In addition to concerns about AI-generated content taking human jobs, it seems there are also questions regarding the material these tools are trained on. AI-powered content-generating tools have seen their popularity explode in recent months, but it hasn't stopped the controversy that surrounds them. That's been especially true of systems that create art. The problem was highlighted last September when the Colorado State Fair's contest for emerging digital artists was by Jason M. Allen, who created his entry using Midjourney. Midjourney and Stable Diffusion are trained on billions of images.

Check out all the on-demand sessions from the Intelligent Security Summit here. Three artists launched the lawsuit through the Joseph Saveri Law Firm and lawyer and designer/programmer Matthew Butterick, who recently teamed up to file a similar lawsuit against Microsoft, GitHub and OpenAI, related to the generative AI programming model CoPilot. The artists claim that Stable Diffusion and Midjourney scraped the Internet to copy billions of works without permission, including theirs, which then are used to produce "derivative works." In a blog post, Butterick described Stable Diffusion as a "parasite that, if allowed to proliferate, will cause irreparable harm to artists, now and in the future."

The artists taking action -- Sarah Anderson, Kelly McKernan, Karla Ortiz -- "seek to end this blatant and enormous infringement of their rights before their professions are eliminated by a computer program powered entirely by their hard work," according to the official text of the complaint filed to the court. Using tools like Stability AI's Stable Diffusion, Midjourney, or the DreamUp generator on DeviantArt, people can type phrases to create artwork similar to living artists. Since the mainstream emergence of AI image synthesis in the last year, AI-generated artwork has been highly controversial among artists, sparking protests and culture wars on social media. Enlarge/ A selection of images generated by Stable Diffusion. Knowledge of how to render them came from scraped images on the web.

Algorithm fairness in the application of artificial intelligence (AI) is essential for a better society. As the foundational axiom of social mechanisms, fairness consists of multiple facets. Although the machine learning (ML) community has focused on intersectionality as a matter of statistical parity, especially in discrimination issues, an emerging body of literature addresses another facet -- monotonicity. Based on domain expertise, monotonicity plays a vital role in numerous fairness-related areas, where violations could misguide human decisions and lead to disastrous consequences. In this paper, we first systematically evaluate the significance of applying monotonic neural additive models (MNAMs), which use a fairness-aware ML algorithm to enforce both individual and pairwise monotonicity principles, for the fairness of AI ethics and society. We have found, through a hybrid method of theoretical reasoning, simulation, and extensive empirical analysis, that considering monotonicity axioms is essential in all areas of fairness, including criminology, education, health care, and finance. Our research contributes to the interdisciplinary research at the interface of AI ethics, explainable AI (XAI), and human-computer interactions (HCIs). By evidencing the catastrophic consequences if monotonicity is not met, we address the significance of monotonicity requirements in AI applications. Furthermore, we demonstrate that MNAMs are an effective fairness-aware ML approach by imposing monotonicity restrictions integrating human intelligence.

Learning from raw high dimensional data via interaction with a given environment has been effectively achieved through the utilization of deep neural networks. Yet the observed degradation in policy performance caused by imperceptible worst-case policy dependent translations along high sensitivity directions (i.e. adversarial perturbations) raises concerns on the robustness of deep reinforcement learning policies. In our paper, we show that these high sensitivity directions do not lie only along particular worst-case directions, but rather are more abundant in the deep neural policy landscape and can be found via more natural means in a black-box setting. Furthermore, we show that vanilla training techniques intriguingly result in learning more robust policies compared to the policies learnt via the state-of-the-art adversarial training techniques. We believe our work lays out intriguing properties of the deep reinforcement learning policy manifold and our results can help to build robust and generalizable deep reinforcement learning policies.

The Special Issue will focus on the opportunities and challenges of automation and artificial intelligence (AI) applications for human resource management (HRM). Building on an increasing evidence base of its benefits for the business and employee experience (Budhwar et al., 2022; Malik et al., 2020, 2021; Nguyen & Malik, 2021) as well as threats to future of work and employment (Huang & Rust, 2018), this call aims to address several attendant challenges for HR managers, leaders and employees for responsible and sustainable deployment AI in the HR profession and the communities it will serve (Charlwood & Guenole, 2021). Although AI can make moral judgements, but the bigger question is whether we should allow it to make such judgements (Wheeler, 2021) as there are ethical, moral and legal issues at stake in the adoption, design and implementation of AI-enabled HRM applications and algorithmic decision-making (Duan, Edwards, & Dwivedi, 2019). Increasingly, questions about responsible management, decent work, ethics, trust between the interactions humans have with AI or humanoids as co-workers are topics of great scholarly pursuit (Agar, 2019; De Stefano, 2019; Glikson & Woolley, 2020). Submissions are made using ScholarOne Manuscripts.

A class action lawsuit is filed in the US against Midjourney and Stability AI as well as the art platform DeviantArt. US artists Sarah Andersen, Kelly McKernan, and Karla Ortiz file a class action lawsuit in California against Stability AI (Stable Diffusion) and Midjourney. The artists are seeking damages and an injunction to prevent future harm. Art platform DeviantArt is also accused of providing thousands or even millions of images from the LAION dataset for Stable Diffusion's training. Instead of siding with the artists, DeviantArt put DreamUp online, an AI art app based on Stable Diffusion, according to the plaintiffs.

Check out all the on-demand sessions from the Intelligent Security Summit here. As the use of AI becomes more and more ubiquitous across industries, becoming as widespread as electricity and clean drinking water, the conversation around the new technology is beginning to move from how to implement AI to how to implement it responsibly. How does AI differ from other software technologies that we have been using to build products, and is there a need for new regulations and new compliance frameworks? That conversation hasn't yet spurred most organizations to action. In a recent survey by Wakefield Research and Juniper Networks, 63% of companies said that they are at least most of the way to their planned AI adoption goals -- but only 9% have fully mature governance policies.

A smartphone app that tells a defendant what to say in court using artificial intelligence has been used for the first time - and is a lot cheaper than a lawyer. It is the first time artificial intelligence (AI) has been used in a trial anywhere in the world. The neural network will listen to all speeches from witnesses, lawyers and the judge. The defendant will be told exactly what to say via an earpiece - sticking to only those words. Legal history is being made over a speeding fine.

GitHub Next has unveiled a project called Code Brushes which uses machine learning to update code "like painting with Photoshop". Using the feature, developers can "brush" over their code to see it update in real-time. Several different brushes are included to achieve various aims. For example, one brush makes code more readable--especially important when coding as part of a team or contributing to open-source projects. Code Brushes also supports the creation of custom brushes.