There are competing notions of fairness — and sometimes they’re totally incompatible with each other.
…Computer scientists are used to thinking about “bias” in terms of its statistical meaning: A program for making predictions is biased if it’s consistently wrong in one direction or another. (For example, if a weather app always overestimates the probability of rain, its predictions are statistically biased.) That’s very clear, but it’s also very different from the way most people colloquially use the word “bias” — which is more like “prejudiced against a certain group or characteristic.”
The problem is that if there’s a predictable difference between two groups on average, then these two definitions will be at odds. If you design your search engine to make statistically unbiased predictions about the gender breakdown among CEOs, then it will necessarily be biased in the second sense of the word. And if you design it not to have its predictions correlate with gender, it will necessarily be biased in the statistical sense. Read More
Daily Archives: April 21, 2022
Are you at fault? Patterned after subreddit r/AmITheAsshole this AI will let you know
AYTA is a project created by WTTDOTM and Alex Petros and presented by Digital Void. It is a collection of 3 unique AI text generation models trained on posts and comments from r/AmITheAsshole and answers the questions that you’ve been asking on reddit for years: was my response to this reasonable, or am I the asshole in this situation?
AYTA responses are auto-generated and based on different datasets. The red model has only been trained on YTA responses and will always say you are at fault. The green model has only been trained on NTA responses and will always absolve you. And the white model was trained on the pre-filtered data. Have fun! Read More
Amazon releases 51-language dataset for language understanding
MASSIVE dataset and Massively Multilingual NLU (MMNLU-22) competition and workshop will help researchers scale natural-language-understanding technology to every language on Earth.
Imagine that all people around the world could use voice AI systems such as Alexa in their native tongues.
One promising approach to realizing this vision is massively multilingual natural-language understanding (MMNLU), a paradigm in which a single machine learning model can parse and understand inputs from many typologically diverse languages. By learning a shared data representation that spans languages, the model can transfer knowledge from languages with abundant training data to those in which training data is scarce. Read More
Rick's Cafe AI 