Fox News medical contributor Dr. Marc Siegel outlines how the medical field is working to integrate artificial intelligence to care for heart conditions. If you've read the headlines in 2023, Artificial Intelligence (AI) is either coming to save or destroy education. From AI tools that can help proofread students' work to chatbots that can act as a kind of virtual research assistant, there are applications emerging that could rapidly improve what students are able to do and how they are able to do it. At the same time, ask any teacher, and you'll hear myriad stories of AI-generated essays (many with incorrect information in them), and the yeoman's work necessary to ChatGPT-proof their tests and quizzes. Rather than look at the whole AI and education universe, let's focus on one significant challenge confronting K-12 education today.

Artificial intelligence dominated the conversation in Hollywood this year, from stars like Sheryl Crow and Simon Cowell expressing concerns, to others like Howie Mandel embracing the technology.

Many human-facing algorithms -- including those that power recommender systems or hiring decision tools -- are trained on data provided by their users. The developers of these algorithms commonly adopt the assumption that the data generating process is exogenous: that is, how a user reacts to a given prompt (e.g., a recommendation or hiring suggestion) depends on the prompt and not on the algorithm that generated it. For example, the assumption that a person's behavior follows a ground-truth distribution is an exogeneity assumption. In practice, when algorithms interact with humans, this assumption rarely holds because users can be strategic. Recent studies document, for example, TikTok users changing their scrolling behavior after learning that TikTok uses it to curate their feed, and Uber drivers changing how they accept and cancel rides in response to changes in Uber's algorithm. Our work studies the implications of this strategic behavior by modeling the interactions between a user and their data-driven platform as a repeated, two-player game. We first find that user strategization can actually help platforms in the short term. We then show that it corrupts platforms' data and ultimately hurts their ability to make counterfactual decisions. We connect this phenomenon to user trust, and show that designing trustworthy algorithms can go hand in hand with accurate estimation. Finally, we provide a formalization of trustworthiness that inspires potential interventions.

Dark patterns are deceptive user interface designs for online services that make users behave in unintended ways. Dark patterns, such as privacy invasion, financial loss, and emotional distress, can harm users. These issues have been the subject of considerable debate in recent years. In this paper, we study interpretable dark pattern auto-detection, that is, why a particular user interface is detected as having dark patterns. First, we trained a model using transformer-based pre-trained language models, BERT, on a text-based dataset for the automatic detection of dark patterns in e-commerce. Then, we applied post-hoc explanation techniques, including local interpretable model agnostic explanation (LIME) and Shapley additive explanations (SHAP), to the trained model, which revealed which terms influence each prediction as a dark pattern. In addition, we extracted and analyzed terms that affected the dark patterns. Our findings may prevent users from being manipulated by dark patterns, and aid in the construction of more equitable internet services. Our code is available at https://github.com/yamanalab/why-darkpattern.

This study aims to explore the complex relationship between perceptual and cognitive interactions in multimodal data analysis,with a specific emphasis on spatial experience design in overseas Chinese gardens. It is found that evaluation content and images on social media can reflect individuals' concerns and sentiment responses, providing a rich data base for cognitive research that contains both sentimental and image-based cognitive information. Leveraging deep learning techniques, we analyze textual and visual data from social media, thereby unveiling the relationship between people's perceptions and sentiment cognition within the context of overseas Chinese gardens. In addition, our study introduces a multi-agent system (MAS)alongside AI agents. Each agent explores the laws of aesthetic cognition through chat scene simulation combined with web search. This study goes beyond the traditional approach of translating perceptions into sentiment scores, allowing for an extension of the research methodology in terms of directly analyzing texts and digging deeper into opinion data. This study provides new perspectives for understanding aesthetic experience and its impact on architecture and landscape design across diverse cultural contexts, which is an essential contribution to the field of cultural communication and aesthetic understanding.

Feature attribution methods highlight the important input tokens as explanations to model predictions, which have been widely applied to deep neural networks towards trustworthy AI. However, recent works show that explanations provided by these methods face challenges of being faithful and robust. In this paper, we propose a method with Robustness improvement and Explanation Guided training towards more faithful EXplanations (REGEX) for text classification. First, we improve model robustness by input gradient regularization technique and virtual adversarial training. Secondly, we use salient ranking to mask noisy tokens and maximize the similarity between model attention and feature attribution, which can be seen as a self-training procedure without importing other external information. We conduct extensive experiments on six datasets with five attribution methods, and also evaluate the faithfulness in the out-of-domain setting. The results show that REGEX improves fidelity metrics of explanations in all settings and further achieves consistent gains based on two randomization tests. Moreover, we show that using highlight explanations produced by REGEX to train select-then-predict models results in comparable task performance to the end-to-end method.

Production of the Tesla CyberTruck is delayed, so a man in Vietnam made his own. A Tesla engineer was reportedly a victim of a bloody attack by a robot at a factory near Austin, Texas. Recent reports revealed that a 2021 injury report which claims the robot that was designed to move aluminum car parts, pinned the engineer against a surface and dug its metal claws into the his back and arm, according to witnesses who spoke to The Information in a story published last month. After another worker hit an emergency stop button, the engineer maneuvered his way out of the robot's grasp, falling a couple of feet down a chute designed to collect scrap aluminum and leaving a trail of blood behind him, one of the witnesses told The Information. The attack reportedly occurred while the engineer was programming software for two disabled Tesla robots nearby.

On 7 October 1779 a letter appeared in Berrow's Worcester Journal. "To the printer," wrote a disgruntled reader. "I take the liberty of informing you and the public that the account of a melancholy accident happening to a poor man at Evesham which was inserted in your last paper is utterly devoid of foundation." Reports of a man falling in a vat of boiling ale were, it turned out, greatly exaggerated, published on the back of an anonymous tip. But now the journal, which lays claim to being the oldest surviving newspaper in the world, says it has a cutting-edge new method to help reporters get out of the office and check their facts: artificial intelligence.

One of the more amusing TikToks that followed the announcement of the forthcoming Legend of Zelda movie riffs on a scene from the animated series Drawn Together. In it, the blue-caped Captain Hero sits in a wheelchair at the bottom of a staircase next to the text "Zelda fans when the movie was announced." One beat later, the words "it's live action" appear, and Captain Hero screams. Another beat, then "it's produced by Avi Arad (Morbius)" flashes up, this time to a louder scream. Finally, "It was written by the writer of Batman v Superman, Rise of Skywalker, and Jurassic Worlld [sic]," and Captain Hero unleashes one last wounded wail.

Jodi Long was caught off guard by the cage filled with cameras meant to capture images of her face and body. "I was a little freaked out because, before I walked in there, I said I don't remember this being in my contract," the actor said. The filmmakers needed her digital scan, Long was told, because they wanted to make sure her arms were positioned correctly in a scene where she holds a computer-generated character. That moment in 2020 stuck with Long, president of SAG-AFTRA's Los Angeles local, while she was negotiating for protections around the use of artificial intelligence when actors went on strike. In November, the actors guild reached a deal with Hollywood studios that -- among other things -- required consent and compensation for the use of a worker's digital replica.