Call for Abstracts

We invite submissions of extended abstracts for the Muslims in ML Workshop at NeurIPS 2023. We invite submissions both from authors who self-identify as Muslim (Track 1) and by authors researching topics that address challenges faced by the Muslim community (Track 2). Papers are published in a non-archival format and may have been published in other venues before or may be published in other venues in the future.

Authors of accepted abstracts will be invited to a poster session. Selected abstracts will be additionally invited to give a lighting talk. Talks will follow a hybrid format, with authors having the option to present in person or remotely; however, all posters must be presented in person. We will recognize outstanding posters with a Best Poster Award.

Submission Link: https://openreview.net/group?id=NeurIPS.cc/2023/Workshop/MusIML

Important Dates

All deadlines are by the end of day, Anywhere On Earth (AoE).

Call for submission opens: August 18, 2023
Submissions Deadline: ~~September 22, 2023~~ October 6, 2023
Author Notifications: ~~October 13, 2023~~ October 16, 2023
NeurIPS Early Registration Deadline: October 21, 2023
Workshop Date: December 11, 2023

Areas of interest

The Muslims in ML workshop focus on both the potential for advancement and harm to Muslims and those in Muslim-majority countries who religiously identify, culturally associate, or are classified by proximity, as “Muslim”. Track 1 is designed to broadly showcase research by Muslim scholars in ML. Track 2 is designed to highlight work addressing challenges faced by the muslim community. Both tracks aim to promote awareness, collaboration, and mitigation strategies to ensure fair and equitable implementation of AI/ML technologies for Muslims worldwide.

Track 1 - topics related to ML and AI, including but not limited to:

Applications (e.g., vision, language, speech and audio)
Deep learning (e.g., architectures, generative models, optimization for deep networks)
Evaluation (e.g., methodology, meta studies, replicability and validity)
General machine learning (supervised, unsupervised, online, active, etc.)
Infrastructure (e.g., libraries, improved implementation and scalability, distributed solutions)
Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences)
Neuroscience and cognitive science (e.g., neural coding, brain-computer interfaces)
Optimization (e.g., convex and non-convex, stochastic, robust)
Probabilistic methods (e.g., variational inference, causal inference, Gaussian processes)
Reinforcement learning (e.g., decision and control, planning, hierarchical RL, robotics)
Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)
Theory (e.g., control theory, learning theory, algorithmic game theory)

Track 2 - topics related to challenges faced by the Muslim community, including but not limited to:

Algorithmic Bias, including how technology is used to identify ways in which algorithms and models are used to discriminate against Muslim individuals and communities in different contexts, and mitigation strategies.
Language and Speech, including: bridging gaps in to decrease disparity in Arabic-speaking groups, expanding support for tools using Arabic character sets, accurate transcription and translation for religious texts.
Data Collection and Representation, including challenges in collecting representative data for muslim communities, collecting data from diverse dialects and vernaculars.
Leveraging AI for Community Engagement and Outreach, including engaging Muslim youth in AI and ML.

Submission Format and Guidelines

All submissions must be in PDF format. Submissions are limited to two content pages, including all figures and tables. An additional page containing only references is allowed. To prepare your submission, please use the LaTeX style files for NeurIPS 2023: NeurIPS 2023 LaTeX style file.

All submissions must follow NeurIPS Author Guidelines. Submissions must be anonymized, please refrain from including personally identifiable information. Submissions will be reviewed in a double-blind setting.