Large language models produce human-like text that drives a growing number of applications. However, recent literature and, increasingly, real world observations, have demonstrated that these models can generate language that is toxic, biased, untruthful or otherwise harmful. Though work to evaluate language model harms is under way, translating foresight about which harms may arise into rigorous benchmarks is not straightforward. To facilitate this translation, we outline six ways of characterizing harmful text which merit explicit consideration when designing new benchmarks. We then use these characteristics as a lens to identify trends and gaps in existing benchmarks. Finally, we apply them in a case study of the Perspective API, a toxicity classifier that is widely used in harm benchmarks. Our characteristics provide one piece of the bridge that translates between foresight and effective evaluation.

We propose to design and build an algorithm that will use a Convolutional Neural Network (CNN) and observations from the Unistellar network to reliably detect asteroid occultations. The Unistellar Network, made of more than 10,000 digital telescopes owned by citizen scientists, and is regularly used to record asteroid occultations. In order to process the increasing amount of observational produced by this network, we need a quick and reliable way to analyze occultations. In an effort to solve this problem, we trained a CNN with artificial images of stars with twenty different types of photometric signals. Inputs to the network consists of two stacks of snippet images of stars, one around the star that is supposed to be occulted and a reference star used for comparison. We need the reference star to distinguish between a true occultation and artefacts introduced by poor atmospheric condition. Our Occultation Detection Neural Network (ODNet), can analyze three sequence of stars per second with 91\% of precision and 87\% of recall. The algorithm is sufficiently fast and robust so we can envision incorporating onboard the eVscopes to deliver real-time results. We conclude that citizen science represents an important opportunity for the future studies and discoveries in the occultations, and that application of artificial intelligence will permit us to to take better advantage of the ever-growing quantity of data to categorize asteroids.

Graph-structured data is ubiquitous in practice and often processed using graph neural networks (GNNs). With the adoption of recent laws ensuring the ``right to be forgotten'', the problem of graph data removal has become of significant importance. To address the problem, we introduce the first known framework for \emph{certified graph unlearning} of GNNs. In contrast to standard machine unlearning, new analytical and heuristic unlearning challenges arise when dealing with complex graph data. First, three different types of unlearning requests need to be considered, including node feature, edge and node unlearning. Second, to establish provable performance guarantees, one needs to address challenges associated with feature mixing during propagation. The underlying analysis is illustrated on the example of simple graph convolutions (SGC) and their generalized PageRank (GPR) extensions, thereby laying the theoretical foundation for certified unlearning of GNNs. Our empirical studies on six benchmark datasets demonstrate excellent performance-complexity trade-offs when compared to complete retraining methods and approaches that do not leverage graph information. For example, when unlearning $20\%$ of the nodes on the Cora dataset, our approach suffers only a $0.1\%$ loss in test accuracy while offering a $4$-fold speed-up compared to complete retraining. Our scheme also outperforms unlearning methods that do not leverage graph information with a $12\%$ increase in test accuracy for a comparable time complexity.

Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education. Synthetic data generation algorithms with privacy guarantees are emerging as a paradigm to break this data logjam. Existing approaches, however, assume that the data holders supply their raw data to a trusted curator, who uses it as fuel for synthetic data generation. This severely limits the applicability, as much of the valuable data in the world is locked up in silos, controlled by entities who cannot show their data to each other or a central aggregator without raising privacy concerns. To overcome this roadblock, we propose the first solution in which data holders only share encrypted data for differentially private synthetic data generation. Data holders send shares to servers who perform Secure Multiparty Computation (MPC) computations while the original data stays encrypted. We instantiate this idea in an MPC protocol for the Multiplicative Weights with Exponential Mechanism (MWEM) algorithm to generate synthetic data based on real data originating from many data holders without reliance on a single point of failure.

WASHINGTON--Federal prosecutors and securities regulators are investigating whether Tesla Inc. misled consumers and investors about how its advanced driver-assistance system performed, according to a person familiar with the matter. The Justice Department is looking at statements that Tesla and its executives made about the safety and functionality of the system known as Autopilot, the person said. The Securities and Exchange Commission is conducting a similar civil investigation, people familiar with the matter said. The company's Autopilot system is among the most well-known advanced driver-assistance systems and comes standard on new Teslas. The technology helps drivers with tasks such as steering and maintaining a safe distance from other vehicles on the highway, but does not make cars autonomous.

Police should be banned from using live facial recognition technology in all public spaces because they are breaking ethical standards and human rights laws, a study has concluded. LFR involves linking cameras to databases containing photos of people. Images from the cameras can then be checked against those photos to see if they match. British police have experimented with the technology, believing it can help combat crime and terrorism. But in some cases, courts have found against the way police have used LFR, and how they have dealt with infringements of the privacy rights of people walking in the streets where the technology has been used.

The Department of Justice is reportedly investigating whether Tesla has misled customers and investors by claiming that its Autopilot technology enables full-fledged self-driving capabilities. According to Reuters, the DOJ launched the probe last year following over a dozen crashes, including fatal ones, in which Autopilot was activated. Prosecutors in Washington and San Francisco are examining if Tesla had made unsupported full self-driving claims about the technology, and they could ultimately pursue criminal charges or seek sanctions. But they could also shut the probe down without doing anything if they determine that Tesla hasn't done anything wrong. Back in August, reports came out that the California DMV had filed complaints against the automaker with the California Office of Administrative Hearings.

Social media users may trust artificial intelligence (AI) as much as human editors to flag hate speech and harmful content, according to researchers at Penn State. The researchers said that when users think about positive attributes of machines, like their accuracy and objectivity, they show more faith in AI. However, if users are reminded about the inability of machines to make subjective decisions, their trust is lower. The findings may help developers design better AI-powered content curation systems that can handle the large amounts of information currently being generated while avoiding the perception that the material has been censored, or inaccurately classified, said S. Shyam Sundar, James P. Jimirro Professor of Media Effects in the Donald P. Bellisario College of Communications and co-director of the Media Effects Research Laboratory. "There's this dire need for content moderation on social media and more generally, online media," said Sundar, who is also an affiliate of Penn State's Institute for Computational and Data Sciences.

Professors Mireille Hildebrandt (VUB, Brussels) and Catherine Régis (Université de Montréal – Mila, Canada) will present some of the major current questions around Law and Artificial Intelligence. How to bring AI applications under the rule of law, and what fundamental rights assessments must be put in place? Does the GDPR set the right tone and how can AI development be aligned with individual rights and freedoms, including rights to non-discrimination, privacy, due process and the presumption of innocence? Mireille Hildebrandt will focus on the concepts of robust AI (in terms of reliability and resilience) and robust law (in terms of the rule of law), and discuss how robust AI could support the rule of law and vice versa. She will also give an overview of future developments in the legal regulation of AI, and explore the role of ethical guidelines and charters, through their formalization process and their potential for legal developments.

In September 2021, the "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Michael Littman of Brown University. The report, entitled "Gathering Strength, Gathering Storms," answers a set of 14 questions probing critical areas of AI development addressing the major risks and dangers of AI, its effects on society, its public perception and the future of the field. The report concludes that AI has made a major leap from the lab to people's lives in recent years, which increases the urgency to understand its potential negative effects. The questions were developed by the AI100 Standing Committee, chaired by Peter Stone of the University of Texas at Austin, consisting of a group of AI leaders with expertise in computer science, sociology, ethics, economics, and other disciplines.