"Big Sister is Watching You", it warned in big white letters, written behind a smiling photograph of Ylva Johansson, the EU commissioner in charge of home affairs. Within the bureaucratic confines of Brussels, it's rare for a politician to evoke enough anger to feature on a meme--let alone be labeled as the modern incarnation of author George Orwell's Big Brother by her colleagues. But Johansson has become a divisive figure in Europe. The Swedish politician has positioned herself in the midst of a vitriolic debate over online child sexual abuse material (CSAM), one that pits individual privacy against the safety of vulnerable young people. The EU Home Affairs Commissioner is the architect of a deeply controversial new bill that proposes ways to force tech companies, including those with encrypted platforms, to scan their users' private messages in an attempt to wipe both CSAM and grooming attempts off the internet.

"Gutfeld!" panelists weigh in on the rise of video and audio clips made using artificial intelligence tools to mimic the voice and the likeness of anyone you want. Images generated by artificial intelligence are becoming more convincing and prevalent, and they could lead to more complicated court cases if the synthetic media is submitted as evidence, legal experts say. "Deepfakes" often involve editing videos or photos of people to make them look like someone else by using deep-learning AI. The technology broadly hit the public's radar in 2017 after a Reddit user posted realistic-looking pornography of celebrities to the platform. The pornography was revealed to be doctored, but the revolutionary tech has only become more realistic and easier to make in the years since.

AI systems are not intrinsically neutral and biases trickle in any type of technological tool. In particular when dealing with people, AI algorithms reflect technical errors originating with mislabeled data. As they feed wrong and discriminatory classifications, perpetuating structural racism and marginalization, these systems are not systematically guarded against bias. In this article we consider the problem of bias in AI systems from the point of view of Information Quality dimensions. We illustrate potential improvements of a bias mitigation tool in gender classification errors, referring to two typically difficult contexts: the classification of non-binary individuals and the classification of transgender individuals. The identification of data quality dimensions to implement in bias mitigation tool may help achieve more fairness. Hence, we propose to consider this issue in terms of completeness, consistency, timeliness and reliability, and offer some theoretical results.

This paper aims to quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations such as temporal relations, causal relations, and discourse relations. Given ChatGPT's promising performance across various tasks, we conduct extensive evaluations on the whole test sets of 13 datasets, including temporal and causal relations, PDTB2.0-based and dialogue-based discourse relations, and downstream applications on discourse understanding. To achieve reliable results, we adopt three tailored prompt templates for each task, including the zero-shot prompt template, zero-shot prompt engineering (PE) template, and in-context learning (ICL) prompt template, to establish the initial baseline scores for all popular sentence-pair relation classification tasks for the first time. We find that ChatGPT exhibits strong performance in detecting and reasoning about causal relations, while it may not be proficient in identifying the temporal order between two events. It can recognize most discourse relations with existing explicit discourse connectives, but the implicit discourse relation still remains a challenging task. Meanwhile, ChatGPT performs poorly in the dialogue discourse parsing task that requires structural understanding in a dialogue before being aware of the discourse relation.

Based on the remarkable achievements of pre-trained language models in abstractive summarization, the copying mechanism has proved helpful by improving the factuality, stability, and overall performance. This work proposes PROM, a new PhRase-level cOpying Mechanism that enhances attention on n-grams, which can be applied to zero-shot summarization with pre-training. PROM adds an indicator layer to explicitly pick up tokens in n-gram that can be copied from the source, and calculates an auxiliary loss for the copying prediction. Empirical studies show that PROM makes significant improvements in fine-tuning on benchmarks. In zero-shot setting, PROM is utilized in the self-supervised pre-training on raw corpora and provides new general baselines on a wide range of summarization datasets. Further analysis shows that PROM performs more reasonable copying and contributes to faithfulness.

