Unsupervised Explainable Controversy Detection from Online News

Kim, Youngwoo; Allan, James

doi:10.1007/978-3-030-15712-8_60

Cited by 2 publications

(1 citation statement)

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“…Therefore, recently there has been growing interest in building explainable artificial intelligence (XAI)-specifically explainable machine learning-systems. Notable examples include explainable recommendation systems (e.g., [10,92,101]), classification systems (e.g., [4,40,71,91]), and activity recognition systems (e.g., [6,56,75,102]). In this paper, we focused on an explainable activity recognition system for videos based on our previous work [75].…”

Section: Explainable Machine Learning and Artificial Intelligencementioning

confidence: 99%

Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition

Nourani,

Roy,

Rahman

et al. 2020

Preprint

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Explainable machine learning and artificial intelligence models have been used to justify a model's decisionmaking process. This added transparency aims to help improve user performance and understanding of the underlying model. However, in practice, explainable systems face many open questions and challenges. Specifically, designers might reduce the complexity of deep learning models in order to provide interpretability. The explanations generated by these simplified models, however, might not accurately justify and be truthful to the model. This can further add confusion to the users as they might not find the explanations meaningful with respect to the model predictions. Understanding how these explanations affect user behavior is an ongoing challenge. In this paper, we explore how explanation veracity affects user performance and agreement in intelligent systems. Through a controlled user study with an explainable activity recognition system, we compare variations in explanation veracity for a video review and querying task. The results suggest that low veracity explanations significantly decrease user performance and agreement compared to both accurate explanations and a system without explanations. These findings demonstrate the importance of accurate and understandable explanations and caution that poor explanations can sometimes be worse than no explanations with respect to their effect on user performance and reliance on an AI system. CCS Concepts: • Human-centered computing → Empirical studies in HCI; User studies; • Computing methodologies → Machine learning approaches.

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