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Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection
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2023 | Information Systems Frontiers | Citations: 0
Authors: Meske, Christian; Bunde, Enrico

Abstract: Hate speech in social media is an increasing problem that can negatively affect ... Expand

Abstract: Hate speech in social media is an increasing problem that can negatively affect individuals and society as a whole. Moderators on social media platforms need to be technologically supported to detect problematic content and react accordingly. In this article, we develop and discuss the design principles that are best suited for creating efficient user interfaces for decision support systems that use artificial intelligence (AI) to assist human moderators. We qualitatively and quantitatively evaluated various design options over three design cycles with a total of 641 participants. Besides measuring perceived ease of use, perceived usefulness, and intention to use, we also conducted an experiment to prove the significant influence of AI explainability on end users’ perceived cognitive efforts, perceived informativeness, mental model, and trustworthiness in AI. Finally, we tested the acquired design knowledge with software developers, who rated the reusability of the proposed design principles as high. Collapse

Semantic filters: ULMFit

Topics: artificial intelligence explainable artificial intelligence perceived ease of use usage intention social media

Methods: design science experiment design principle survey qualitative content analysis
Transformer-based Summarization and Sentiment Analysis of SEC 10-K Annual Reports for Company Performance Prediction
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2022 | HICSS | Citations: 0
Authors: Hsieh, Hsin-Ting; Hristova, Diana

Abstract: Annual reports published by companies contain important insights regarding their ... Expand

Abstract: Annual reports published by companies contain important insights regarding their performance and are often analyzed in a manual, subjective manner. We address this point by combining the streams of research on text summarization and topic modelling with the one on sentiment analysis. Our approach consists of the steps of text summarization using BERTSUMEXT, topic modelling with LDA, sentiment analysis with FinBERT, and performance prediction with Decision Trees and Random Forest. The result provides decision makers with an interpretable and condensed representation of the content of annual reports, together with its relationship to future company performance. We evaluate our approach on 10-K reports, demonstrating both its interpretability for analysts and explanatory power regarding future company performance. Collapse

Semantic filters: ULMFit

Topics: organizational productivity data breach cloud computing Python explainable artificial intelligence

Methods: sentiment analysis topic model machine learning BERT decision tree classification

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Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

Transformer-based Summarization and Sentiment Analysis of SEC 10-K Annual Reports for Company Performance Prediction