Browsing by Author "El-Assady, Mennatallah"

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Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework
(IEEE, 2017-08-29) El-Assady, Mennatallah; Sevastjanova, Rita; Sperrle, Fabian; Keim, Daniel; Collins, Christopher
Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.
The Role of Interactive Visualization in Fostering Trust in AI
(IEEE, 2021-12-10) Beauxis-Aussalet, Emma; Behrisch, Michael; Borgo, Rita; Chau, Duen Horng; Collins, Christopher; Ebert, David; El-Assady, Mennatallah; Endert, Alex; Keim, Daniel A.; Kohlhammer, Jörn; Oelke, Daniela; Peltonen, Jaakko; Riveiro, Maria; Schreck, Tobias; Strobelt, Hendrik; van Wijk, Jarke J.
The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI's trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.
Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution
(IEEE, 2018-09-18) El-Assady, Mennatallah; Sperrle, Fabian; Deussen, Oliver; Keim, Daniel; Collins, Christopher
To effectively assess the potential consequences of human interventions in model-driven analytics systems, we establish the concept of speculative execution as a visual analytics paradigm for creating user-steerable preview mechanisms. This paper presents an explainable, mixed-initiative topic modeling framework that integrates speculative execution into the algorithmic decision- making process. Our approach visualizes the model-space of our novel incremental hierarchical topic modeling algorithm, unveiling its inner-workings. We support the active incorporation of the user’s domain knowledge in every step through explicit model manipulation interactions. In addition, users can initialize the model with expected topic seeds, the backbone priors. For a more targeted optimization, the modeling process automatically triggers a speculative execution of various optimization strategies, and requests feedback whenever the measured model quality deteriorates. Users compare the proposed optimizations to the current model state and preview their effect on the next model iterations, before applying one of them. This supervised human-in-the-loop process targets maximum improvement for minimum feedback and has proven to be effective in three independent studies that confirm topic model quality improvements.
Visualization and the Digital Humanities: Moving Toward Stronger Collaborations
(IEEE, 2018-12-01) Bradley, Adam James; El-Assady, Mennatallah; Coles, Katharine; Alexander, Eric; Chen, Min; Collins, Christopher; Jänicke, Stefan; Joseph, David
For the past two years, researchers from the visualization community and the digital humanities have come together at the IEEE VIS conference to discuss how both disciplines can work together to push research goals in their respective disciplines. In this paper, we present our experiences as a result of this collaboration.