Regulation, legal liabilities, and societal concerns challenge the adoption of AI in safety and security-critical applications. One of the key concerns is that adversaries can cause harm by manipulating model predictions without being detected. Regulation hence demands an assessment of the risk of damage caused by adversaries. Yet, there is no method to translate this high-level demand into actionable metrics that quantify the risk of damage. In this article, we propose a method to model and statistically estimate the probability of damage arising from adversarial attacks. We show that our proposed estimator is statistically consistent and unbiased. In experiments, we demonstrate that the estimation results of our method have a clear and actionable interpretation and outperform conventional metrics. We then show how operators can use the estimation results to reliably select the model with the lowest risk.

In a new study, University of Minnesota law professors used ChatGPT AI chatbot to answer graduate exams at four courses in their school. The AI passed all four, but with an average grade of C . The University of Minnesota group noted ChatGPT was good at addressing "basic legal rules" and summaries, but it floundered when trying to pinpoint issues relevant in a case. When faced with business management questions in a different study, the generator was "amazing" with simple operations management and process analysis questions, but it couldn't handle advanced process questions. It even made mistakes with sixth-grade-level math – something other AI authors have struggled with.

National Institute of Standards and Technology (NIST), a US-based federal agency responsible for building technology standards, has released artificial intelligence risk management framework (AI RMF 1.0), which can be used by companies to build and use AI systems in an ethical and risk-free manner. Developed in collaboration with private and public sector organisations, AI RMF framework is voluntary, which means it's usage is not binding on any company. However, NIST director Laurie E. Locascio believes that it can help large and small organisations across sectors manage their AI related risks more effectively. The framework is part of NIST's larger goal of "cultivating trust" in AI technologies within all communities, added Locascio. "It should accelerate AI innovation and growth while advancing -- rather than restricting or damaging -- civil rights, civil liberties and equity for all," Don Graves, Deputy Commerce Secretary, said in a statement.

Social media is a modern person's digital voice to project and engage with new ideas and mobilise communities $\unicode{x2013}$ a power shared with extremists. Given the societal risks of unvetted content-moderating algorithms for Extremism, Radicalisation, and Hate speech (ERH) detection, responsible software engineering must understand the who, what, when, where, and why such models are necessary to protect user safety and free expression. Hence, we propose and examine the unique research field of ERH context mining to unify disjoint studies. Specifically, we evaluate the start-to-finish design process from socio-technical definition-building and dataset collection strategies to technical algorithm design and performance. Our 2015-2021 51-study Systematic Literature Review (SLR) provides the first cross-examination of textual, network, and visual approaches to detecting extremist affiliation, hateful content, and radicalisation towards groups and movements. We identify consensus-driven ERH definitions and propose solutions to existing ideological and geographic biases, particularly due to the lack of research in Oceania/Australasia. Our hybridised investigation on Natural Language Processing, Community Detection, and visual-text models demonstrates the dominating performance of textual transformer-based algorithms. We conclude with vital recommendations for ERH context mining researchers and propose an uptake roadmap with guidelines for researchers, industries, and governments to enable a safer cyberspace.

University of Stirling, United Kingdom vanicka.arora@stir.ac.uk Abstract As AI technologies are rolled out into healthcare, academia, human resources, law, and a multitude of other domains, they become de-facto arbiters of truth. But truth is highly contested, with many different definitions and approaches. It then investigates the production of truth in InstructGPT, a large language model, highlighting how data harvesting, model architectures, and social feedback mechanisms weave together disparate understandings of veracity. It conceptualizes this performance as an operationalization of truth, where distinct, often conflicting claims are smoothly synthesized and confidently presented into truth-statements. We argue that these same logics and inconsistencies play out in Instruct's successor, ChatGPT, reiterating truth as a non-trivial problem. We suggest that enriching sociality and thickening "reality" are two promising vectors for enhancing the truth-evaluating capacities of future language models. We conclude, however, by stepping back to consider AI truth-telling as a social practice: what kind of "truth" do we as listeners desire? OpenAI's latest language model appeared to We stress then that truth in AI is not just technical but be powerful and almost magical, generating news articles, also social, cultural, and political, drawing on particular writing poetry, and explaining arcane concepts norms and values. But a week later, the coding the technical matters: translating truth theories into site StackOverflow banned all answers produced actionable architectures and processes updates them by the model. "The primary problem," explained in significant ways. These disparate sociotechnical the staff, "is that while the answers which ChatGPT forces coalesce into a final AI model which purports produces have a high rate of being incorrect, they typically to tell the truth--and in doing so, our understanding look like they might be good and the answers of "truth" is remade. "The ideal of truth is a fallacy are very easy to produce" (Vincent 2022). For a site for semantic interpretation and needs to be changed," aiming to provide correct answers to coding problems, suggested two AI researchers (Welty and Aroyo 2015).

