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AESM 2022 : Algorithms towards Ethical and Privacy challenges in Social Media recommendation systems - ICDMW

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Link: https://sites.google.com/view/aesm2022/home
 
When Aug 20, 2022 - Dec 1, 2022
Where Orlando, Florida, USA
Submission Deadline Sep 2, 2022
Notification Due Sep 23, 2022
Final Version Due Oct 1, 2022
Categories    data mining   artificial intelligence   machine learning   computer science
 

Call For Papers

https://sites.google.com/view/aesm2022


Workshop objectives & call for papers


Fairness, ethics, and explainability are critical topics for the long-term health of online platforms and in turn, modern society. This workshop aims to bring together a diverse set of researchers and practitioners working on socially responsible social media and recommendation systems. We are broadly interested in both methodological and empirical research, in particular on works that study the varied ethical dilemmas, open algorithmic & practical challenges associated with implementing real-world systems.


Topics of interest include but are not limited to


- Ethical considerations on social platforms

- Equity in AI-based systems—identification, definition, mitigation

- Explainability, transparency, and trust in recommendation systems


Example topics include scalable approaches to ethical content moderation, operationalizing different fairness considerations, robustness, and explainability in recommendation systems. By bringing together researchers and practitioners across industry and academia, our goal is to start an application-centered discussion on developing ethical and responsible online social platforms.


*Speakers

Joaquin Quiñonero Candela (AI Fellow, LinkedIn; previously led responsible AI at Meta)

Swati Gupta (Georgia Tech)

Manish Raghavan (Harvard =) MIT)

Ankur Taly (Google)


*Organizers

Ying Xuan (LinkedIn)

Sakshi Jain (LinkedIn)

Hongseok Namkoong (Columbia University)


*Program Committee

William Cai (Stanford University)

Shaunak Chatterjee (LinkedIn)

Maria De-Arteaga (UTAustin)

Fereshte Khani (Microsoft)

Sarah Tan (Meta)

Birjodh Tiwana (LinkedIn)

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