As interest in implementing artificial intelligence (AI) in medical systems grows, discussion continues on how to evaluate the fairness of these systems, or the disparities they may perpetuate. Socioeconomic status (SES) is commonly included in machine learning models to control for health inequities, with the underlying assumption that increased SES is associated with better health. In this work, we considered a large cohort of patients from the Mount Sinai Health System in New York City to investigate the effect of patient SES, race, and sex on schizophrenia (SCZ) diagnosis rates via a logistic regression model. Within an intersectional framework, patient SES, race, and sex were found to have significant interactions. Our findings showed that increased SES is associated with a higher probability of obtaining a SCZ diagnosis in Black Americans ($\beta=4.1\times10^{-8}$, $SE=4.5\times10^{-9}$, $p < 0.001$). Whereas high SES acts as a protective factor for SCZ diagnosis in White Americans ($\beta=-4.1\times10^{-8}$, $SE=6.7\times10^{-9}$, $p < 0.001$). Further investigation is needed to reliably explain and quantify health disparities. Nevertheless, we advocate that building fair AI tools for the health care space requires recognizing the intersectionality of sociodemographic factors.

Step 2: Use Whisper to transcribe the audio to text: https://github.com/openai/whisper Prompt: "Provide a summary of the following pasted text. Format in a style that can be emailed to a team, it should be in the style of condensed Meeting minutes and easily digestible, but includes all the salient points.

It was a busy month for affinity groups at NeurIPS, with workshops from Black in AI, Queer in AI, LatinX in AI, Indigenous in AI, Global South in AI, Women in ML, and North Africans in ML. These workshops give researchers the opportunity to share their work, find support and make connections, and raise awareness of issues affecting their communities. Here are some of our highlights from the workshops. David Adelani presented his work on transfer languages – taking a model in one language and applying it to other languages. Transferring from one to another language can be tricky, especially when they use different structures or scripts.

The conference on Neural Information Processing Systems (NeurIPS) 2020 kicked off on Sunday 6th December and will run until Saturday 12th December. Here, we give a brief summary of many of the planned sessions and events for the week ahead. The public version of the schedule can be found here. You will need to be registered to access all of the content. Note that we have included links to the public versions in this article.

We also especially encourage students from underrepresented minorities to participate. Hands-on programming labs are a core part of our curriculum, so having some programming knowledge (specifically Python) will help participants get more out of the workshop. However, programming knowledge is not required; the workshop will include a track for participants who are completely new to programming. Experience with typical undergraduate math (calculus, linear algebra) and statistics (intro probability) is also helpful, but not required. The workshop will be run on Eastern Time, though students from outside this timezone are welcome to apply.

I am incredibly humbled to have been able to give a short talk at the Queer in AI workshop at ICML2020. This is the text of the talk. What an experience we are all having in recording these videos! Of all the videos you could be watching, Thank you for watching this one, and being here - I'm honoured to be given the gift of your time. And of course, a huge thank you to the organisers of the ICML2020 Queer In AI workshop for this opportunity.

We present a comparison of methods for uncertainty quantification (UQ) in deep learning algorithms in the context of a simple physical system. Three of the most common uncertainty quantification methods - Bayesian Neural Networks (BNN), Concrete Dropout (CD), and Deep Ensembles (DE) - are compared to the standard analytic error propagation. We discuss this comparison in terms endemic to both machine learning ("epistemic" and "aleatoric") and the physical sciences ("statistical" and "systematic"). The comparisons are presented in terms of simulated experimental measurements of a single pendulum - a prototypical physical system for studying measurement and analysis techniques. Our results highlight some pitfalls that may occur when using these UQ methods. For example, when the variation of noise in the training set is small, all methods predicted the same relative uncertainty independently of the inputs. This issue is particularly hard to avoid in BNN. On the other hand, when the test set contains samples far from the training distribution, we found that no methods sufficiently increased the uncertainties associated to their predictions. This problem was particularly clear for CD. In light of these results, we make some recommendations for usage and interpretation of UQ methods.

In this AI workshop, you are going to build a model to predict the bike demand for a specific hour of a day for the city of Washington. The data is available as sample data in the Azure ML Studio (classic) and is based on the data that has been collected in 2011 and 2012 in Washington. The dataset contains whether information and the number of bikes that have been rented. More information can be found on the UCI Machine Learning repository site: https://archive.ics.uci.edu/ml/datasets/bike We highly recommend you visit that site and investigate what kind of data you have available. Note: this workshop is to get in touch with machine learning.

Canadian immigration officials deny travel visas to a large number of AI researchers and research students scheduled to attend the NeurIPS and Black in AI workshop, event organizers said. Among the people who have been denied entry is Tẹjúmádé Àfọ njá, co-organizer of the NeurIPS Machine Learning workshop for the developing world. NeurIP Information Processing Systems (NeurIPs) is the world's largest annual international AI conference, according to the AI Index 2018 report. The conference is scheduled to be held from December 8 to 14 in Vancouver, Canada. On Tuesday, Black in AI co-founder and Google AI researcher Timnit Gebru said that 15 of the 44 attendees who planned to join the workshop on December 9 were denied entry.

Earlier this month Google quietly unveiled an incredibly unique opportunity for seasoned developers to explore pilot experiments based on some of Google's frontier AI research, aptly called "AI Workshop." Google already makes a wealth of AI research available on platforms from GitHub to its own AI Hub, complete with a searchable library of ready-to-use code examples, demonstrations and even wrappers around production systems. What makes AI Workshop so different from these other mediums is that it presents an early glimpse at selections from Google's bleeding edge enterprise AI research that might become future product offerings, allowing the research and developer community to provide feedback that can help influence those innovations, granting a rare opportunity to help shape the future of AI in the enterprise. The rise of deep learning has represented a unique era of collaboration between the commercial and research sectors. Many of the underlying toolkits, workflows, algorithms and even research models have all been released under open source licenses, with companies, academics and citizen researchers collaborating together to create innovative new applications and to improve the underlying infrastructure powering the modern deep learning revolution.