Academic citations are widely used for evaluating research and tracing knowledge flows. Such uses typically rely on raw citation counts and neglect variability in citation types. In particular, citations can vary in their fidelity as original knowledge from cited studies may be paraphrased, summarized, or reinterpreted, possibly wrongly, leading to variation in how much information changes from cited to citing paper. In this study, we introduce a computational pipeline to quantify citation fidelity at scale. Using full texts of papers, the pipeline identifies citations in citing papers and the corresponding claims in cited papers, and applies supervised models to measure fidelity at the sentence level. Analyzing a large-scale multi-disciplinary dataset of approximately 13 million citation sentence pairs, we find that citation fidelity is higher when authors cite papers that are 1) more recent and intellectually close, 2) more accessible, and 3) the first author has a lower H-index and the author team is medium-sized. Using a quasi-experiment, we establish the "telephone effect" - when citing papers have low fidelity to the original claim, future papers that cite the citing paper and the original have lower fidelity to the original. Our work reveals systematic differences in citation fidelity, underscoring the limitations of analyses that rely on citation quantity alone and the potential for distortion of evidence.

Learning-based solutions are efficient, but are they trustworthy enough to be embedded in a robot cooperating with or assisting humans? In this blogpost, Daniele Meli explores this question, and reviews logic programming as a route to trustworthy autonomous (and cooperative) robotic systems. As part of the 37th AAAI Conference on Artificial Intelligence (AAAI2023), 32 different workshops were held, covering a wide range of topics. We heard from the organisers of four of these workshops, who told us their key takeaways from their respective events. These were split into two articles: 1) #AAAI2023 workshops round-up 1: AI for credible elections, and responsible human-centric AI, and 2) #AAAI2023 workshops round-up 2: health intelligence and privacy-preserving AI. Hosted by the Alan Turing Institute, AI UK is a two-day conference that showcases artificial intelligence and data science research, development, and policy in the UK. This year, the event took place on 21 and 22 March, and we covered the panel discussion session on the role and impact of science journalism. AAAI have updated their publication policy to deal with AI systems: "It is AAAI's policy that any AI system, including Generative Models such as Chat-GPT, BARD, and DALL-E, does not satisfy the criteria for authorship of papers published by AAAI and, as such, also cannot be used as a citable source in papers published by AAAI".

Isabelle Augenstein was one of the invited speakers at this year's AAAI Conference on Artificial Intelligence. She presented some of her work relating the communication of scientific research, and how information changes as it is reported by different media. Accurate reporting of science and technology is of paramount importance. The general public relies principally on mainstream media outlets for their science news. Overhyping, exaggeration and misrepresentation of research findings erode trust in science and scientists.

Whether the media faithfully communicate scientific information has long been a core issue to the science community. Automatically identifying paraphrased scientific findings could enable large-scale tracking and analysis of information changes in the science communication process, but this requires systems to understand the similarity between scientific information across multiple domains. To this end, we present the SCIENTIFIC PARAPHRASE AND INFORMATION CHANGE DATASET (SPICED), the first paraphrase dataset of scientific findings annotated for degree of information change. SPICED contains 6,000 scientific finding pairs extracted from news stories, social media discussions, and full texts of original papers. We demonstrate that SPICED poses a challenging task and that models trained on SPICED improve downstream performance on evidence retrieval for fact checking of real-world scientific claims. Finally, we show that models trained on SPICED can reveal large-scale trends in the degrees to which people and organizations faithfully communicate new scientific findings. Data, code, and pre-trained models are available at http://www.copenlu.com/publication/2022_emnlp_wright/.

To a multi-agent logic of knowledge or belief we can add public announcements to model publicly observed information change, or action models to model information change that is differently observed by different agents, but also modalities representing quantification over such information change, such as quantifiers over announcements or quantifiers over actions models. Such additions may result in more complex or undecidable logics, and create a very open landscape of relative expressivity. The survey [88] of such logics focused on open problems. Some such open problems have since then been resolved, and yet others have come to the fore. In this updated survey we review what is known about such logics with quantification over information change, including digressions into what are known as relation changing modal(but often not epistemic) logics. Again we focus on open problems.