Call for Papers: IEEE ICDM International Workshop on AI for Nudging and
Personalization (WAIN-21)
Co-located with IEEE International Conference on Data Mining (ICDM)
(Due to ongoing COVID-19, WAIN-21 workshop will be held virtually.)

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,
adoption, 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

Topics of interest include, but not limited to, the following:
- Theoretical foundations of 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
- Evaluation strategies to measure impact and effectiveness of nudging and
- Applications: Healthcare, Precision Medicine, Energy, Environment,
Transportation, Workforce, Education, Advertising, Government, Politics,
Policy, Software Engineering

Important dates:
- (Extended) Sept. 12, 2021: Paper submission
- Sep. 24, 2021: Acceptance notification
- Oct. 01, 2021: Camera-ready deadline and copyright form
- Dec. 17, 2021: (Due to ongoing COVID-19, WAIN workshop will be fully

Paper Submissions:
This is an open call-for-papers. We invite both full papers (max 8 pages)
describing mature work and short papers (max 5-6 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.
Submission instructions can be found at

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

- Visit the official workshop website for additional details at:
- If you have questions, please contact us by e-mail to:
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