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Muslims in ML
Marzyeh Ghassemi · Mohammad Norouzi · Shakir Mohamed · Tasmie Sarker · Aya Salama

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Event URL: http://www.musiml.org/ »

Muslims In ML (MusIML) is an affinity workshop for the NeurIPS community. We 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”. The workshop will run on Tuesday, December 8, 2020 from 10:30am - 1:30pm EST. We will feature pre-recorded talks, with live Q/A sessions on Tuesday. This will be followed by a short panel discussion.

Tue 7:30 a.m. - 7:40 a.m. [iCal]
Welcome (Live Intro)
Marzyeh Ghassemi
Tue 7:40 a.m. - 8:00 a.m. [iCal]
Investigating Anti-Muslim Bias in GPT-3 through Words, Images, & Stories (Invited Talk)
Abubakar Abid

In this short talk I use the conceptual framing of a digital enclosure to consider the way Uyghur and Kazakh societies in Northwest China have been enveloped by a surveillance system over the past decade. I show how novel enclosures are produced and, in turn, construct new frontiers in capital accumulation and state power. The Turkic Muslim digital enclosure system began with the construction of 3-G cellular wireless networks which provided Uyghurs and Kazakhs with interactive smart-phone enabled capabilities across time and space. But over time state authorities paid private technology companies to build a data-intensive system with a wide range of spatial scales and information analytics that came to center on "Muslim" social media assessment and ethno-racialized face recognition technology. This complex matrix of overlaid enclosures assessed and controlled the movements and behavior of Muslims in increasingly intimate ways, resulting in mass detentions in "reeducation" camps. What makes the case in Northwest China unique beyond its scale and cruelty, is that in this context rather than banishing targeted populations solely to human warehousing spaces such as peripheral ghettos, camps or prisons, the digital enclosure works to explicitly “reeducate” the population as industrial workers and implement a forced labor regime.

Darren Byler
Taking from the Hands that Give: CRA audits of Muslim-led Charities (Invited Talk)
Anver Emon

Social media continues to grow in its scope, importance, and toxicity. Hate speech is ever-present in today’s social media, and causes or contributes to dangerous situations in the real world for those it targets. Anti-Muslim bias and hatred has escalated in both public life and social media in recent years. This talk will overview a new and ongoing project in identifying Islamophobia in social media using techniques from Natural Language Processing. I will describe our methods of data collection and annotation, and discuss some of the challenges we have encountered thus far. In addition I’ll describe some of the pitfalls that exist for any effort attempting to identify hate speech (automatically or not).

Ted Pedersen
Creating Multilingual Corpora for Arabic Characterset (Invited Talk)
Nayel Shafei

Author Information

Marzyeh Ghassemi (University of Toronto / Vector Institute)
Mohammad Norouzi
Shakir Mohamed (DeepMind)

Shakir Mohamed is a senior staff scientist at DeepMind in London. Shakir's main interests lie at the intersection of approximate Bayesian inference, deep learning and reinforcement learning, and the role that machine learning systems at this intersection have in the development of more intelligent and general-purpose learning systems. Before moving to London, Shakir held a Junior Research Fellowship from the Canadian Institute for Advanced Research (CIFAR), based in Vancouver at the University of British Columbia with Nando de Freitas. Shakir completed his PhD with Zoubin Ghahramani at the University of Cambridge, where he was a Commonwealth Scholar to the United Kingdom. Shakir is from South Africa and completed his previous degrees in Electrical and Information Engineering at the University of the Witwatersrand, Johannesburg.

Tasmie Sarker (University of Toronto)
Aya Salama (Aigorithm Tech)

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