What We Do
Use tab to navigate through the menu items.
Jul 12, 2021
Ethically Automating Legal Practice
Can AI automate legal practice?
Jun 29, 2021
The Increasing Gap between AI Innovation and AI Ethics: Facial Recognition
Mapping out the facial recognition landscape
Mar 29, 2021
StereoSet: Combatting Inherently Biased Linguistic Models
Exploring a dataset that measures bias in AI language models
Jan 7, 2021
The Future of Commercial Deep Learning
How do we balance its benefits and integrity going forward?
Sep 20, 2020
Adversarial Machine Learning: An Overview
Explaining the latest and greatest ways people have been fooling your neighborhood neural networks
Aug 13, 2020
Explaining Machine Learning Predictions & Building Trust with LIME
A technique to explain how black-box machine learning classifiers make predictions
Jul 2, 2020
We Need to Change How Image Datasets are Curated
Why many gold-standard computer vision datasets, such as ImageNet, are flawed ImageNet Even though it was created in 2009, ImageNet is...
Jun 18, 2020
New Way to Measure Crowdsourcing Bias in Machine Learning
An overview of how to use counterfactual fairness to quantify the social bias of crowd workers
Jun 10, 2020
How Biased is GPT-3?
The world’s newest language model reflects societal biases in gender, race, and religion
May 20, 2020
What is Transparency in AI?
What does it mean for a machine learning algorithm to be “transparent”?
May 13, 2020
NLP Bias Against People with Disabilities
An overview of how biases against mentions of disabilities are embedded in natural language processing tasks and models
May 5, 2020
Fair Bytes: A Deeper Lens into Fairness in AI
Understanding algorithmic fairness and ethics is more imperative than ever