Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we find out about Neural Operators, take a virtual trip to IJCAI, and try to bridge the gap between user expectations and AI capabilities. Anima Anandkumar is the inventor of Neural Operators which extend deep learning to modelling multi-scale processes in many scientific domains, including weather and climate modelling, drug discovery, and engineering design problems. In the next in our series of interviews with the 2024 AAAI Fellows, Anima tells us about Neural Operators and how she has applied them to many important science and engineering problems. Florian Tramer, Gautam Kamath and Nicholas Carlini won an International Conference on Machine Learning (ICML 2024) best paper award for their work Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining, in which they challenge the paradigm of pretraining models with public data, and then privately fine-tuning the weights with sensitive data.

Today, AI systems are increasingly integrated into everyday workplaces. However, as AI systems become more prevalent in everyday workplaces, their integration has not always been as smooth or successful as anticipated. A significant reason for this is the gap between user (worker) expectations and the actual capabilities of AI systems. This gap often leads to user dissatisfaction and poor adoption rates, highlighting the need for better design approaches to align user needs with AI functionalities. To address these design challenges, it's crucial to understand dual perspectives: both the user's and the AI system's point of view. From the user's viewpoint, designers need to comprehend their information and interaction needs, routines, and skills.

Increasing evidence suggests that many deployed AI systems do not sufficiently support end-user interaction and information needs. Engaging end-users in the design of these systems can reveal user needs and expectations, yet effective ways of engaging end-users in the AI explanation design remain under-explored. To address this gap, we developed a design method, called AI-DEC, that defines four dimensions of AI explanations that are critical for the integration of AI systems -- communication content, modality, frequency, and direction -- and offers design examples for end-users to design AI explanations that meet their needs. We evaluated this method through co-design sessions with workers in healthcare, finance, and management industries who regularly use AI systems in their daily work. Findings indicate that the AI-DEC effectively supported workers in designing explanations that accommodated diverse levels of performance and autonomy needs, which varied depending on the AI system's workplace role and worker values. We discuss the implications of using the AI-DEC for the user-centered design of AI explanations in real-world systems.