ASSET

ABSTRACT: Verified artificial intelligence (AI) is the goal of designing AI-based systems that have strong, ideally provable, assurances of correctness with respect to formally specified requirements. This talk will review the main challenges to achieving Verified AI, and the initial progress the research community has made towards this goal. A particular focus will be on […]

ABSTRACT: “Does generative AI infringe copyright?” is an urgent question. It is also a difficult question, for two reasons. First, “generative AI” is not just one product from one company. It is a catch-all name for a massive ecosystem of loosely related technologies. These systems behave differently and raise different legal issues. Second, copyright law […]

ABSTRACT:  Algorithm audits are powerful tools for studying black-box systems without direct knowledge of those systems’ inner workings. While they have been effectively deployed to identify harms and biases in algorithmic content, algorithm audits’ narrow focus on technical components stop short of considering users themselves as integral and dynamic parts of the system, to be […]

ABSTRACT:  Shapley values and similar methods are widely used to explain the importance of features in model predictions. Clarity in the semantics of these feature importances is subtle, but crucial: What do these explanations actually mean? And how are they useful? We illustrate using explanations of predictions in three domains: (a) medical outcomes, (b) image […]

ABSTRACT: As deep learning systems improve, their applicability to critical domains is hampered because of a lack of transparency. Post-hoc explanations attempt to address this concern but they provide no guarantee of faithfulness to the model’s computations. Inherently interpretable models are an alternative but such models are often considered to be too simple to perform […]

ABSTRACT: In recent years, foundation models—large pretrained models that can be adapted for a wide range of tasks—have achieved state-of-the-art performance on a variety of tasks. While the pretrained models are trained on broad data, the adaptation (or fine-tuning) process is often performed on limited data. As a result, the challenges of distribution shift, where […]

ABSTRACT: As the sizes of Large Language Models (LLMs) continue to grow exponentially, it becomes imperative to explore novel computing paradigms that can address the dual challenge of scaling these models while adhering to constraints posed by compute and data resources. This presentation will delve into several strategies aimed at alleviating this dilemma: (1) refraining […]

ABSTRACT: We propose and demonstrate a novel approach to training image classification models based on large collections of images with limited labels. We take advantage of availability of radiology reports to construct joint multimodal embedding that serves as a basis for classification. We demonstrate the advantages of this approach in application to assessment of pulmonary […]

ABSTRACT: Machine learning has been increasingly deployed for high-stakes decisions that deeply impact people’s lives. My research focuses on developing interpretable algorithms and pipelines to ensure the safe and efficient utilization of machine learning models in the decision-making process. In this talk, I will introduce a new paradigm, called learning the Rashomon set, which finds and stores […]

ABSTRACT: In the past decade, we have begun grappling with difficult questions related to the rise of AI, including: What rights do individuals have in the age of AI? When should we regulate AI and when should we abstain? What degree of transparency is needed to monitor AI systems? These questions are all concerned with […]