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09/10/2023
Decolonial AI Alignment: Viśesadharma, Argument, and Artistic Expression
Prior work has explicated the coloniality of artificial intelligence (AI...
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05/22/2023
Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models
Auditing unwanted social bias in language models (LMs) is inherently har...
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04/02/2023
Towards Healthy AI: Large Language Models Need Therapists Too
Recent advances in large language models (LLMs) have led to the developm...
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02/17/2023
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions
Ensuring trustworthiness in machine learning (ML) models is a multi-dime...
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12/13/2022
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting
In consequential decision-making applications, mitigating unwanted biase...
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11/02/2022
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach
Interpretable and explainable machine learning has seen a recent surge o...
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10/13/2022
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models
We introduce equi-tuning, a novel fine-tuning method that transforms (po...
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08/22/2022
Minimax AUC Fairness: Efficient Algorithm with Provable Convergence
The use of machine learning models in consequential decision making ofte...
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08/02/2022
Humble Machines: Attending to the Underappreciated Costs of Misplaced Distrust
It is curious that AI increasingly outperforms human decision makers, ye...
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01/22/2022
Differentially Private SGDA for Minimax Problems
Stochastic gradient descent ascent (SGDA) and its variants have been the...
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10/20/2021
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
As a technical sub-field of artificial intelligence (AI), explainable AI...
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09/29/2021
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness
To ensure trust in AI models, it is becoming increasingly apparent that ...
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09/24/2021
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increa...
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08/18/2021
Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention
Current methods for viral discovery target evolutionarily conserved prot...
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06/17/2021
Biomedical Interpretable Entity Representations
Pre-trained language models induce dense entity representations that off...
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06/02/2021
Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI
In this paper, we describe an open source Python toolkit named Uncertain...
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04/09/2021
Automated Meta-Analysis: A Causal Learning Perspective
Meta-analysis is a systematic approach for understanding a phenomenon by...
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02/03/2021
Insiders and Outsiders in Research on Machine Learning and Society
A subset of machine learning research intersects with societal issues, i...
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01/29/2021
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users
Systems aiming to aid consumers in their decision-making (e.g., by imple...
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12/22/2020
Learning to Initialize Gradient Descent Using Gradient Descent
Non-convex optimization problems are challenging to solve; the success a...
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10/30/2020
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective
Recently, invariant risk minimization (IRM) was proposed as a promising ...
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10/15/2020
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making
Several strands of research have aimed to bridge the gap between artific...
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06/19/2020
Trust and Transparency in Contact Tracing Applications
The global outbreak of COVID-19 has led to focus on efforts to manage an...
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06/10/2020
Fair Data Integration
The use of machine learning (ML) in high-stakes societal decisions has e...
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02/05/2020
Joint Optimization of AI Fairness and Utility: A Human-Centered Approach
Today, AI is increasingly being used in many high-stakes decision-making...
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11/18/2019
Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies
More than 200 generic drugs approved by the U.S. Food and Drug Administr...
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11/11/2019
Experiences with Improving the Transparency of AI Models and Services
AI models and services are used in a growing number of highstakes areas,...
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11/09/2019
Preservation of Anomalous Subgroups On Machine Learning Transformed Data
In this paper, we investigate the effect of machine learning based anony...
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10/30/2019
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning
We introduce a framework for dynamic adversarial discovery of informatio...
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10/29/2019
Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets
Recent advances in computer vision and deep learning have led to breakth...
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10/17/2019
An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy
Our goal is to understand the so-called trade-off between fairness and a...
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09/08/2019
How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?
In recent years there has been an increasing trend in which data scienti...
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09/06/2019
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
As artificial intelligence and machine learning algorithms make further ...
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07/09/2019
Characterization of Overlap in Observational Studies
Overlap between treatment groups is required for nonparametric estimatio...
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06/05/2019
Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning
Using machine learning in high-stakes applications often requires predic...
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05/27/2019
Open Platforms for Artificial Intelligence for Social Good: Common Patterns as a Pathway to True Impact
The AI for social good movement has now reached a state in which a large...
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05/08/2019
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
The dearth of prescribing guidelines for physicians is one key driver of...
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12/14/2018
Bias Mitigation Post-processing for Individual and Group Fairness
Whereas previous post-processing approaches for increasing the fairness ...
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11/30/2018
Understanding Unequal Gender Classification Accuracy from Face Images
Recent work shows unequal performance of commercial face classification ...
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11/12/2018
TED: Teaching AI to Explain its Decisions
Artificial intelligence systems are being increasingly deployed due to t...
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11/03/2018
SimplerVoice: A Key Message & Visual Description Generator System for Illiteracy
We introduce SimplerVoice: a key message and visual description generato...
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10/25/2018
Promoting Distributed Trust in Machine Learning and Computational Simulation via a Blockchain Network
Policy decisions are increasingly dependent on the outcomes of simulatio...
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10/03/2018
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Fairness is an increasingly important concern as machine learning models...
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09/22/2018
Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach
Large-scale computational experiments, often running over weeks and over...
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08/22/2018
Increasing Trust in AI Services through Supplier's Declarations of Conformity
The accuracy and reliability of machine learning algorithms are an impor...
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07/16/2018
Teaching machines to understand data science code by semantic enrichment of dataflow graphs
Your computer is continuously executing programs, but does it really und...
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07/03/2018
Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018)
This is the Proceedings of the 2018 ICML Workshop on Human Interpretabil...
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06/25/2018
Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory
As artificial intelligence is increasingly affecting all parts of societ...
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06/14/2018
Financial Forecasting and Analysis for Low-Wage Workers
Despite the plethora of financial services and products on the market no...
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05/29/2018