1st International Workshop on Responsible AI and Data Ethics (RAIDE 2022)
In Conjunction with 2022 IEEE International Conference on Big Data (IEEE BigData 2022)
December 17-20
Okasa, Japan
AI/ML and other Big Data technologies are pervasive features of modern life that not only increasingly mediate our lives (e.g. – product recommenders, traffic navigation apps), but modulate them, as well (e.g. – recidivism prediction systems, resume screening). Their scale, ubiquity, and influence within social structures means ethical considerations are not negligible. While the potential benefits of AI and other Big Data technologies are undeniable, the hazards are numerous and recent years have seen harms befall individuals, groups, and, in some cases, entire societies. Much of the response has focused on high-level ethical principles and frameworks, but practitioners often struggle to put these principles into practice.
This workshop seeks to establish an international venue that brings together researchers and practitioners to promote awareness and research activities on ethical issues in AI and Data science, showcase new methods and best practices, explore the unknown and challenges, and foster cross-cultural collaboration and exchange of ideas. We expressly welcome research that interfaces with abstract normative concepts (e.g. – justice, fairness, trustworthiness, beneficence), moral stakeholders, and wicked sociotechnical problems to characterize issues and understand fundamental tensions and trade-offs. We also welcome research focused on ethically-aware methods for AI and Big Data, from developing diagnostic tools and best practices to formulating contextually-appropriate interventions and technical tools to mitigate harms where possible.
Research topics included in the workshop
Research facilitating deeper understanding of the Applied AI Ethics problemdomain including, but not limited to:
Taxonomical research
Case studies and empirical investigations
Applied philosophical work
Relevant work from sociology, psychology, and other socialsciences
Best practices and procedures, including diagnostic tools, documentation for improving transparency and accountability, and approaches to oversight and governance
Application papers discussing specific implementations
Ethically-aware methods in AI, Machine Learning and Big Data
Contributions to specific sub-domains of Applied AI Ethics, including but not limited to:
Fairness and Bias (FairML)
Explainability and Interpretability (XAI)
Robustness, Reliability, and Trustworthiness (RobustML, AI Alignment, AI Safety)
Privacy, Anonymity, and Security (Private and Secure AI)
Sustainability
Oversight and Governance
Foresight, Feedback, and Ethical Alignment in Dynamical Systems
Transparency and Accountability
Beneficence, Non-maleficence, and AI for Social Good (AI4SG)
Paper Submission
Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines: https://www.ieee.org/conferences/publishing/templates.html
Please submit your paper (5-10 pages) at
Important Dates
Oct 1, 2022: Due date for full workshop papers submission
Nov 1, 2022: Notification of paper acceptance to authors
Nov 20, 2022: Camera-ready of accepted papers
Dec 17-20, 2022: Workshop (one day)
Dr. Jonathan Broadman, Senior Data Scientist, Equifax Inc, USA
Dr. Ying Xie, Professor, Kennesaw State University, USA
Program Committee Members
Wenbin Zhang, Carnegie Mellon University, USA
Sherrill Hayes, Director, Kennesaw State University - School of Data Science and Analytics, USA
Jiang Zhong, Chongqing University, China
Dapeng Liu, University of New South Wales Sydney, Australia
Lili Zhang, Hewlett Packard Enterprise, USA
Bob Vanderheyden, Microsoft, USA
Varsha Rani Chawan, Home Depot, USA
Akihiro Abe, Chiba University, Japan
Jessica Rudd, Chief AI Officer, Mirry.AI, USA
Yan Wang, Amazon, USA
Workshop Co-Chairs
Jonathan Boardman, Senior Data Scientist, Equifax Inc. USA
Ying Xie, Professor, Kennesaw State University, USA
Workshop Schedule
1st International Workshop on Responsible AI and Data Ethics (RAIDE 2022) - Online
December 17, 2022
Time (JST)
Title
Presenter/Author
8:30AM – 9:20AM
Invited Talk - Why We Need to Formalize a Practically Oriented Sub-Discipline of AI and Data Ethics
Jonathan Boardman
9:30AM-9:50AM
S42201 Humans as Mitigators of Biases in Risk Prediction via Field Studies
Arul Mishra, Himanshu Mishra, and Bei Wang
9:50AM-10:10AM
S42202 Towards Implementing Responsible AI
Conrad Sanderson, Qinghua Lu, David Douglas, Xiwei Xu, Liming Zhu, and Jon Whittle
10:10AM-10:30AM
S42203 Prioritizing Policies for Furthering Responsible Artificial Intelligence in the United States
Emily Hadley
10:50AM-11:10AM
S42204 Siri, how long should this offender stay in prison?” Considerations about the use of algorithms for judgments in criminal proceedings
Anna-Katharina Dhungel
11:10AM-11:30AM
S42205 Standardization on Bias in Artificial Intelligence as Industry Support
Ewelina Szczekocka, Christèle Tarnec, and Janusz Pieczerak
11:30AM-11:50AM
S42206
The Analysis and Development of an XAI Process on Feature Contribution Explanation
Jun Huang, Zerui Wang, Ding Li, and Yan Liu
Break
12:20PM-12:40PM
S42207 Entity Matching with AUC-Based Fairness
Soudeh Nilforoushan, Qianfan Wu, and Mostafa Milani
12:40PM-1:00PM
S42209
Stereotype and Categorical Bias Evaluation via Differential Cosine Bias Measure
Sudhashree Sayenju, Ramazan Aygun, Bill Franks, Sereres Johnston, George Lee, and Girish Modgil
1:00PM-1:20PM
S42212 Integrated Gradients is a Nonlinear Generalization of the Industry Standard Approach to Variable Attribution for Credit Risk Models
Jonathan Boardman, Md Shafiul Alam, Xiao Huang, and Ying Xie
1:20PM-1:40PM
S42210 Applications of Integrated Gradients in Credit Risk Modeling
Md Shafiul Alam, Jonathan Boardman, Xiao Huang, and Matthew Turner
1:40PM-2:00PM
S42211 An Application of Localized Model Explainability: Identifying Key Disparities in Social Determinants of Health in Food Deserts
Md Shafiul Alam, Namazbai Ishmakhametov, Ying Xie, and Sumit Chakravarty
2:00PM-2:20PM
BigD477 Feature Integration Strategies for Multilingual Fake News Classification
Jędrzej Kozal, Michał Leś, Paweł Zyblewski, Paweł Ksieniewicz, and Michał Woźniak
2:20PM-2:40PM
BigD753 Don’t blindly use data. Towards a data statement for computational financial research.
Stacey Taylor and Vlado Keselj
Closing Remarks