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

On-Line Paper Submission (wi-lab.com)

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

1^st 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