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COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations

https://doi.org/10.1145/3313831.3376615
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Stimulus complexity and information integration in the spontaneous interpretations of line graphs
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Supersparse linear integer models for optimized medical scoring systems
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Explanation in artificial intelligence: Insights from the social sciences
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Simplicity and probability in causal explanation
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The structure and function of explanations
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Short-term memory processes in text comprehension
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Pattern span: a tool for unwelding visuo–spatial memory
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The robust beauty of improper linear models in decision making.
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Cognitive load in simultaneous interpreting
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Sparse Partially Linear Additive Models
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Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
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Four Experiments on the Perception of Bar Charts
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TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning
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Bar and Line Graph Comprehension: An Interaction of Top‐Down and Bottom‐Up Processes
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Interpretable Classification Models for Recidivism Prediction
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The Role of Surprise in Learning: Different Surprising Outcomes Affect Memorability Differentially
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Artificial intelligence and the future of cybersecurity
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Principles of Explanatory Debugging to Personalize Interactive Machine Learning
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Intelligible Models for HealthCare
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Interacting with Predictions
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Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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The Mythos of Model Interpretability
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Guidelines for Human-AI Interaction
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Gamut
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Designing Theory-Driven User-Centric Explainable AI
inferredcites →https://doi.org/10.1145/3301275.3302289openalex-snapshot
The effects of example-based explanations in a machine learning interface
inferredcites →https://doi.org/10.1145/3301275.3302316openalex-snapshot
Automated rationale generation
inferredcites →https://doi.org/10.1145/3359130openalex-snapshot
Crowdsourcing Perceptions of Fair Predictors for Machine Learning
inferredcites →https://doi.org/10.1177/0018720812452592openalex-snapshot
The Effect of Gestalt Laws of Perceptual Organization on the Comprehension of Three-Variable Bar and Line Graphs
inferredcites →https://doi.org/10.1177/0265532209346454openalex-snapshot
Aspects of performance on line graph description tasks: influenced by graph familiarity and different task features
inferredcites →https://doi.org/10.1201/9781315370279openalex-snapshot
Generalized Additive Models
inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot
Greedy function approximation: A gradient boosting machine.
inferredcites →https://doi.org/10.1214/ss/1177013604openalex-snapshot
Generalized Additive Models
inferredcites →https://doi.org/10.1609/aaai.v32i1.11491openalex-snapshot
Anchors: High-Precision Model-Agnostic Explanations
inferredcites →https://doi.org/10.1609/aaai.v32i1.11501openalex-snapshot
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
inferredcites →https://doi.org/10.1609/aimag.v40i2.2850openalex-snapshot
DARPA's Explainable Artificial Intelligence Program
inferredcites →https://doi.org/10.16910/jemr.9.1.4openalex-snapshot
Task-relevant spatialized auditory cues enhance attention orientation and peripheral target detection in natural scenes
inferredcites →https://doi.org/10.16910/jemr.9.4.4openalex-snapshot
Do Graph Readers Prefer the Graph Type Most Suited to a Given Task? Insights from Eye Tracking
inferredcites →https://doi.org/10.18653/v1/n16-3020openalex-snapshot
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
inferredcites →https://doi.org/10.2139/ssrn.3064761openalex-snapshot
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inferredcites →https://doi.org/10.2307/2532174openalex-snapshot
Generalized Additive Models.
inferredcites →https://doi.org/10.3758/s13423-017-1275-yopenalex-snapshot
Contextual utility affects the perceived quality of explanations
inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
inferredcites →https://doi.org/10.48550/arxiv.1506.03850openalex-snapshot
Generalized Additive Model Selection
inferredcites →https://doi.org/10.48550/arxiv.1711.06178openalex-snapshot
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
inferredcites →https://doi.org/10.48550/arxiv.1802.00682openalex-snapshot
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation
inferredcites →https://doi.org/10.48550/arxiv.1909.09223openalex-snapshot
InterpretML: A Unified Framework for Machine Learning Interpretability
inferredcites →https://doi.org/10.5281/zenodo.1208723openalex-snapshot
dswah/pyGAM: v

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Interpretable Music Recommender Systems
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