To avoid any terminological doubt, when we write "models of the language", we refer to any models that explain some linguistic phenomena, while "language models" refer to probabilistic language models as defined in Subsection 2.3 Probabilistic Language Models. While not long ago probabilistic language models were just models that assign probabilities to sequences of words [4], now they are the cornerstone of any task in computational linguistics through few-shot learning [6], prompt engineering [38] or fine-tuning [13]. On the other hand, current language models fail to catch long-range dependencies in the text consistently. For example, text generation with maximum likelihood target leads to rapid text degeneration, and consistent text generation requires probabilistic sampling and other tricks [22]. Large language models such as GPT-3 [6] push the boundary of "short text" rather far (specifically, to 2048 tokens), but do not remove the problem. Our contributions in this work are the following: We explain how the laws of autocorrelations decay in texts are related to applicability of language models to long texts; We pioneer the use of pretrained word vectors for autocorrelation computations that allows us to study a widest range of autocorrelation distances; We show that the autocorrelations in literary texts decay according to power laws for all these distances; We show that distributional semantics typically provides coherent autocorrelations decay exponents for texts translated to multiple languages, unlike earlier flawed approaches; We show that the behavior of autocorrelations decay in generated texts is quantitatively and often qualitatively different from the literary texts.

This paper describes the approach of the THUIR team at the COLIEE 2023 Legal Case Entailment task. This task requires the participant to identify a specific paragraph from a given supporting case that entails the decision for the query case. We try traditional lexical matching methods and pre-trained language models with different sizes. Furthermore, learning-to-rank methods are employed to further improve performance. However, learning-to-rank is not very robust on this task. which suggests that answer passages cannot simply be determined with information retrieval techniques. Experimental results show that more parameters and legal knowledge contribute to the legal case entailment task. Finally, we get the third place in COLIEE 2023. The implementation of our method can be found at https://github.com/CSHaitao/THUIR-COLIEE2023.

Recent advances in Artificial Intelligence (AI) have leveraged promising results in solving complex problems in the area of Natural Language Processing (NLP), being an important tool to help in the expeditious resolution of judicial proceedings in the legal area. In this context, this work targets the problem of detecting the degree of similarity between judicial documents that can be achieved in the inference group, by applying six NLP techniques based on the transformers architecture to a case study of legal proceedings in the Brazilian judicial system. The NLP transformer-based models, namely BERT, GPT-2 and RoBERTa, were pre-trained using a general purpose corpora of the Brazilian Portuguese language, and then were fine-tuned and specialised for the legal sector using 210,000 legal proceedings. Vector representations of each legal document were calculated based on their embeddings, which were used to cluster the lawsuits, calculating the quality of each model based on the cosine of the distance between the elements of the group to its centroid. We noticed that models based on transformers presented better performance when compared to previous traditional NLP techniques, with the RoBERTa model specialised for the Brazilian Portuguese language presenting the best results. This methodology can be also applied to other case studies for different languages, making it possible to advance in the current state of the art in the area of NLP applied to the legal sector.

Legal case retrieval techniques play an essential role in modern intelligent legal systems. As an annually well-known international competition, COLIEE is aiming to achieve the state-of-the-art retrieval model for legal texts. This paper summarizes the approach of the championship team THUIR in COLIEE 2023. To be specific, we design structure-aware pre-trained language models to enhance the understanding of legal cases. Furthermore, we propose heuristic pre-processing and post-processing approaches to reduce the influence of irrelevant messages. In the end, learning-to-rank methods are employed to merge features with different dimensions. Experimental results demonstrate the superiority of our proposal. Official results show that our run has the best performance among all submissions. The implementation of our method can be found at https://github.com/CSHaitao/THUIR-COLIEE2023.

In recent years, ML researchers have wrestled with defining and improving machine learning (ML) benchmarks and datasets. In parallel, some have trained a critical lens on the ethics of dataset creation and ML research. In this position paper, we highlight the entanglement of ethics with seemingly ``technical'' or ``scientific'' decisions about the design of ML benchmarks. Our starting point is the existence of multiple overlooked structural similarities between human intelligence benchmarks and ML benchmarks. Both types of benchmarks set standards for describing, evaluating, and comparing performance on tasks relevant to intelligence -- standards that many scholars of human intelligence have long recognized as value-laden. We use perspectives from feminist philosophy of science on IQ benchmarks and thick concepts in social science to argue that values need to be considered and documented when creating ML benchmarks. It is neither possible nor desirable to avoid this choice by creating value-neutral benchmarks. Finally, we outline practical recommendations for ML benchmark research ethics and ethics review.