As they have a vital effect on social decision-making, AI algorithms not only should be accurate and but also should not pose unfairness against certain sensitive groups (e.g., non-white, women). Various specially designed AI algorithms to ensure trained AI models to be fair between sensitive groups have been developed. In this paper, we raise a new issue that between-group fair AI models could treat individuals in a same sensitive group unfairly. We introduce a new concept of fairness so-called within-group fairness which requires that AI models should be fair for those in a same sensitive group as well as those in different sensitive groups. We materialize the concept of within-group fairness by proposing corresponding mathematical definitions and developing learning algorithms to control within-group fairness and between-group fairness simultaneously. Numerical studies show that the proposed learning algorithms improve within-group fairness without sacrificing accuracy as well as between-group fairness.

ChatGPT was hailed as one 2022's most impressive technological innovations upon its release last November. The powerful artificial intelligence (AI) chatbot can generate text on almost any topic or theme, from a Shakespearean sonnet reimagined in the style of Megan Thee Stallion, to complex mathematical theorems described in language a 5 year old can understand. Within a week, it had more than a million users. ChatGPT's creator, OpenAI, is now reportedly in talks with investors to raise funds at a $29 billion valuation, including a potential $10 billion investment by Microsoft. That would make OpenAI, which was founded in San Francisco in 2015 with the aim of building superintelligent machines, one of the world's most valuable AI companies.

The ability of a document classifier to handle inputs that are drawn from a distribution different from the training distribution is crucial for robust deployment and generalizability. The RVL-CDIP corpus is the de facto standard benchmark for document classification, yet to our knowledge all studies that use this corpus do not include evaluation on out-of-distribution documents. In this paper, we curate and release a new out-of-distribution benchmark for evaluating out-of-distribution performance for document classifiers. Our new out-of-distribution benchmark consists of two types of documents: those that are not part of any of the 16 in-domain RVL-CDIP categories (RVL-CDIP-O), and those that are one of the 16 in-domain categories yet are drawn from a distribution different from that of the original RVL-CDIP dataset (RVL-CDIP-N). While prior work on document classification for in-domain RVL-CDIP documents reports high accuracy scores, we find that these models exhibit accuracy drops of between roughly 15-30% on our new out-of-domain RVL-CDIP-N benchmark, and further struggle to distinguish between in-domain RVL-CDIP-N and out-of-domain RVL-CDIP-O inputs. Our new benchmark provides researchers with a valuable new resource for analyzing out-of-distribution performance on document classifiers. Our new out-of-distribution data can be found at https://github.com/gxlarson/rvl-cdip-ood.

In this article, we propose the Artificial Intelligence Security Taxonomy to systematize the knowledge of threats, vulnerabilities, and security controls of machine-learning-based (ML-based) systems. We first classify the damage caused by attacks against ML-based systems, define ML-specific security, and discuss its characteristics. Next, we enumerate all relevant assets and stakeholders and provide a general taxonomy for ML-specific threats. Then, we collect a wide range of security controls against ML-specific threats through an extensive review of recent literature. Finally, we classify the vulnerabilities and controls of an ML-based system in terms of each vulnerable asset in the system's entire lifecycle.

We still do not live in an Age where Equality, Diversity and Inclusion (EDI) is by default. Instead, bias and discrimination are part of the everyday. Moreover, inequality in income and wealth, which transfers into inequality in opportunity, is rising according to the 2019 UN Global Sustainable Development Report. Sadly, EDI manifests not just in what we see, hear and experience, but also in the judgments made against us. Moreover, this is not confined to the people who are responsible for creating negative experiences.

In essence, Getty is claiming that Stability AI is benefiting from training its model on images published by Getty to the internet, without compensation. Getty images are published to the internet with a visible watermark; licensed images have the watermark removed. Getty said that Stability AI did not seek a license to use Getty's images. "We think similarly these generative models need to address the intellectual property rights of others, that's the crux of it," Craig Peters, the chief executive of Getty Images, told The Verge. "And we're taking this action to get clarity."

The intended audience for the workshop includes practitioners, researchers, and developers working to employ technology to improve access to justice. The workshop is intended be accessible to attorneys, computer scientists, legal aid workers, and social scientists. The workshop will address technological innovations intended improve access to justice and delivery of services and benefits to citizens and reduction of risks created by such technology, including the due-process, bias, and privacy concerns that can arise from automated support of self-represented litigants. It will also examine human-computer interface issues concerning how and when self-represented litigants (SRLs) should use AI systems.

"Getty Images believes artificial intelligence has the potential to stimulate creative endeavors." "Getty Images provided licenses to leading technology innovators for purposes related to training artificial intelligence systems in a manner that respects personal and intellectual property rights," the company continued. "Stability AI did not seek any such license from Getty Images and instead, we believe, chose to ignore viable licensing options and long‑standing legal protections in pursuit of their stand‑alone commercial interests." Furthermore, Peters explained that the company is not seeking monetary damages in this case so as much as it is hoping to establish a favorable precedent for future litigation. Text-to-image generation tools like Stable Diffusion, Dall-E and Midjourney don't create the artwork that they produce in the same way people do -- there is no imagination from which these ideas can spring forth.

As a rather commercially successful author once wrote, "the night is dark and full of terrors, the day bright and beautiful and full of hope." It's fitting imagery for AI, which like all tech has its upsides and downsides. Art-generating models like Stable Diffusion, for instance, have led to incredible outpourings of creativity, powering apps and even entirely new business models. On the other hand, its open source nature lets bad actors use it to create deepfakes at scale -- all while artists protest that it's profiting off of their work. Will regulation rein in the worst of what AI brings, or are the floodgates open?

During the development of Stable Diffusion, Stability AI trained the neural network on a large dataset of images. "These copies were made without the knowledge or consent of the artists."