How to Create, Release, and Share Generative AI Responsibly

What are the responsibilities of generating? 

ACM NEWS

How to Create, Release, and Share Generative AI Responsibly

By MIT Technology Review,February 27, 2023

One of the most important elements of the guidelines is a pact by the companies to include and research ways to tell users when they are interacting with something generated by AI.

Credit: Stephanie Arnett/MITTR | Getty, Envato

A group of 10 companies, including OpenAI, TikTok, Adobe, the BBC, and the dating app Bumble, have signed up to a new set of guidelines on how to build, create, and share AI-generated content responsibly. 

The recommendations call for both the builders of the technology, such as OpenAI, and creators and distributors of digitally created synthetic media, such as the BBC and TikTok, to be more transparent about what the technology can and cannot do, and disclose when people might be interacting with this type of content. 

The voluntary recommendations were put together by the Partnership on AI (PAI), an AI research nonprofit, in consultation with over 50 organizations. PAI's partners include big tech companies as well as academic, civil society, and media organizations. The first 10 companies to commit to the guidance are Adobe, BBC, CBC/Radio-Canada, Bumble, OpenAI, TikTok, Witness, and synthetic-media startups Synthesia, D-ID, and Respeecher. 

"We want to ensure that synthetic media is not used to harm, disempower, or disenfranchise but rather to support creativity, knowledge sharing, and commentary," says Claire Leibowicz, PAI's head of AI and media integrity.

From MIT Technology Review

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On Editing Your Own Self Image

This came to my attention recently when I was asked for a simple self image to use on a startup web site.  How much should we edit? We have the tools now.  At very least by taking lots of images in many contexts.   But in the past, or now,  we could go to a professional portraitist and get advice on how to produce an image.  Below an intro, much more at the link. 

Editing Self-Image
By Ohad Fried, Jennifer Jacobs, Adam Finkelstein, Maneesh Agrawala
Communications of the ACM, March 2020, Vol. 63 No. 3, Pages 70-79
10.1145/3326601

Self-portraiture has become ubiquitous. Once an awkward feat, the "selfie"—a picture of one's self taken by one's self, typically at arm's length—is now easily accomplished with any smartphone, and often shared with others through social media. A 2013 poll indicated selfies accounted for one-third of photos taken within the 18-to-24 age group. Google estimated in 2014 that 93 billion selfies were taken per day just by Android users alone.  More recently, selfie taking has begun to influence human behavior in the physical world. Museums   have started to develop environments that cater specifically to Instagram and Snapchat users. Even facial plastic surgeons have observed an increase in the number of patients that seek plastic surgery specifically to look better in selfies (55% of surgeons had such patients in 2017, up 13% from 2016).2 Perhaps most strikingly, plastic surgeons have begun reporting a new phenomenon termed "Snapchat dysmorphia," where patients seek surgery to adjust their features to correspond to those achieved through digital filters ... "

Malevolence of the Use of Evolving Images

Good-non technical view of the current state of creating and evolving images. Somewhat like the 'photo shopping' enigma still going on, but more subtle and automated.   At first this seems like its not malevolent at all,  just amusing,  but it shows how an AI can be misled, depending how its used by people.

Malevolent Machine Learning   By Chris Edwards in the CACM

Communications of the ACM, December 2019, Vol. 62 No. 12, Pages 13-15
10.1145/3365573

At the start of the decade, deep learning restored the reputation of artificial intelligence (AI) following years stuck in a technological winter. Within a few years of becoming computationally feasible, systems trained on thousands of labeled examples began to exceed the performance of humans on specific tasks. One was able to decode road signs that had been rendered almost completely unreadable by the bleaching action of the sun, for example.

It just as quickly became apparent, however, that the same systems could just as easily be misled.

In 2013, Christian Szegedy and colleagues working at Google Brain found subtle pixel-level changes, imperceptible to a human, that extended across the image would lead to a bright yellow U.S. school bus being classified by a deep neural network (DNN) as an ostrich.

Two years later, Anh Nguyen, then a Ph.D. student at the University of Wyoming, and colleagues developed what they referre3d to as "evolved images." Some were regular patterns with added noise; others looked like the static from an analog TV broadcast. Both were just abstract images to humans, but these evolved images would be classified by DNNs trained on conventional photographs as cheetahs, armadillos, motorcycles, and whatever else the system had been trained to recognize. ... "