Related papers: Generative AI and the Digital Commons
With strict protections and regulations of data privacy and security, conventional machine learning based on centralized datasets is confronted with significant challenges, making artificial intelligence (AI) impractical in many…
The rise of Generative Artificial Intelligence systems ("AI systems") has created unprecedented social engagement. AI code generation systems provide responses (output) to questions or requests by accessing the vast library of open-source…
Vision foundation models (VFMs) are predominantly developed using data-centric methods. These methods require training on vast amounts of data usually with high-quality labels, which poses a bottleneck for most institutions that lack both…
Generative AI tools are used to create art-like outputs and sometimes aid in the creative process. These tools have potential benefits for artists, but they also have the potential to harm the art workforce and infringe upon artistic and…
Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deployers are concerned about the legal repercussions and the…
The availability and abundance of GenAI tools to administer tasks traditionally managed by people have raised concerns, particularly within the education and academic sectors, as some students may highly rely on these tools to complete the…
Machine Learning and Artificial Intelligence are considered an integral part of the Fourth Industrial Revolution. Their impact, and far-reaching consequences, while acknowledged, are yet to be comprehended. These technologies are very…
Generative Artificial Intelligence (generative AI) poses both opportunities and risks for the integrity of research. Universities must guide researchers in using generative AI responsibly, and in navigating a complex regulatory landscape…
We live in the era of Generative Artificial Intelligence (GenAI). Deepfakes and Large Language Models (LLMs) are two examples of GenAI. Deepfakes, in particular, pose an alarming threat to society as they are capable of spreading…
Educators and learners worldwide are embracing the rise of Generative Artificial Intelligence (GenAI) as it reshapes higher education. However, GenAI also raises significant privacy and security concerns, as models and privacy-sensitive…
In human activity recognition (HAR), the limited availability of annotated data presents a significant challenge. Drawing inspiration from the latest advancements in generative AI, including Large Language Models (LLMs) and motion synthesis…
Data commons collate data with cloud computing infrastructure and commonly used software services, tools and applications to create biomedical resources for the large-scale management, analysis, harmonization, and sharing of biomedical…
Latency to end-users and regulatory requirements push large companies to build data centers all around the world. The resulting data is "born" geographically distributed. On the other hand, many machine learning applications require a…
The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superior data modeling of generative AI models enable them to…
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains. Meanwhile, the field of graph machine…
The deep integration of foundation models (FM) with federated learning (FL) enhances personalization and scalability for diverse downstream tasks, making it crucial in sensitive domains like healthcare. Achieving group fairness has become…
Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms. The EU's recent General Data Protection Regulation (GDPR) has been seen as a core tool for achieving better governance…
As the capabilities of artificial intelligence (AI) continue to expand rapidly, Human-AI (HAI) Collaboration, combining human intellect and AI systems, has become pivotal for advancing problem-solving and decision-making processes. The…
The widespread adoption of wearable sensors has the potential to provide massive and heterogeneous time series data, driving the use of Artificial Intelligence in human sensing applications. However, data collection remains limited due to…
Training generative machine learning models to produce synthetic tabular data has become a popular approach for enhancing privacy in data sharing. As this typically involves processing sensitive personal information, releasing either the…