DIG-BUGS: Data in Generative Models (The Bad, the Ugly, and the Greats)

Generative models have become extremely powerful and are now integral to various aspects of daily life from creative arts to customer service. Given their increasing interaction with people, ensuring their trustworthiness is crucial. This workshop centers on the idea that the safety and reliability of generative models are deeply connected to the nature and treatment of their training data. We aim to explore the hypothesis that building reliable and trustworthy artificial intelligence (AI) systems based on generative models must start with high-quality and responsibly managed data.The workshop will focus on several key areas where training data impacts the trustworthiness of generative models. Among others, we will address 1) privacy concerns, highlighting how improper inclusion and handling of sensitive information in the training data can lead to significant privacy violations; 2) safety risks, like backdoors and data poisoning that threaten robust generations; and 3) the impact of biases in generative models' training data, which can cause models to perpetuate or even amplify societal biases, resulting in unfair outcomes.Through expert talks, panel discussions, and interactive sessions, participants will delve into these issues and explore strategies for developing safe, trustworthy, and reliable generative models. This workshop aims to foster collaboration and drive forward research to ensure that generative models, as they become more embedded in our lives, do so in a trustworthy and beneficial manner.