Since 2022 we have been exploring application areas and technologies in which Artificial Intelligence (AI) and modern Natural Language Processing (NLP), such as Large Language Models (LLMs), can be employed to foster the usage and facilitate the documentation of Indigenous languages which are in danger of disappearing. We start by discussing the decreasing diversity of languages in the world and how working with Indigenous languages poses unique ethical challenges for AI and NLP. To address those challenges, we propose an alternative development AI cycle based on community engagement and usage. Then, we report encouraging results in the development of high-quality machine learning translators for Indigenous languages by fine-tuning state-of-the-art (SOTA) translators with tiny amounts of data and discuss how to avoid some common pitfalls in the process. We also present prototypes we have built in projects done in 2023 and 2024 with Indigenous communities in Brazil, aimed at facilitating writing, and discuss the development of Indigenous Language Models (ILMs) as a replicable and scalable way to create spell-checkers, next-word predictors, and similar tools. Finally, we discuss how we envision a future for language documentation where dying languages are preserved as interactive language models.

Anomaly detection in videos is challenging due to the complexity, noise, and diverse nature of activities such as violence, shoplifting, and vandalism. While deep learning (DL) has shown excellent performance in this area, existing approaches have struggled to apply DL models across different anomaly tasks without extensive retraining. This repeated retraining is time-consuming, computationally intensive, and unfair. To address this limitation, a new DL framework is introduced in this study, consisting of three key components: transfer learning to enhance feature generalization, model fusion to improve feature representation, and multi-task classification to generalize the classifier across multiple tasks without training from scratch when new task is introduced. The framework's main advantage is its ability to generalize without requiring retraining from scratch for each new task. Empirical evaluations demonstrate the framework's effectiveness, achieving an accuracy of 97.99% on the RLVS dataset (violence detection), 83.59% on the UCF dataset (shoplifting detection), and 88.37% across both datasets using a single classifier without retraining. Additionally, when tested on an unseen dataset, the framework achieved an accuracy of 87.25%. The study also utilizes two explainability tools to identify potential biases, ensuring robustness and fairness. This research represents the first successful resolution of the generalization issue in anomaly detection, marking a significant advancement in the field.

The language used by US courtroom actors in criminal trials has long been studied for biases. However, systematic studies for bias in high-stakes court trials have been difficult, due to the nuanced nature of bias and the legal expertise required. New large language models offer the possibility to automate annotation, saving time and cost. But validating these approaches requires both high quantitative performance as well as an understanding of how automated methods fit in existing workflows, and what they really offer. In this paper we present a case study of adding an automated system to a complex and high-stakes problem: identifying gender-biased language in US capital trials for women defendants. Our team of experienced death-penalty lawyers and NLP technologists pursued a three-phase study: first annotating manually, then training and evaluating computational models, and finally comparing human annotations to model predictions. Unlike many typical NLP tasks, annotating for gender bias in months-long capital trials was a complicated task that involves with many individual judgment calls. In contrast to standard arguments for automation that are based on efficiency and scalability, legal experts found the computational models most useful in challenging their personal bias in annotation and providing opportunities to refine and build consensus on rules for annotation. This suggests that seeking to replace experts with computational models is both unrealistic and undesirable. Rather, computational models offer valuable opportunities to assist the legal experts in annotation-based studies.

Influencer marketing remains largely undisclosed or inappropriately disclosed on social media. Non-disclosure issues have become a priority for national and supranational authorities worldwide, who are starting to impose increasingly harsher sanctions on them. This paper proposes a transparent methodology for measuring whether and how influencers comply with disclosures based on legal standards. We introduce a novel distinction between disclosures that are legally sufficient (green) and legally insufficient (yellow). We apply this methodology to an original dataset reflecting the content of 150 Dutch influencers publicly registered with the Dutch Media Authority based on recently introduced registration obligations. The dataset consists of 292,315 posts and is multi-language (English and Dutch) and cross-platform (Instagram, YouTube and TikTok). We find that influencer marketing remains generally underdisclosed on social media, and that bigger influencers are not necessarily more compliant with disclosure standards.

2023 was the year the world woke up to generative AI, and 2024 is the year policymakers are responding more firmly. Importantly, this policy momentum is taking place alongside real world creation and distribution of synthetic media. Social media platforms, news organizations, dating apps, image generation companies, and more are already navigating a world of AI-generated visuals and sounds, already changing hearts and minds, as policymakers try to catch up. How, then, can AI governance capture the complexity of the synthetic media landscape? How can it attend to synthetic media's myriad uses, ranging from storytelling to privacy preservation, to deception, fraud, and defamation, taking into account the many stakeholders involved in its development, creation, and distribution? And what might it mean to govern synthetic media in a manner that upholds the truth while bolstering freedom of expression? What follows is the first known collection of diverse examples of the implementation of synthetic media governance that responds to these questions, specifically through Partnership on AI's (PAI) Responsible Practices for Synthetic Media - a voluntary, normative Framework for creating, distributing, and building technology for synthetic media responsibly, launched in February 2023. In this paper, we present a case bank of real world examples that help operationalize the Framework - highlighting areas synthetic media governance can be applied, augmented, expanded, and refined for use, in practice. Read together, the cases emphasize distinct elements of AI policymaking and seven emergent best practices supporting transparency, safety, expression, and digital dignity online: consent, disclosure, and differentiation between harmful and creative use cases.

