Explainability Visualization
Explainability Visualization - Related Videos
01:30
Peer Review Explained
0 Views •
2026
Peer review is a central quality-control process in academic publishing. Before research is shared with the scholarly community, experts in the relevant field evaluate manuscripts to ensure that the findings are accurate, original, and methodologically sound. This review process helps maintain the reliability of the scientific record and reinforces trust in published literature. Submission and Editorial Screening The process begins when an author submits a manuscript to an academic journal. The...
06:02
Visual Statistical Learning
0 Views •
2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University The visual environment contains massive amounts of information involving the relations between objects in space and time; certain objects are more likely to appear in the vicinity of other objects. Learning these regularities can support a wide array of visual processing, including object recognition. Unsurprisingly, then, humans appear to learn these regularities automatically, quickly, and without conscious awareness. The name...
12:23
Dynamic Visual Tests to Identify and Quantify Visual Damage and Repair Following Demyelination in Optic Neuritis Patients
0 Views •
Cited by 8 •
2014
Object From Motion (OFM) and Time-constrained stereo protocols are sensitive tools to identify monocular and binocular dynamic visual function deficits, which are uniquely affected in optic neuritis patients. Furthermore, these tests may be used as quantitative noninvasive tools to assess the extent of myelination along visual pathways.
02:34
Kinetic Molecular Theory and Gas Laws Explain Properties of Gas Molecules
0 Views •
2020
The test of the kinetic molecular theory (KMT) and its postulates is its ability to explain and describe the behavior of a gas. The various gas laws (Boyle’s, Charles’s, Gay-Lussac’s, Avogadro’s, and Dalton’s laws) can be derived from the assumptions of the KMT, which have led chemists to believe that the assumptions of the theory accurately represent the properties of gas molecules. The Kinetic Molecular Theory Explains the Behavior of Gases Recalling that gas pressure is exerted by rapidly...
05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
0 Views •
Cited by 2 •
2025
This article describes RUGGED (Retrieval Under Graph-Guided Explainable disease Distinction), which integrates Large Language Model (LLM) inference with Retrieval-Augmented Generation (RAG). It draws evidence from expert-curated biomedical knowledge bases and peer-reviewed biomedical publications to synthesize new knowledge from up-to-date information, identify explainable and actionable predictions, and pinpoint promising directions for hypothesis-driven investigations.
FAQs
These displays create a trace between the information entering a model, patterns formed within it, and the prediction produced at the end. Feature-importance rankings emphasize influential variables, decision paths show the route toward an outcome, and plots or heat maps expose relationships in a more inspectable form. This makes model behavior easier to evaluate than an output viewed alone.
Feature-importance rankings indicate which variables most influence a result, while decision paths present the sequence associated with reaching an outcome. Plots and heat maps provide visual views of relationships between inputs, intermediate patterns, and predictions. Selecting among these formats helps an evaluator focus on variable influence, model reasoning, or broader patterns in the analytical results.
Comparing visualizations across cases can show whether influential variables or decision patterns remain consistent or change with the situation. Such comparisons help researchers identify errors, recognize possible bias, and examine how a system responds to different inputs. In engineering, this case-based view also supports evaluation of model behavior as part of broader safety assessment.
A basic workflow is to select a visual representation suited to the behavior being examined, relate the displayed information to the model’s inputs and predicted outcome, and inspect the resulting pattern for influential variables or unexpected behavior. Researchers can then use those observations to support validation, debugging, comparison across cases, or communication with others evaluating the system.
During validation, visual displays help researchers inspect whether model behavior appears consistent with the analytical result and the variables contributing to it. During debugging, they can expose influential variables, unexpected patterns, or errors associated with particular outcomes. This gives engineering teams a more direct basis for examining model performance than relying only on final predictions.
Explainability visualizations support safety assessment by making model behavior available for inspection across individual results and comparisons. Researchers can use the displays to examine influential variables, detect errors or bias, and communicate how a system behaves. These activities strengthen confidence in data-driven engineering systems and help determine whether deployment can proceed responsibly.