Homepage
Open in app
Sign in
Get started
Fair Bytes
Sharing byte-sized stories about fairness and ethics of AI
About
Research
Resources
Expert Interviews
Follow
Interview: Responsible AI with Anna Bethke
Interview: Responsible AI with Anna Bethke
How can we more fully consider building AI for social good?
Catherine Yeo
Jul 14, 2022
The Future of AI Ethics: Blockchain
The Future of AI Ethics: Blockchain
How blockchain enables data transparency and ethical AI
Tanishq Sandhu
Apr 2, 2022
Interview: AI & Criminal Justice with Julia Dressel
Interview: AI & Criminal Justice with Julia Dressel
How do AI algorithms fit into recidivism prediction and criminal justice going forward?
Catherine Yeo
Mar 23, 2022
Interview: Robot Ethics & Design with Shalaleh Rismani
Interview: Robot Ethics & Design with Shalaleh Rismani
How can we better design responsible AI and technology?
Catherine Yeo
Oct 26, 2021
Ethically Automating Legal Practice
Ethically Automating Legal Practice
Can AI automate legal practice?
Sylvia E
Jul 12, 2021
The Increasing Gap between AI Innovation and AI Ethics: Facial Recognition
The Increasing Gap between AI Innovation and AI Ethics: Facial Recognition
Mapping out the facial recognition landscape.
Tanishq Sandhu
Jun 29, 2021
StereoSet: Combatting inherently biased linguistic models
StereoSet: Combatting inherently biased linguistic models
Exploring a dataset that measures bias in AI language models
Tanishq Sandhu
Mar 29, 2021
The Future of Commercial Deep Learning
The Future of Commercial Deep Learning
How do we balance its benefits and integrity going forward?
Arjun Subramonian 💻🐻🏳️🌈
Jan 7, 2021
Algorithms & Bias
Algorithms & Bias
Resources to gain a better understanding of how technology can be bias too.
Hiba
Sep 20, 2020
Adversarial Machine Learning: An Overview
Adversarial Machine Learning: An Overview
An exposition of the latest and greatest ways people have been fooling your neighborhood neural networks.
Pradyumna Shome
Sep 20, 2020
Explaining Machine Learning Predictions and Building Trust with LIME
Explaining Machine Learning Predictions and Building Trust with LIME
A technique to explain how black-box machine learning classifiers make predictions
Catherine Yeo
Aug 13, 2020
We Need to Change How Image Datasets are Curated
We Need to Change How Image Datasets are Curated
Why many gold-standard computer vision datasets, such as ImageNet, are flawed
Catherine Yeo
Jul 1, 2020
Reading List for Fairness in AI Topics
Reading List for Fairness in AI Topics
Papers, books, and resources to learn about fairness in vision, NLP, and more
Catherine Yeo
Jun 24, 2020
New Way to Measure Crowdsourcing Bias in Machine Learning
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
Catherine Yeo
Jun 17, 2020
Why We Need AI Literacy
Why We Need AI Literacy
Reason #5: Just because you CAN develop an algorithm, doesn’t mean you SHOULD
Catherine Yeo
Jun 10, 2020
How Biased is GPT-3?
How Biased is GPT-3?
Despite its impressive performance, the world’s newest language model reflects societal biases in gender, race, and religion
Catherine Yeo
Jun 3, 2020
About Fair Bytes
Latest Stories
Archive
About Medium
Terms
Privacy
Teams