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Found 9 papers, 5 papers with code
An In-depth Look at Gemini's Language Abilities
The recently released Google Gemini class of models are the first to comprehensively report results that rival the OpenAI GPT series across a wide variety of tasks.
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms
Motivated by these challenges, ML researchers have developed new slice discovery algorithms that aim to group together coherent and high-error subsets of data.
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.
Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models
A growing body of work studies Blindspot Discovery Methods ("BDM"s): methods that use an image embedding to find semantically meaningful (i. e., united by a human-understandable concept) subsets of the data where an image classifier performs significantly worse.
"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.
Symphony: Composing Interactive Interfaces for Machine Learning
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems.
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.
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
We present FairVis, a mixed-initiative visual analytics system that integrates a novel subgroup discovery technique for users to audit the fairness of machine learning models.
Interactive Classification for Deep Learning Interpretation
We present an interactive system enabling users to manipulate images to explore the robustness and sensitivity of deep learning image classifiers.
