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Found 15 papers, 6 papers with code
Using Visual Analytics to Interpret Predictive Machine Learning Models
It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power.
Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models
In this work, we present a visual analysis tool that allows interaction with a trained sequence-to-sequence model through each stage of the translation process.
Debugging Sequence-to-Sequence Models with Seq2Seq-Vis
Neural attention-based sequence-to-sequence models (seq2seq) (Sutskever et al., 2014; Bahdanau et al., 2014) have proven to be accurate and robust for many sequence prediction tasks.
Regularizing Black-box Models for Improved Interpretability
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, whose explanation quality can be unpredictable.
Ablate, Variate, and Contemplate: Visual Analytics for Discovering Neural Architectures
In this paper, we present Rapid Exploration of Model Architectures and Parameters, or REMAP, a visual analytics tool that allows a model builder to discover a deep learning model quickly via exploration and rapid experimentation of neural network architectures.
TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora
Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another.
Discovering and Validating AI Errors With Crowdsourced Failure Reports
AI systems can fail to learn important behaviors, leading to real-world issues like safety concerns and biases.
Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment
Explainable AI (XAI) is a promising means of supporting human-AI collaborations for high-stakes visual detection tasks, such as damage detection tasks from satellite imageries, as fully-automated approaches are unlikely to be perfectly safe and reliable.
Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces
Modern machine learning techniques commonly rely on complex, high-dimensional embedding representations to capture underlying structure in the data and improve performance.
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support
AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts.
"Public(s)-in-the-Loop": Facilitating Deliberation of Algorithmic Decisions in Contentious Public Policy Domains
This position paper offers a framework to think about how to better involve human influence in algorithmic decision-making of contentious public policy issues.
An Interactive Interpretability System for Breast Cancer Screening with Deep Learning
Deep learning methods, in particular convolutional neural networks, have emerged as a powerful tool in medical image computing tasks.
Improving Human-AI Collaboration With Descriptions of AI Behavior
People work with AI systems to improve their decision making, but often under- or over-rely on AI predictions and perform worse than they would have unassisted.
The Impact of Imperfect XAI on Human-AI Decision-Making
Our findings reveal the influence of imperfect XAI and humans' level of expertise on their reliance on AI and human-AI team performance.
How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics Models
Reinforcement learning (RL) is a promising approach to generate treatment policies for sepsis patients in intensive care.
