Illegal loot box advertising on social media? An empirical study using the Meta and TikTok ad transparency repositories

Loot boxes are gambling-like products inside video games that can be bought with real-world money to obtain random rewards. They are widely available to children, and stakeholders are concerned about potential harms, e.g., overspending. UK advertising must disclose, if relevant, that a game contains (i) any in-game purchases and (ii) loot boxes specifically. An empirical examination of relevant adverts on Meta-owned platforms (i.e., Facebook, Instagram, and Messenger) and TikTok revealed that only about 7 % disclosed loot box presence. The vast majority of social media advertising (93 %) was therefore non-compliant with UK advertising regulations and also EU consumer protection law. In the UK alone, the 93 most viewed TikTok adverts failing to disclose loot box presence were watched 292,641,000 times total or approximately 11 impressions per active user. Many people have therefore been repeatedly exposed to prohibited and socially irresponsible advertising that failed to provide important and mandated information. Implementation deficiencies with ad repositories, which must comply with transparency obligations imposed by the EU Digital Services Act, are also highlighted, e.g., not disclosing the beneficiary. How data access empowered by law can and should be used by researchers is practically demonstrated. Policymakers should consider enabling more such opportunities for the public benefit

Why AI Monitoring Faces Resistance and What Healthcare Organizations Can Do About It: An Emotion-Based Perspective

Continuous monitoring of patients' health facilitated by Artificial Intelligence (AI) has enhanced the quality of health care, that is, the ability to access effective care. However, AI monitoring often encounters resistance in adoption by decision makers. Healthcare organizations frequently assume that the resistance stems from patients’ rational evaluation of the technology's costs and benefits. Recent research challenges this assumption and suggests that the resistance to AI monitoring is influenced by the emotional experiences of patients and their surrogate decision makers. We develop a framework from an emotional perspective, provide important implications for healthcare organizations, and offer recommendations to help reduce resistance to AI monitoring

Minimizing Combined Sewer Overflows with Online Model-Predictive Reinforcement Learning: A Case Study of the Stormwater Tunnel in Denmark

This is an artifact that can help reproduce the experimental results represented in the paper "Minimizing Combined Sewer Overflows with Online Model-Predictive Reinforcement Learning". The package contains models such as SWMM model, UPPAAL STRATEGO model, Historical weather data, and python code to run the experiment

Modyn: Data-Centric Machine Learning Pipeline Orchestration

In real-world machine learning (ML) pipelines, datasets are continuously growing. Models must incorporate this new training data to improve generalization and adapt to potential distribution shifts. The cost of model retraining is proportional to how frequently the model is retrained and how much data it is trained on, which makes the naive approach of retraining from scratch each time impractical. We present Modyn, a data-centric end-to-end machine learning platform. Modyn's ML pipeline abstraction enables users to declaratively describe policies for continuously training a model on a growing dataset. Modyn pipelines allow users to apply data selection policies (to reduce the number of data points) and triggering policies (to reduce the number of trainings). Modyn executes and orchestrates these continuous ML training pipelines. The system is open-source and comes with an ecosystem of benchmark datasets, models, and tooling. We formally discuss how to measure the performance of ML pipelines by introducing the concept of composite models, enabling fair comparison of pipelines with different data selection and triggering policies. We empirically analyze how various data selection and triggering policies impact model accuracy, and also show that Modyn enables high throughput training with sample-level data selection