This paper investigates the relationship between law and eXplainable Artificial Intelligence (XAI). While there is much discussion about the AI Act, for which the trilogue of the European Parliament, Council and Commission recently concluded, other areas of law seem underexplored. This paper focuses on European (and in part German) law, although with international concepts and regulations such as fiduciary plausibility checks, the General Data Protection Regulation (GDPR), and product safety and liability. Based on XAI-taxonomies, requirements for XAI-methods are derived from each of the legal bases, resulting in the conclusion that each legal basis requires different XAI properties and that the current state of the art does not fulfill these to full satisfaction, especially regarding the correctness (sometimes called fidelity) and confidence estimates of XAI-methods.

We audited large language models (LLMs) for their ability to create evidence-based and stylistic counter-arguments to posts from the Reddit ChangeMyView dataset. We benchmarked their rhetorical quality across a host of qualitative and quantitative metrics and then ultimately evaluated them on their persuasive abilities as compared to human counter-arguments. Our evaluation is based on Counterfire: a new dataset of 32,000 counter-arguments generated from large language models (LLMs): GPT-3.5 Turbo and Koala and their fine-tuned variants, and PaLM 2, with varying prompts for evidence use and argumentative style. GPT-3.5 Turbo ranked highest in argument quality with strong paraphrasing and style adherence, particularly in `reciprocity' style arguments. However, the stylistic counter-arguments still fall short of human persuasive standards, where people also preferred reciprocal to evidence-based rebuttals. The findings suggest that a balance between evidentiality and stylistic elements is vital to a compelling counter-argument. We close with a discussion of future research directions and implications for evaluating LLM outputs.

Hyperspectral imaging (HSI) is a key technology for earth observation, surveillance, medical imaging and diagnostics, astronomy and space exploration. The conventional technology for HSI in remote sensing applications is based on the push-broom scanning approach in which the camera records the spectral image of a stripe of the scene at a time, while the image is generated by the aggregation of measurements through time. In real-world airborne and spaceborne HSI instruments, some empty stripes would appear at certain locations, because platforms do not always maintain a constant programmed attitude, or have access to accurate digital elevation maps (DEM), and the travelling track is not necessarily aligned with the hyperspectral cameras at all times. This makes the enhancement of the acquired HS images from incomplete or corrupted observations an essential task. We introduce a novel HSI inpainting algorithm here, called Hyperspectral Equivariant Imaging (Hyper-EI). Hyper-EI is a self-supervised learning-based method which does not require training on extensive datasets or access to a pre-trained model. Experimental results show that the proposed method achieves state-of-the-art inpainting performance compared to the existing methods.

Large language models (LLMs) are a promising venue for natural language understanding and generation tasks. However, current LLMs are far from reliable: they are prone to generate non-factual information and, more crucially, to contradict themselves when prompted to reason about beliefs of the world. These problems are currently addressed with large scale fine-tuning or by delegating consistent reasoning to external tools. In this work, we strive for a middle ground and introduce a training objective based on principled probabilistic reasoning that teaches a LLM to be consistent with external knowledge in the form of a set of facts and rules. Fine-tuning with our loss on a limited set of facts enables our LLMs to be more logically consistent than previous baselines and allows them to extrapolate to unseen but semantically similar factual knowledge more systematically.

Motivated by equilibrium models of labor markets, we develop a formulation of causal strategic classification in which strategic agents can directly manipulate their outcomes. As an application, we compare employers that anticipate the strategic response of a labor force with employers that do not. We show through a combination of theory and experiment that employers with performatively optimal hiring policies improve employer reward, labor force skill level, and in some cases labor force equity. On the other hand, we demonstrate that performative employers harm labor force utility and fail to prevent discrimination in other cases. In many applications of predictive modeling, the model itself may affect the distribution of samples on which it has to make predictions; this problem is known as strategic classification (Hardt et al., 2015; Brückner et al., 2012) or performative prediction (Perdomo et al., 2020). For example, traffic predictions affect route decisions, which ultimately impact traffic. Such situations can arise in a variety of applications; a common theme is that the samples correspond to strategic agents with an incentive to "game the system" and elicit a desired outcome from the model. In the standard strategic classification setup, the agents are allowed to modify their features, but they do not modify the outcome that the predictive model targets. An example of this is spam classification: spammers craft their messages (e.g. There is a line of work on causal strategic classification that seeks to generalize this setup by allowing the agents to change both their features and outcomes, usually by incorporating a causal model between the two (Miller et al., 2020; Kleinberg and Raghavan, 2020; Haghtalab et al., 2023; Horowitz and Rosenfeld, 2023).

Meta Platforms on Thursday released early versions of its latest large language model, Llama 3, and an image generator that updates pictures in real time while users type prompts, as it races to catch up to generative AI market leader OpenAI. The models will be integrated into virtual assistant Meta AI, which the company is pitching as the most sophisticated of its free-to-use peers. The assistant will be given more prominent billing within Meta's Facebook, Instagram, WhatsApp and Messenger apps as well as a new standalone website that positions it to compete more directly with Microsoft-backed OpenAI's breakout hit ChatGPT. The announcement comes as Meta has been scrambling to push generative AI products out to its billions of users to challenge OpenAI's leading position on the technology, involving an overhaul of computing infrastructure and the consolidation of previously distinct research and product teams. The social media giant equipped Llama 3 with new computer coding capabilities and fed it images as well as text this time, though for now the model will output only text, Chris Cox, Meta's chief product officer, said in an interview.

The administration of United States President Joe Biden has imposed new sanctions on Iran in response to its missile and drone attack on Israel, as tensions mount over the possibility of further escalation in the Middle East. In a statement on Thursday, Biden said the sanctions targeted "leaders and entities connected to the Islamic Revolutionary Guard Corps, Iran's Defense Ministry, and the Iranian government's missile and drone program that enabled" the April 13 attack on Israel. "As I discussed with my fellow G7 [Group of Seven] leaders the morning after the attack, we are committed to acting collectively to increase economic pressure on Iran," the US president said. "And our allies and partners have or will issue additional sanctions and measures to restrict Iran's destabilizing military programs." Iran launched hundreds of missiles and drones at Israel in the early hours on Sunday, in retaliation for the deadly bombing of the Iranian consulate in Syria's capital, Damascus, earlier this month.

Meta just announced a major update for its AI assistant platform, Meta AI, which has been built using the long-awaited open source Llama 3 large language model (LLM). The company says it's "now the most intelligent AI assistant you can use for free." As for use case scenarios, the company touts the ability to help users study for tests, plan dinners and schedule nights out. Meta AI, however, has expanded into just about every nook and cranny throughout the company's entire portfolio, after a test run with Instagram DMs last week. It's still available with Instagram, but now users can access it on Messenger, Facebook feeds and Whatsapp.

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The compliments start flowing as soon as she answers the video call. "Wow, you so pretty, honey," says the man on the other side of the screen. His video feed shows he's white, with short hair, likely a few years younger than her, and is sitting in front of his camera wearing a plaid shirt. "You're looking different with that beard and stuff gone," the woman says in an American accent as the conversation gets going. The man doesn't miss a beat. "I told you I was going to shave my beard so I will look good."