An assurance case is a structured argument, typically produced by safety engineers, to communicate confidence that a critical or complex system, such as an aircraft, will be acceptably safe within its intended context. Assurance cases often inform third party approval of a system. One emerging proposition within the trustworthy AI and autonomous systems (AI/AS) research community is to use assurance cases to instil justified confidence that specific AI/AS will be ethically acceptable when operational in well-defined contexts. This paper substantially develops the proposition and makes it concrete. It brings together the assurance case methodology with a set of ethical principles to structure a principles-based ethics assurance argument pattern. The principles are justice, beneficence, non-maleficence, and respect for human autonomy, with the principle of transparency playing a supporting role. The argument pattern, shortened to the acronym PRAISE, is described. The objective of the proposed PRAISE argument pattern is to provide a reusable template for individual ethics assurance cases, by which engineers, developers, operators, or regulators could justify, communicate, or challenge a claim about the overall ethical acceptability of the use of a specific AI/AS in a given socio-technical context. We apply the pattern to the hypothetical use case of an autonomous robo-taxi service in a city centre.

The UK should bar technology developers from working on advanced artificial intelligence tools unless they have a licence to do so, Labour has said. Ministers should introduce much stricter rules around companies training their AI products on vast datasets of the kind used by OpenAI to build ChatGPT, Lucy Powell, Labour's digital spokesperson, told the Guardian. Her comments come amid a rethink at the top of government over how to regulate the fast-moving world of AI, with the prime minister, Rishi Sunak, acknowledging it could pose an "existential" threat to humanity. One of the government's advisers on artificial intelligence also said on Monday that humanity could have only two years before AI is able to outwit people, the latest in a series of stark warnings about the threat posed by the fast-developing technology. Powell said: "My real point of concern is the lack of any regulation of the large language models that can then be applied across a range of AI tools, whether that's governing how they are built, how they are managed or how they are controlled."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A small plane had mechanical problems with its autopilot system before it crashed in Arkansas last month and killed a former Mississippi state senator who was flying it, according to a preliminary report by the National Transportation Safety Board. Johnny Morgan, 76, of Oxford, Mississippi, served in the Mississippi Senate from 1984 to 1992. He was the only person aboard the twin-engine Beech King Air E-90 plane when it crashed May 17 in a wooded area in northwestern Arkansas, south of Fayetteville.

Companies deploying AI tools with the ability to generate disinformation, such as ChatGPT and Bard, should label such content as part of their efforts to combat fake news, according to European Commission deputy head Vera Jourova. Unveiled late last year, Microsoft-backed OpenAI's ChatGPT has become the fastest-growing consumer application in history and set off a race among tech companies to bring generative AI products to market. Concerns however are mounting about potential abuse of the technology and the possibility that bad actors and even governments may use it to produce far more disinformation than before. "Signatories who integrate generative AI into their services like Bingchat for Microsoft, Bard for Google should build in necessary safeguards that these services cannot be used by malicious actors to generate disinformation," Jourova told a press conference on Monday. "Signatories who have services with a potential to disseminate AI-generated disinformation should, in turn, put in place technology to recognise such content and clearly label this to users," she said.

SEARCH SUSPENDED - No survivors found after plane violates DC airspace, scrambles military before crashing in Virginia. LAND OF THE FREE? - Capitol Police spark outrage as youth choir's national anthem performance halted. 'BEST SOLUTION' - AI could help solve NJ missing child mystery, become model for cold case probes. RECORD SCRATCH - 'American Pie' icon Don McLean weighs in on AI's effect on the music industry. WHAT'S IN STORE - Target backs organization pushing US demilitarization, Mt. 'IT HAS TO BE JOE BIDEN' - Ex-FBI director James Comey speaks out on 2024 race.

Sign up to receive our weekly newsletter of the best New Yorker podcasts. David Remnick sits down with Sam Altman, the C.E.O. of OpenAI, which created ChatGPT, GPT-4, and other artificial-intelligence programs. A.I. is a tool, Altman emphasizes, that streamlines human work and quickens the pace of scientific advancement. But he claims to empathize with concerns about the emerging technology. "Even if you don't believe in any of the sci-fi stories," he tells Remnick, "you could still be freaked out about the level of change that this is going to bring society and the compressed time frame in which that's going to happen."

Recently, the White House decided that appointing an unqualified, politicized leader is perfect for tackling the complex issue of AI regulation. Kamala Harris, who has now become the AI czar, will likely lead America into a very gloomy future. The nation must correct this blunder before it's too late. We can only solve a problem by asking the right questions and Harris and the polarized Congress are clearly unable to do so. The United States must replace her with an unbiased committee of experts who can protect and fully develop effective AI regulations.

Retrosynthesis is a procedure where a target molecule is transformed into potential reactants and thus the synthesis routes can be identified. Recently, computational approaches have been developed to accelerate the design of synthesis routes. In this paper, we develop a generative framework $\mathsf{G^2Retro}$ for one-step retrosynthesis prediction. $\mathsf{G^2Retro}$ imitates the reversed logic of synthetic reactions. It first predicts the reaction centers in the target molecules (products), identifies the synthons needed to assemble the products, and transforms these synthons into reactants. $\mathsf{G^2Retro}$ defines a comprehensive set of reaction center types, and learns from the molecular graphs of the products to predict potential reaction centers. To complete synthons into reactants, $\mathsf{G^2Retro}$ considers all the involved synthon structures and the product structures to identify the optimal completion paths, and accordingly attaches small substructures sequentially to the synthons. Here we show that $\mathsf{G^2Retro}$ is able to better predict the reactants for given products in the benchmark dataset than the state-of-the-art methods.

To mitigate the bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it often comes at the expense of model performance. Understanding such tradeoffs, therefore, underlies the design of fair algorithms. To this end, this paper provides a complete characterization of the inherent tradeoff of demographic parity on classification problems, under the most general multi-group, multi-class, and noisy setting. Specifically, we show that the minimum error rate achievable by randomized and attribute-aware fair classifiers is given by the optimal value of a Wasserstein-barycenter problem. On the practical side, our findings lead to a simple post-processing algorithm that derives fair classifiers from score functions, which yields the optimal fair classifier when the score is Bayes optimal. We provide suboptimality analysis and sample complexity for our algorithm, and demonstrate its effectiveness on benchmark datasets.

Existing regulations prohibit model developers from accessing protected attributes (gender, race, etc.), often resulting in fairness assessments on populations without knowing their protected groups. In such scenarios, institutions often adopt a separation between the model developers (who train models with no access to the protected attributes) and a compliance team (who may have access to the entire dataset for auditing purposes). However, the model developers might be allowed to test their models for bias by querying the compliance team for group fairness metrics. In this paper, we first demonstrate that simply querying for fairness metrics, such as statistical parity and equalized odds can leak the protected attributes of individuals to the model developers. We demonstrate that there always exist strategies by which the model developers can identify the protected attribute of a targeted individual in the test dataset from just a single query. In particular, we show that one can reconstruct the protected attributes of all the individuals from O(Nk \log( n /Nk)) queries when Nk<