Freedom GPT is a groundbreaking open-source AI technology developed by Age of AI, an Austin-based AI venture capital firm. This state-of-the-art chatbot is programmed to recognize and prioritize ethical considerations without any censorship filter, unlike ChatGPT, which comes with censorship compliance and certain safety rules that prevent it from generating harmful or offensive content. FreedomGPT is built on Alpaca, an open-source model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations released by Stanford University researchers. This powerful foundation allows FreedomGPT to answer questions free from censorship or safety filters, while maintaining ethical considerations. FreedomGPT's ability to cater to controversial topics without safeguarding is one of its most distinguishing features.

Artificial intelligence (AI) has received significant attention in recent years, primarily due to breakthroughs in game playing, computer vision, and natural language processing that captured the imagination of the scientific community and the public at large. Many businesses, industries, and academic disciplines are now contemplating the application of AI to their own challenges. The federal government in the US and other countries have also invested significantly in advancing AI research and created funding initiatives and programs to promote greater collaboration across multiple communities. Some of the investment examples in the US include the establishment of the National AI Initiative Office, the launch of the National AI Research Resource Task Force, and more recently, the establishment of the National AI Advisory Committee. In 2021 INFORMS and ACM SIGAI joined together with the Computing Community Consortium (CCC) to organize a series of three workshops. The objective for this workshop series is to explore ways to exploit the synergies of the AI and Operations Research (OR) communities to transform decision making.

Generative A.I. models have emerged as versatile tools across diverse industries, with applications in privacy-preserving data sharing, computational art, personalization of products and services, and immersive entertainment. Here, we introduce a new privacy concern in the adoption and use of generative A.I. models: that of coincidental generation, where a generative model's output is similar enough to an existing entity, beyond those represented in the dataset used to train the model, to be mistaken for it. Consider, for example, synthetic portrait generators, which are today deployed in commercial applications such as virtual modeling agencies and synthetic stock photography. Due to the low intrinsic dimensionality of human face perception, every synthetically generated face will coincidentally resemble an actual person. Such examples of coincidental generation all but guarantee the misappropriation of likeness and expose organizations that use generative A.I. to legal and regulatory risk.

Preserving the individuals' privacy in sharing spatial-temporal datasets is critical to prevent re-identification attacks based on unique trajectories. Existing privacy techniques tend to propose ideal privacy-utility tradeoffs, however, largely ignore the fairness implications of mobility models and whether such techniques perform equally for different groups of users. The quantification between fairness and privacy-aware models is still unclear and there barely exists any defined sets of metrics for measuring fairness in the spatial-temporal context. In this work, we define a set of fairness metrics designed explicitly for human mobility, based on structural similarity and entropy of the trajectories. Under these definitions, we examine the fairness of two state-of-the-art privacy-preserving models that rely on GAN and representation learning to reduce the re-identification rate of users for data sharing. Our results show that while both models guarantee group fairness in terms of demographic parity, they violate individual fairness criteria, indicating that users with highly similar trajectories receive disparate privacy gain. We conclude that the tension between the re-identification task and individual fairness needs to be considered for future spatial-temporal data analysis and modelling to achieve a privacy-preserving fairness-aware setting.

We present EKILA; a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance -- determining the generative model and training data responsible for an AI-generated image. Furthermore, EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights, enabling a triangular relationship between the asset's Ownership, Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators to express agency over training consent and, through our attribution model, to receive apportioned credit, including royalty payments for the use of their assets in GenAI.

Fox Nation host Piers Morgan talks to Julian Assange's brother and father about this role in leaking classified military documents. People can never seem to agree on which label to give infamous info leaker Julian Assange. The WikiLeaks founder accused of publishing classified U.S. military information looks at up to 175 years in prison if extradited to the U.S. from his current location in a high-security U.K. prison. His father and brother are among those heralding him as a hero, reiterating their belief in a recent appearance on Fox Nation's "Piers Morgan: Uncensored." "Everything that Julian published was in the public interest and he partnered with these media organizations… so you're talking about all the largest media organizations around the world that published this exact same information," Gabriel Shipton, Assange's brother, said. JULIAN ASSANGE'S BROTHER AND FATHER SPEAK OUT OVER HIS DETAINMENT, CALL FOR CHARGES TO BE DROPPED WikiLeaks founder Julian Assange pauses as he makes a statement to media gathered outside the High Court in London, on Monday, Dec. 5, 2011.

It's never been about safety as much as it has control, serving and protecting only to the benefit of the status quo. In More than a Glitch, data journalist and New York University Associate Professor of Journalism Dr. Meredith Broussard, explores how and why we thought automating aspects of already racially-skewed legal, banking, and social systems would be a good idea. From facial recognition tech that doesn't work on dark-skinned folks to mortgage approval algorithms that don't work for dark-skinned folks, Broussard points to a dishearteningly broad array of initiatives that done more harm than good, regardless of their intention. In the excerpt below, Dr. Broussard looks at America's technochauavnistic history of predictive policing. Reprinted with permission from The MIT Press.

Walls and rules have never stopped prisoners from getting what they need. Drugs, phones and other contraband have been smuggled in by guards and visitors, flung over fences and even stashed inside hollowed-out pastries in care packages. Now, two men are accused of using an increasingly common technology to bypass prison walls: drones. Federal prosecutors in Fresno have charged Jose Enrique Oropeza and David Ramirez Jr. with using drones to drop loads of methamphetamine, heroin, cocaine, tobacco and cellphones into the yards of seven prisons across California. Oropeza was arrested March 29; Ramirez on April 4. Along with drug trafficking offenses, the men face airspace violations of operating unregistered aircraft and flying without a certificate, a redacted indictment shows.

Concerns about the growing abilities of chatbots trained on large language models, such as OpenAI's GPT-4, Google's Bard and Microsoft's Bing Chat, are making headlines. Experts warn of their ability to spread misinformation on a monumental scale, as well as the existential risk their development may pose to humanity. As if this isn't worrying enough, a third area of concern has opened up – illustrated by Italy's recent ban of ChatGPT on privacy grounds. The Italian data regulator has voiced concerns over the model used by ChatGPT owner OpenAI and announced it would investigate whether the firm had broken strict European data protection laws. Chatbots can be useful for work and personal tasks, but they collect vast amounts of data.

For the first time, OpenAI may face a lawsuit over ChatGPT-generated defamation. An Australian mayor named Brian Hood, who according to Reuters is peeved about the fact that ChatGPT wrongfully identified him as a guilty party in a "foreign bribery scandal involving a subsidiary of the Reserve Bank of Australia in the early 2000s," apparently claiming that Hood had even served prison time for his so-called crime. Hood was involved in the scandal -- but as the whistleblower, not the crime-doer. Yeah, we'd be pissed, too. Per Reuters, Hood's lawyers sent a "letter of concern" to OpenAI back on March 21 demanding that the company fix its chatbot's error within 28 days.