On February 6, Meta said it was going to label AI-generated images on Facebook, Instagram, and Threads. When someone uses Meta's AI tools to create images, the company will add visible markers to the image, as well as invisible watermarks and metadata in the image file. The company says its standards are in line with best practices laid out by the Partnership on AI, an AI research nonprofit. Big Tech is also throwing its weight behind a promising technical standard that could add a "nutrition label" to images, video, and audio. Called C2PA, it's an open-source internet protocol that relies on cryptography to encode details about the origins of a piece of content, or what technologists refer to as "provenance" information.

With the boom of AI-generated text, images, and videos, both lawmakers and average internet users have been calling for more transparency. Though it might seem like a very reasonable ask to simply add a label (which it is), it is not actually an easy one, and the existing solutions, like AI-powered detection and watermarking, have some serious pitfalls. As my colleague Melissa Heikkilä has written, most of the current technical solutions "don't stand a chance against the latest generation of AI language models." Nevertheless, the race to label and detect AI-generated content is on. That's where this protocol comes in.

Inc (AMZN.O) is planning to roll out warning cards for software sold by its cloud-computing division, in light of ongoing concern that artificially intelligent systems can discriminate against different groups, the company told Reuters. Akin to lengthy nutrition labels, Amazon's so-called AI Service Cards will be public so its business customers can see the limitations of certain cloud services, such as facial recognition and audio transcription. The goal would be to prevent mistaken use of its technology, explain how its systems work and manage privacy, Amazon said. The company is not the first to publish such warnings. International Business Machines Corp (IBM.N), a smaller player in the cloud, did so years ago.

They discussed how AI and machine learning are driving progress in many sectors and whether it's possible to safeguard AI's role in the patient's …

It can be difficult to understand exactly what's going on inside of a deep learning model, which is a real problem for companies concerned about bias, ethics, and explainability. Now IBM is developing something called AI FactSheets, which it describes as a nutrition label for deep learning that explains how models work and that can also detect bias. AI FactSheets is a new addition to Watson Open Scale that will provide a plain-language description of what's going on inside deep learning models. The software, which is expected to be generally available soon, can work with AI models developed by Watson Studio, or any other AI model accessible from a REST API. After being exposed to the model, AI FactSheets generates a PDF with information about bias, trust, and transparency aspects of a given deep learning model.

We present the "Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles" (ABOUT ML) project as an initiative to operationalize ML transparency and work towards a standard ML documentation practice. We make the case for the project's relevance and effectiveness in consolidating disparate efforts across a variety of stakeholders, as well as bringing in the perspectives of currently missing voices that will be valuable in shaping future conversations. We describe the details of the initiative and the gaps we hope this project will help address.