[Air-L] Call For Papers [deadline approaching June 15]- HICSS 56 | Human-Centered Digital Privacy Solutions

[Michele A. L. Villagran]

Conference: Hawaii International Conference on System Sciences (HICSS-56) |
January 3-6, 2023*Minitrack: Human-centered Digital Privacy Solutions for
Digital and Social Media**Track: Digital and Social Media**Submission
Deadline: June 15, 2022 (11:59 PM HST)*

*Visit https://hicss.hawaii.edu/ <https://hicss.hawaii.edu/> for more
details, and https://hicss.hawaii.edu/authors/
<https://hicss.hawaii.edu/authors/> for author submission details.*

The burgeoning growth of artificial intelligence (AI) and machine learning
(ML) solutions, the ubiquity of social media and digital repositories, and
cross-platform data usage have raised the stakes of addressing digital
privacy. Semi-structured digital records, like emails, and unstructured
text, like social media posts, present a significant threat of privacy
breaches. The enormity of data presents an uphill task to identify and
mitigate private information on digital and social media platforms. While
AI-powered privacy solutions are a welcomed step, it is only recently that
researchers have focused on more human-centered and transdisciplinary
(Polk, 2015) digital privacy solutions. Privacy protection is a legal and
ethical responsibility of both public and private sector organizations, and
AI development and deployment should account for the diversity of the
target audience, the cultural contexts, and extant biases (Ehsan et al.,
2021). Privacy solutions should not only be effective and accurate but also
human-centered and accountable (Hepenstal et al., 2019).

This minitrack aims to attract submissions on digital privacy solutions
that bring different disciplines together, especially computer science,
system sciences, information science, social science, and law. The focus of
this minitrack is to bridge the gap between algorithmic development
(automated decision-making systems, fair, accountable, and transparent
systems) and human-centered approaches (usability studies, surveys, user
interviews). We encourage submissions that address privacy concerns in
digital and social media through inter- and trans-disciplinary approaches,
including but not limited to AI and ML.



Potential submissions for this minitrack should address (but are not
limited to) the following research topics:

   -

   quantitative, qualitative, and computational studies on digital (e.g.,
   images, webpages, emails, websites) and social media (e,g., Instagram,
   Twitter, Reddit, Facebook, TikTok, Weibo) privacy;
   -

   qualitative studies using interviews, surveys, and usability studies to
   identify the privacy behavior of users (when using digital and social
   media);
   -

   quantitative work on digital privacy using statistical analysis;
   -

   machine and deep learning approaches to identify, classify, and mitigate
   sensitive and private information on digital and social media platforms;
   -

   novel privacy solutions using explainable AI, transparent ML algorithms,
   or any interdisciplinary methods.

Minitrack Chair and Co-Chairs:

Dr. Souvick Ghosh

San Jose State University, USA

souvick.ghosh at sjsu.edu

Dr. Darra Hofman

San Jose State University, USA

darra.hofman at sjsu.edu

Dr. Aylin Imeri (Ilhan)

Heinrich Heine University Düsseldorf, Germany

Aylin.ilhan at hhu.de

Dr. Michele A. L. Villagran

San Jose State University, USA

michele.villagran at sjsu.edu