I started the organization Fair Bytes to advance AI ethics education for all ages through byte-sized content & resources. As part of Fair Bytes, I've written many short (byte-sized) pieces about research and resources related to fairness and ethics of AI.
I also like to write fiction and occasionally write about school, life, and other topics.
A technique to explain how black-box machine learning classifiers make predictions
Elmo, Bert, and Marge (Simpson) aren’t just your favorite TV characters growing up — they’re also machine learning & NLP models
A guide for college students on factors to consider and options for what to do in the next school yeargap
Why many gold-standard computer vision datasets, such as ImageNet, are flawed
Papers, books, and resources to learn about fairness in vision, NLP, and more
An overview of how to use counterfactual fairness to quantify the social bias of crowd workers
Reason #5: Just because you CAN develop an algorithm, doesn’t mean you SHOULD
Despite its impressive performance, the world’s newest language model reflects societal biases in gender, race, and religion
Curricula, projects, and even fiction books to empower students to learn about AI ethics
What does it mean for a machine learning algorithm to be “transparent”?
An overview of how biases against mentions of disabilities are embedded in natural language processing tasks and models
Understanding algorithmic fairness and ethics is more imperative than ever
As we undergo such chaotic times together, a virtual ecosystem gives us the chance to strengthen the bonds we’ve already forged in physical presence.
Tibby zaps awake from her evening nap at seven o’clock, just like any other day. She stretches her joints and takes eleven steps forward. On the tenth, she stumbles into a chair.