Frequent and long-term exposure to hyperglycemia (i.e., high blood glucose) increases the risk of chronic complications such as neuropathy, nephropathy, and cardiovascular disease. Current technologies like continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) primarily model specific aspects of glycemic control-like hypoglycemia prediction or insulin delivery. Similarly, most digital twin approaches in diabetes management simulate only physiological processes. These systems lack the ability to offer alternative treatment scenarios that support proactive behavioral interventions. To address this, we propose GlyTwin, a novel digital twin framework that uses counterfactual explanations to simulate optimal treatments for glucose regulation. Our approach helps patients and caregivers modify behaviors like carbohydrate intake and insulin dosing to avoid abnormal glucose events. GlyTwin generates behavioral treatment suggestions that proactively prevent hyperglycemia by recommending small adjustments to daily choices, reducing both frequency and duration of these events. Additionally, it incorporates stakeholder preferences into the intervention design, making recommendations patient-centric and tailored. We evaluate GlyTwin on AZT1D, a newly constructed dataset with longitudinal data from 21 type 1 diabetes (T1D) patients on automated insulin delivery systems over 26 days. Results show GlyTwin outperforms state-of-the-art counterfactual methods, generating 76.6% valid and 86% effective interventions. These findings demonstrate the promise of counterfactual-driven digital twins in delivering personalized healthcare.

In her quest to design socially assistive robots--robots that provide social, not physical, support in realms like rehabilitation, education, and therapy--she realized that personalizing interactions would boost both engagement and outcomes. Artificial Intelligence (AI) has made that easier, though as always, surprises are never far when human beings are involved. Here, Matarić shares what she's learned about meeting people where they are. Let's talk about your work on socially assistive robots. You've said that having kids inspired you to build robots that help people. How did that interest develop into the mission of supporting specific behavioral interventions in health, wellness, and education?

Chatbots or conversational agents (CAs) are increasingly used to improve access to digital psychotherapy. Many current systems rely on rigid, rule-based designs, heavily dependent on expert-crafted dialogue scripts for guiding therapeutic conversations. Although recent advances in large language models (LLMs) offer the potential for more flexible interactions, their lack of controllability and transparency poses significant challenges in sensitive areas like psychotherapy. In this work, we explored how aligning LLMs with expert-crafted scripts can enhance psychotherapeutic chatbot performance. Our comparative study showed that LLMs aligned with expert-crafted scripts through prompting and fine-tuning significantly outperformed both pure LLMs and rule-based chatbots, achieving a more effective balance between dialogue flexibility and adherence to therapeutic principles. Building on findings, we proposed ``Script-Strategy Aligned Generation (SSAG)'', a flexible alignment approach that reduces reliance on fully scripted content while enhancing LLMs' therapeutic adherence and controllability. In a 10-day field study, SSAG demonstrated performance comparable to full script alignment and outperformed rule-based chatbots, empirically supporting SSAG as an efficient approach for aligning LLMs with domain expertise. Our work advances LLM applications in psychotherapy by providing a controllable, adaptable, and scalable solution for digital interventions, reducing reliance on expert effort. It also provides a collaborative framework for domain experts and developers to efficiently build expertise-aligned chatbots, broadening access to psychotherapy and behavioral interventions.

The Workshop Program of the Association for the Advancement of Artificial Intelligence's 37th Conference on Artificial Intelligence (AAAI-23) was held in Washington, DC, USA on February 13-14, 2023. There were 32 workshops in the program: AI for Agriculture and Food Systems, AI for Behavior Change, AI for Credible Elections: A Call to Action with Trusted AI, AI for Energy Innovation, AI for Web Advertising, AI to Accelerate Science and Engineering, AI4EDU: AI for Education, Artificial Intelligence and Diplomacy, Artificial Intelligence for Cyber Security (AICS), Artificial Intelligence for Social Good (AI4SG), Artificial Intelligence Safety (SafeAI), Creative AI Across Modalities, Deep Learning on Graphs: Methods and Applications (DLG-AAAI'23), DEFACTIFY: Multimodal Fact-Checking and Hate Speech Detection, Deployable AI (DAI), DL-Hardware Co-Design for AI Acceleration, Energy Efficient Training and Inference of Transformer Based Models, Graphs and More Complex Structures for Learning and Reasoning (GCLR), Health Intelligence (W3PHIAI-23), Knowledge-Augmented Methods for Natural Language Processing, Modelling Uncertainty in the Financial World (MUFin'23), Multi-Agent Path Finding, Multimodal AI for Financial Forecasting (Muffin), Multimodal AI for Financial Forecasting (Muffin), Privacy-Preserving Artificial Intelligence, Recent Trends in Human-Centric AI, Reinforcement Learning Ready for Production, Scientific Document Understanding, Systems Neuroscience Approach to General Intelligence, Uncertainty Reasoning and Quantification in Decision Making (UDM'23), User-Centric Artificial Intelligence for Assistance in At-Home Tasks, and When Machine Learning Meets Dynamical Systems: Theory and Applications. This report contains summaries of the workshops, which were submitted by some, but not all of the workshop chairs. An increasing world population, coupled with finite arable land, changing diets, and the growing expense of agricultural inputs, is poised to stretch our agricultural systems to their limits. By the end of this century, the earth's population is projected to increase by 45% with available arable land decreasing by 20% coupled with changes in what crops these arable lands can best support; this creates the urgent need to enhance agricultural productivity by 70% before 2050.

'As a result of federal initiatives to "get tough on crime," such as the Reagan Administration's War on Drugs and the Clinton Administration's "Three Strikes" laws, the total number of incarcerated Americans more than quadrupled from roughly 500,000 inmates in 1980 to 2.2 million inmates in 2015. During these decades, black Americans were incarcerated at a rate five times higher than that of white Americans. Despite a new 2019 US Bureau of Justice Statistics (BJS) report, which suggests that the racial disparity between white and black incarceration rates is "narrowing," a Pew Research Center review of BJS stats reveals that this 2019 report "counts only inmates sentenced to more than a year."Moreover, Whites accounted for 64% of adults but 30% of prisoners. . . . In 2017, there were 1,549 black prisoners for every 100,000 black adults--nearly six times the imprisonment rate for whites (272 per 100,000)."

Most conservation initiatives require changes in human behavior. For example, the establishment of a protected area will typically require some people to change their land-use or fishing practices. Yet conventional attempts to encourage proenvironmental behavior through awareness campaigns, financial incentives, and regulation can prove ineffective (1, 2). Insights into inducing behavior change from the social and behavioral sciences are therefore of critical importance for conservation scientists and practitioners (2–4). Conservation initiatives have begun to leverage a wide range of such behavioral insights (5) particularly regarding cognitive biases and social influence (see the figure). However, their application in the diverse socioeconomic and cultural contexts in which many conservation programs operate raises important ethical and implementation-related challenges.