experiences. StoryBuddy's design highlighted the need for accommodating dynamic user needs between the desire for parent involvement and parent-child bonding and the goal of minimizing parent intervention when busy. The PD revealed varied assessment and educational goals of parents, which StoryBuddy addressed by supporting configuring question types and tracking child progress.A user study validated StoryBuddy's usability and suggested design insights for future parent-AI collaboration systems.CCS Concepts: • Human-centered computing → Human computer interaction (HCI); Natural language interfaces; • Social and professional topics → Children.

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Section: Systems For Question-answer Generationmentioning

confidence: 99%

StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement

Zhang,

Xu,

Wang

et al. 2022

Preprint

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“…The generated explanations and annotations with a feedback loop are combined to rectify wrong answers [197]. In [198], the authors have proposed XAlgo, an interactive algorithm explaining the system's internal state through question answering. Explanationbased conversational systems are designed to provide better explanations than conventional report-based systems for customer relationship management with an interactive approach for multisensory fusion [199].…”

Section: Cmentioning

confidence: 99%

A Review on Explainability in Multimodal Deep Neural Nets

2021

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Artificial Intelligence techniques powered by deep neural nets have achieved much success in several application domains, most significantly and notably in the Computer Vision applications and Natural Language Processing tasks. Surpassing human-level performance propelled the research in the applications where different modalities amongst language, vision, sensory, text play an important role in accurate predictions and identification. Several multimodal fusion methods employing deep learning models are proposed in the literature. Despite their outstanding performance, the complex, opaque and black-box nature of the deep neural nets limits their social acceptance and usability. This has given rise to the quest for model interpretability and explainability, more so in the complex tasks involving multimodal AI methods. This paper extensively reviews the present literature to present a comprehensive survey and commentary on the explainability in multimodal deep neural nets, especially for the vision and language tasks. Several topics on multimodal AI and its applications for generic domains have been covered in this paper, including the significance, datasets, fundamental building blocks of the methods and techniques, challenges, applications, and future trends in this domain.INDEX TERMS deep multimodal learning, explainable AI, interpretability, survey, trends, vision and language research, XAI.

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“…Despite the different types of explainability one can choose, it appears to be always possible to frame the information provided by explainability with one or (sometimes) more questions. In fact, it is common to many works in the field [9,14,19,21,24,[27][28][29]35] the use of archetypal (e.g. why, who, how, when, etc.)…”

Section: Background and Related Workmentioning

confidence: 99%

“…While Dhurandhar et al [9] clearly state that they designed CEM (a method for the generation of counterfactuals and other contrastive explanations) to answer the question "why is input x classified in class y?". Also, Rebanal et al [28] propose and studies an interactive approach where explaining is defined in terms of answering why-what-how questions.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Interestingly, (and indirectly) on the same line of [24], also Rebanal et al [28] propose and study (only through a Wizard-of-Oz test though) an interactive approach using question answering, to explain deterministic algorithms to non-expert users. Nonetheless, similarly to [24], also [28] focus on a small sub-set of possible types of explanations, avoiding illocution, as suggested by a few of the comments given by their participants: "it answers everything accurately and it gives the information that I asked for but it does so like sounding more like a glossary like a dictionary", "... like a robot's answers ... If I asked someone to explain it, it wouldn't give me all this".…”

Section: Background and Related Workmentioning

confidence: 99%

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Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces

Sovrano,

Vitali

2021

Preprint

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We propose a new method for generating explanations with Artificial Intelligence (AI) and a tool to test its expressive power within a user interface. In order to bridge the gap between philosophy and human-computer interfaces, we show a new approach for the generation of interactive explanations based on a sophisticated pipeline of AI algorithms for structuring natural language documents into knowledge graphs, answering questions effectively and satisfactorily. With this work we aim to prove that the philosophical theory of explanations presented by Achinstein can be actually adapted for being implemented into a concrete software application, as an interactive and illocutionary process of answering questions. Specifically, our contribution is an approach to frame illocution in a computer-friendly way, to achieve user-centrality with statistical question answering. In fact, we frame illocution, in an explanatory process, as that mechanism responsible for anticipating the needs of the explainee in the form of unposed, implicit, archetypal questions, hence improving the user-centrality of the underlying explanatory process. More precisely, we hypothesise that given an arbitrary explanatory process, increasing its goal-orientedness and degree of illocution results in the generation of more usable (as per ISO 9241-210) explanations. We tested our hypotheses with a user-study involving more than 60 participants, on two XAI-based systems, one for credit approval (finance) and one for heart disease prediction (healthcare). The results showed that our proposed solution produced a statistically significant improvement (hence with a p-value lower than 0.05) on effectiveness. This, combined with a visible alignment between the increments in effectiveness and satisfaction, suggests that our understanding of illocution can be correct, giving evidence in favour of our theory.CCS Concepts: • Human-centered computing → HCI theory, concepts and models; Empirical studies in HCI.

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XAlgo: a Design Probe of Explaining Algorithms’ Internal States via Question-Answering

Cited by 15 publications

References 44 publications

StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement

StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement

A Review on Explainability in Multimodal Deep Neural Nets

Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces

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