Co-located with the IEEE International Conference on Data Mining (ICDM)

Nudging has been widely used by decision makers and organizations (both government and private) to influence the behavior of target populations, and the concept of nudging is now being widely used in the digital world. Examples of digital nudging include emails from hospitals or public health officials encouraging individuals to get vaccinated, text messages from colleges to stressed-out students to advertise the availability of counseling services during exam weeks, marketing messages through various digital media, and user interfaces designed to guide people’s behavior in digital choice environments.  

The central idea behind nudging is to make small changes to the environments in which citizens make decisions to encourage better behaviors. Even though nudges have traditionally involved simple changes that are easy and inexpensive to implement, more complex and sustained behavior change requires more complex interventions, presenting new challenges for nudging in the virtual world. Though the concept of nudging has been popularized recently, nudges have been in use in various aspects of society for a long time, including in healthcare, public health policy, law, economics, politics, insurance, finance, and advertising. With increasing availability of big data from many scientific disciplines, artificial intelligence (AI), machine learning (ML), and data science (DS) technologies have vast potential to transform data-driven nudging and decision making. This workshop seeks to build a new community around AI for nudging and provide a platform for exploring the state of the art in AI/ML/DS based systems and applications of digital nudging.  

Adaptation of products and services to individual preferences, called Personalization, has been at the core of modern businesses to improve customer satisfaction. Modern business and digital systems coupled with artificial intelligence technologies are poised to enable personalization on a grand scale. Personalization is a key element behind many modern businesses such as Netflix, Facebook, and Amazon to increase their revenue and customer base. Modern businesses are tailoring content for individual users based on the social, economic, and cultural profiles mined from the data, as it is shown to increase revenue and attract new customers. Modern applications ranging from precision marketing to precision healthcare have shown a clear demand for personalized content.  

We invite contributions from researchers of any discipline who are developing AI/ML/DS technologies that impact human behavior based on nudging theory or personalization or behavioral science-based solutions. For example, in the context of public health communications, how can AI/ML be used to address the construction of a message incorporating nudges; how do you digitally nudge people towards better healthcare outcomes, better financial decisions, or improve productivity; or how can nudging be personalized? What are the key data, technology, privacy and ethical, adaptation, and scaling challenges in nudging and personalization? In addition to algorithmic and systems papers, case studies that shed light on the effectiveness of nudges and personalization at maximizing a specific outcome, how AI/ML based systems can nudge people to make better decisions, or how industry is developing and/or using nudging and personalization technology to influence behavior of consumers are of great interest to this workshop.  

Topics of interest include, but not limited to, the following:  

• Theoretical foundations of nudging and personalization 
• Data driven and evidence based approaches in nudging and personalization
• Core AI/ML topics including multi-agents, federated learning, active learning, semi-supervised learning, multi-armed bandits, contextual bandits, reinforcement learning, deep learning, transfer learning 
• Multi-modal data and model fusion 
• Representation learning, and embeddings 
• Learning from categorical and relational data 
• Feature engineering 
• Statistical models, A/B testing 
• Privacy and Ethical issues in nudging and personalization 
• Personalized nudging 
• Challenges for AI in real-time nudging 
• AI-driven interactions encoding behavior change solutions 
• Nudging and personalization in conversational AI systems 
• Evaluation strategies to measure impact and effectiveness of nudging and personalization
• Applications: Healthcare, Precision Medicine, Energy, Environment, Transportation, Workforce, Education, Advertising, Government, Politics, Policy, Software Engineering 

Important Dates: 

Sept. 10, 2022: Paper submission 

Sep. 23, 2022: Acceptance notification 

Oct. 01, 2022: Camera-ready deadline and copyright form 

Nov. 28, 2022: Workshop 

Paper Submissions:  

This is an open call-for-papers. We invite both full papers (max 8 pages) describing mature work and short papers (max 4-5 pages) describing work-in-progress or case studies. Only original and high-quality papers formatted using the IEEE 2-column format (Latex Template), including the bibliography and any possible appendices will be considered for reviewing.

Proceedings:  

All submitted papers will be evaluated by 2-3 program committee members, and accepted papers will be included in an ICDM Workshop Proceedings volume, to be published by IEEE Computer Society Press and will be included in the IEEE Xplore Digital Library.  

Best Research/Application/Student Paper Awards:  

Best research, application, and student paper awards are sponsored by Lirio. The awards committee will select papers for these awards based on relevance, program committee reviews, and presentation quality.

Contact:  

• Visit the official workshop website for additional details at: https://lirio-brell.github.io/wain22/    
• If you have questions, please contact us by e-mail to: lirio.brell@gmail.com