Arvind Sanjeev shows how private user data leaks into machine learning data — from medical records and smart home photos to real faces.

Fawzi Ammache explores solutions and challenges of paying artists for their contributions to train AI models.

Yet another sad story shows that futuristic AI-based products are backed by sweat & pain of low-waged human workers.

Artists can search machine learning databases for links to their work and flag them for removal. It's a part of a bigger Spawning initiative — they're building tools for artist ownership of their training data, allowing them to opt into or opt out of the training of large AI models, set permissions on how their style and likeness is used, and offer their own models to the public.

Brilliant thinking by Alexander Wales on how tools like DALL-E and Midjourney influence professional illustrators and artists. It'll kick the economy of these professions in the stomach for sure. However, art as self-expression will stay for sure. Erik Hoel has a similar take.

Aaron Hertzmann draws interesting parallels between today's algorithm-driven design tool boom and other branches of arts and culture for past centuries. He thinks current state is just interim and shows on-spot analogies.

This experiment shows how words like "assertive" and "gentle" are mapped to stereotypes and biases in models like Stable Diffusion and DALL-E 2 (review). Bloomberg has a good long-read article about this problem.

Under current law, only natural persons may be named as an inventor in a patent application.

An accessible synthesis of ethical issues raised by artificial intelligence that moves beyond hype and nightmare scenarios to address concrete questions.

AI is like photography in the 19th century–struggling to be accepted as its own art form. Aaron Hertzmann discusses tricky situations in art world.