ConceptExplainer: Understanding the Mental Model of Deep Learning Algorithms via Interactive Concept-based Explanations

Traditional deep learning interpretability methods which are suitable for non-expert users cannot explain network behaviors at the global level and are inflexible at providing fine-grained explanations. As a solution, concept-based explanations are gaining attention due to their human intuitiveness and their flexibility to describe both global and local model behaviors. Concepts are groups of similarly meaningful pixels that express a notion, embedded within the network's latent space and have primarily been hand-generated, but have recently been discovered by automated approaches. Unfortunately, the magnitude and diversity of discovered concepts makes it difficult for non-experts to navigate and make sense of the concept space, and lack of easy-to-use software also makes concept explanations inaccessible to many non-expert users. Visual analytics can serve a valuable role in bridging these gaps by enabling structured navigation and exploration of the concept space to provide concept-based insights of model behavior to users. To this end, we design, develop, and validate ConceptExplainer, a visual analytics system that enables non-expert users to interactively probe and explore the concept space to explain model behavior at the instance/class/global level. The system was developed via iterative prototyping to address a number of design challenges that non-experts face in interpreting the behavior of deep learning models. Via a rigorous user study, we validate how ConceptExplainer supports these challenges. Likewise, we conduct a series of usage scenarios to demonstrate how the system supports the interactive analysis of model behavior across a variety of tasks and explanation granularities, such as identifying concepts that are important to classification, identifying bias in training data, and understanding how concepts can be shared across diverse and seemingly dissimilar classes.Comment: 9 pages, 6 figure

People's Perceptions Toward Bias and Related Concepts in Large Language Models: A Systematic Review

Large language models (LLMs) have brought breakthroughs in tasks including translation, summarization, information retrieval, and language generation, gaining growing interest in the CHI community. Meanwhile, the literature shows researchers' controversial perceptions about the efficacy, ethics, and intellectual abilities of LLMs. However, we do not know how lay people perceive LLMs that are pervasive in everyday tools, specifically regarding their experience with LLMs around bias, stereotypes, social norms, or safety. In this study, we conducted a systematic review to understand what empirical insights papers have gathered about people's perceptions toward LLMs. From a total of 231 retrieved papers, we full-text reviewed 15 papers that recruited human evaluators to assess their experiences with LLMs. We report different biases and related concepts investigated by these studies, four broader LLM application areas, the evaluators' perceptions toward LLMs' performances including advantages, biases, and conflicting perceptions, factors influencing these perceptions, and concerns about LLM applications

PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models

Large Language Models (LLMs) have gained widespread popularity due to their ability to perform ad-hoc Natural Language Processing (NLP) tasks with a simple natural language prompt. Part of the appeal for LLMs is their approachability to the general public, including individuals with no prior technical experience in NLP techniques. However, natural language prompts can vary significantly in terms of their linguistic structure, context, and other semantics. Modifying one or more of these aspects can result in significant differences in task performance. Non-expert users may find it challenging to identify the changes needed to improve a prompt, especially when they lack domain-specific knowledge and lack appropriate feedback. To address this challenge, we present PromptAid, a visual analytics system designed to interactively create, refine, and test prompts through exploration, perturbation, testing, and iteration. PromptAid uses multiple, coordinated visualizations which allow users to improve prompts by using the three strategies: keyword perturbations, paraphrasing perturbations, and obtaining the best set of in-context few-shot examples. PromptAid was designed through an iterative prototyping process involving NLP experts and was evaluated through quantitative and qualitative assessments for LLMs. Our findings indicate that PromptAid helps users to iterate over prompt template alterations with less cognitive overhead, generate diverse prompts with help of recommendations, and analyze the performance of the generated prompts while surpassing existing state-of-the-art prompting interfaces in performance

Spherical similarity explorer for comparative case analysis

Comparative Case Analysis (CCA) is an important tool for criminal investigation and crime theory extraction. It analyzes the commonalities and differences between a collection of crime reports in order to understand crime patterns and identify abnormal cases. A big challenge of CCA is the data processing and exploration. Traditional manual approach can no longer cope with the increasing volume and complexity of the data. In this paper we introduce a novel visual analytics system, Spherical Similarity Explorer (SSE) that automates the data processing process and provides interactive visualizations to support the data exploration. We illustrate the use of the system with uses cases that involve real world application data and evaluate the system with criminal intelligence analysts

Herbal Extracts That Reduce Ocular Oxidative Stress May Enhance Attentive Performance in Humans

We used herbal extracts in this study to investigate the effects of blue-light-induced oxidative stress on subjects’ attentive performance, which is also associated with work performance. We employed an attention network test (ANT) to measure the subjects’ work performance indirectly and used herbal extracts to reduce ocular oxidative stress. Thirty-two subjects participated in either an experimental group (wearing glasses containing herbal extracts) or a control group (wearing glasses without herbal extracts). During the ANT experiment, we collected electroencephalography (EEG) and electrooculography (EOG) data and measured button responses. In addition, electrocardiogram (ECG) data were collected before and after the experiments. The EOG results showed that the experimental group exhibited a reduced number of eye blinks per second during the experiment and faster button responses with a smaller variation than did the control group; this group also showed relatively more sustained tension in their ECG results. In the EEG analysis, the experimental group had significantly greater cognitive processing, with larger P300 and parietal 2–6 Hz activity, an orienting effect with neural processing of frontal area, high beta activity in the occipital area, and an alpha and beta recovery process after the button response. We concluded that reducing blue-light-induced oxidative stress with herbal extracts may be associated with reducing the number of eye blinks and enhancing attentive performance