As research and practice in artificial intelligence (A.I.) grow in leaps and bounds, the resources necessary to sustain and support their operations also grow at an increasing pace. While innovations and applications from A.I. have brought significant advances, from applications to vision and natural language to improvements to fields like medical imaging and materials engineering, their costs should not be neglected. As we embrace a world with ever-increasing amounts of data as well as research and development of A.I. applications, we are sure to face an ever-mounting energy footprint to sustain these computational budgets, data storage needs, and more. But, is this sustainable and, more importantly, what kind of setting is best positioned to nurture such sustainable A.I. in both research and practice? In this paper, we outline our outlook for Green A.I. -- a more sustainable, energy-efficient and energy-aware ecosystem for developing A.I. across the research, computing, and practitioner communities alike -- and the steps required to arrive there. We present a bird's eye view of various areas for potential changes and improvements from the ground floor of AI's operational and hardware optimizations for datacenters/HPCs to the current incentive structures in the world of A.I. research and practice, and more. We hope these points will spur further discussion, and action, on some of these issues and their potential solutions.

On a recent episode of Dr. Phil, the host spoke with some of Jeffrey Dahmer's victims and showed them an interview he filmed with the father of one of America's most infamous serial killers. A 21-year-old Louisiana man has been sentenced to 45 years in prison after plotting a Jeffrey Dahmer-like scheme to meet men on the gay dating app Grindr and kill them, according to federal officials. Chance Seneca of Lafayette Parish targeted one particular victim, as well as other gay men, through the app in 2020 because of their sexual orientation and gender, the Justice Department said. "The facts of this case are truly shocking, and the defendant's decision to specifically target gay men is a disturbing reminder of the unique prejudices and dangers facing the LGBTQ community today," Assistant Attorney General Kristen Clarke of the Justice Department's Civil Rights Division said in a Wednesday statement. Clarke continued: "The internet should be accessible and safe for all Americans, regardless of their gender or sexual orientation. We will continue to identify and intercept the predators who weaponize online platforms to target LGBTQ victims and carry out acts of violence and hate."

A "robot" lawyer powered by artificial intelligence was set to be the first of its kind to help a defendant fight a traffic ticket in court next month. But the experiment has been scrapped after "State Bar prosecutors" threatened the man behind the company that created the chatbot with prison time. Joshua Browder, CEO of DoNotPay, on Wednesday tweeted that his company "is postponing our court case and sticking to consumer rights." Bad news: after receiving threats from State Bar prosecutors, it seems likely they will put me in jail for 6 months if I follow through with bringing a robot lawyer into a physical courtroom. Browder also said he will not be sending the company's robot lawyer to court.

Kelly Conlon, who was kept from seeing the Rockettes, and Sam Davis, who was barred from attending a Rangers game, speak out against MSG Entertainment and James Dolan for their use of facial recognition on'America's Newsroom.' The latest development to come from Madison Square Garden and CEO James Dolan is one that will likely leave fans very unhappy. Dolan threatened to cancel all alcohol sales at The Garden – he mentioned a Rangers game – as a response to the New York State Liquor Authority, which is currently investigating Dolan regarding his facial recognition technology that has resulted in several bans against lawyers who are suing him. Dolan said it all on Fox 5's "Good Day New York." with Rosanna Scotto. James Dolan, left, and head coach Tom Thibodeau of the New York Knicks attend the NBA Summer League at the Thomas and Mack Center on July 8, 2022, in Las Vegas.

There's plenty of concern that OpenAI's ChatGPT could help students cheat on tests, but just how well would the chatbot fare if you asked it to write a graduate-level exam? It would pass -- if only just. In a newly published study, University of Minnesota law professors had ChatGPT produce answers for graduate exams at four courses in their school. The AI passed all four, but with an average grade of C . In another recent paper, Wharton School of Business professor Christian Terwiesch found that ChatGPT passed a business management exam with a B to B- grade.