explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning

Spinner, Thilo; Schlegel, Udo; Schäfer, Hanna; El‐Assady, Mennatallah

doi:10.1109/tvcg.2019.2934629

Cited by 174 publications

(163 citation statements)

References 61 publications

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“…Spinner et al [SSSE20] develop a pipeline for model analysis and comparison that organizes such methods into three categories that focus on model input, model output, and model internals. In the latter category, a range of methods have been proposed to “open up” those black boxes and provide a view of the inner workings of ML models.…”

Section: Related Workmentioning

confidence: 99%

Boxer: Interactive Comparison of Classifier Results

Gleicher

Barve

et al. 2020

Computer Graphics Forum

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Machine learning practitioners often compare the results of different classifiers to help select, diagnose and tune models. We present Boxer, a system to enable such comparison. Our system facilitates interactive exploration of the experimental results obtained by applying multiple classifiers to a common set of model inputs. The approach focuses on allowing the user to identify interesting subsets of training and testing instances and comparing performance of the classifiers on these subsets. The system couples standard visual designs with set algebra interactions and comparative elements. This allows the user to compose and coordinate views to specify subsets and assess classifier performance on them. The flexibility of these compositions allow the user to address a wide range of scenarios in developing and assessing classifiers. We demonstrate Boxer in use cases including model selection, tuning, fairness assessment, and data quality diagnosis.

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Section: Related Workmentioning

confidence: 99%

Boxer: Interactive Comparison of Classifier Results

Gleicher

Barve

et al. 2020

Computer Graphics Forum

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“…Aiming a user-centric view into decision-making for fraud detection, the domain of visual analytics has also been providing contributions through visualization tools and capabilities for fraud detection [21]. Indeed, some researchers have acknowledged the importance of the human-computer interaction or human-in-the-loop perspectives contributing to research in XAI, and the need to investigate new human-AI interfaces for Explainable AI [22][23][24][25][26]. Therefore, a user-centric perspective is essential for reviewing fraud cases, whether in XAI or visual analytics research, as every wrong decision made causes financial harm for customers and organizations.…”

Section: Introductionmentioning

confidence: 99%

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Cirqueira

Nedbal

Helfert

et al. 2020

Lecture Notes in Computer Science

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Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder's requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenariobased design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection.

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“…However, most approaches only use data analysis and only use machine learning for recommendations [11,22,25]. Decision support using machine learning techniques to provide explanations is a recent development, and as such, the amount of work is scarce [9,12,50].…”

Section: Visual Analyticsmentioning

confidence: 99%

ExplainExplore: Visual Exploration of Machine Learning Explanations

Collaris

Wijk

2020

2020 IEEE Pacific Visualization Symposium (PacificVis)

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explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning

Cited by 174 publications

References 61 publications

Boxer: Interactive Comparison of Classifier Results

Boxer: Interactive Comparison of Classifier Results

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

ExplainExplore: Visual Exploration of Machine Learning Explanations

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