Expanding the involvement of women in Science, Technology, Engineering, and Mathematics (STEM) across Latin America is crucial for economic advancement, social equity, and global competitiveness; however, these efforts have proven to be challenging. Women in the region are underrepresented in STEM10 and even more so in leadership positions.17,18 The limited availability of current information and the difficulties associated with obtaining reliable data to mitigate gender disparities create difficulties in implementing policies to reduce the gender gap in STEM. Researchers, organizations, and policymakers working to reduce the gender gap need access to dependable data to understand the root causes of gender disparities, promote evidence-based interventions, and increase accountability and transparency. In the quest for solutions to these challenges, an international research network between Bolivia, Brazil, and Peru, "Equality in Leadership for Latin America STEM" (ELLAS), emerged in 2022.6

For the last couple of years, the tech community has tested no-strings-attached payments of 500 or 1,000 a month to those in dire need. Some of these experiments have happened in the heart of Silicon Valley, where a one-bedroom apartment rents for 3,000 a month and a modest house is often an unaffordable luxury. Silicon Valley's backing of these efforts has propelled the idea of a guaranteed income -- also known as cash transfers, unconditional cash and, in its most utopian form, universal basic income -- into the mainstream. But a bipartisan political consensus around the movement is fracturing even though the data seems to show that the programs are effective. In recent months, the Texas attorney general went to court to prevent public funds from being used in a basic income program in Houston.

Robust access to trustworthy information is a critical need for society including implications for knowledge production, public health education, and promoting informed citizenry in democratic societies. Generative AI technologies such as large language models (LLMs) may enable new ways to access information and improve effectiveness of existing information retrieval (IR) systems. More efficient basic task execution with the help of LLMs can also enable people to focus on the more challenging aspects of information retrieval related tasks and research. However, the long-term social implications of deploying these technologies in the context of information access are not yet well-understood. Existing research has focused on how these models may generate biased and harmful content [11, 23, 69, 80, 124, 158, 236] as well as the environmental costs [23, 31, 61, 166, 167, 241] of developing and deploying these models at scale. In the context of information access, Shah and Bender [187] have argued that certain framings of LLMs as "search engines" lack the necessary theoretical underpinnings and may constitute as a category error. In this current work, we present a broader perspective on the sociotechnical implications of generative AI for information access. Our perspective is informed by existing literature and aims to provide a summary of known challenges viewed through a systemic lens that we hope will serve as a useful resource for future critical research in this area. We present a summary of these implications next followed by recommendations for evaluation and mitigation later in this chapter.

Evaluating modern ML models is hard. The strong incentive for researchers and companies to report a state-of-the-art result on some metric often leads to questionable research practices (QRPs): bad practices which fall short of outright research fraud. We describe 43 such practices which can undermine reported results, giving examples where possible. Our list emphasises the evaluation of large language models (LLMs) on public benchmarks. We also discuss "irreproducible research practices", i.e. decisions that make it difficult or impossible for other researchers to reproduce, build on or audit previous research.

Driven by the powerful representation ability of Graph Neural Networks (GNNs), plentiful GNN models have been widely deployed in many real-world applications. Nevertheless, due to distribution disparities between different demographic groups, fairness in high-stake decision-making systems is receiving increasing attention. Although lots of recent works devoted to improving the fairness of GNNs and achieved considerable success, they all require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Surprisingly, we find that simple re-balancing methods can easily match or surpass existing fair GNN methods. We claim that the imbalance across different demographic groups is a significant source of unfairness, resulting in imbalanced contributions from each group to the parameters updating. However, these simple re-balancing methods have their own shortcomings during training. In this paper, we propose FairGB, Fair Graph Neural Network via re-Balancing, which mitigates the unfairness of GNNs by group balancing. Technically, FairGB consists of two modules: counterfactual node mixup and contribution alignment loss. Firstly, we select counterfactual pairs across inter-domain and inter-class, and interpolate the ego-networks to generate new samples. Guided by analysis, we can reveal the debiasing mechanism of our model by the causal view and prove that our strategy can make sensitive attributes statistically independent from target labels. Secondly, we reweigh the contribution of each group according to gradients. By combining these two modules, they can mutually promote each other. Experimental results on benchmark datasets show that our method can achieve state-of-the-art results concerning both utility and fairness metrics. Code is available at https://github.com/ZhixunLEE/FairGB.