Inspired by the Combahee River Collective Statement and the pioneering essays of Prof. Kimberlé Crenshaw, this talk suggests some tentative ways to imagine a practice of intersectionality in India. Using Dr. Ambedkar’s remarkable insight into the relation between caste and sexuality, this talk will do a critical reading of Navtej Johar and NALSA judgments by the Supreme Court of India that recognised the equal rights of LGB and T persons. Instead of understanding intersectionality as a catch all solution to political problems, the talk suggests that intersectionality sometimes leaves us with an aporia.

Post-2019, the reading down of section 377 by the Supreme court of India various production houses and OTT decided that it was time to have some queer content. But more often than not this was not really about empowering the community but getting a tick mark on having something to do with inclusion. What followed was a handful of films with queer narratives, mostly directed by cis men and as they put it ""taking baby steps"" to understand the community. What was missing was the queer community being empowered to tell their own stories, or empowering queer artists and technicians being hired to represent themselves.

This panel brings together a diverse group of queer faculty to discuss their experiences in the workplace. From navigating academia to being out in their department, panelists will share experiences and lessons on the same. The panel will also include discussions on inculcating inclusivity in the classroom.

We survey the treatment of sex and gender in the Computer Graphics research literature from an algorithmic fairness perspective. We conclude current trends on the use of gender in our research community are scientifically incorrect and constitute a form of algorithmic bias with potential harmful effects. We propose ways of addressing these trends as technical limitations.

This talk will cover the current state of name changes on previously published papers, an issue that’s particularly important for trans authors, largely through the lens of my own name change. I’ll talk about what publishers allow, how we got there, and issues with third-party tools like Google Scholar. I’ll also examine how effective all this is at getting people to actually cite you by the right name.

Queer individuals often have unique relations to their national identity based on societal norms and political tolerance in the country. These tensions can even motivate emigration, which then results in new challenges. This panel will discuss the difficulties faced by queer individuals when moving to new countries as well as challenges faced when living in a multicultural environment.

Many practical applications require optimization of multiple, computationally expensive, and possibly competing objectives that are well-suited for multi-objective Bayesian optimization (MOBO) procedures. However, for many types of biomedical data, measures of data analysis workflow success are often heuristic and therefore it is not known a priori which objectives are useful. Thus, MOBO methods that return the full Pareto front may be suboptimal in these cases. Here we propose a novel MOBO method that adaptively updates the scalarization function using properties of the posterior of a multi-output Gaussian process surrogate function. This approach selects useful objectives based on a flexible set of desirable criteria, allowing the functional form of each objective to guide optimization. We demonstrate the qualitative behaviour of our method on toy data and perform proof-of-concept analyses of single-cell RNA sequencing and highly multiplexed imaging datasets.

In December 2018, Tumblr took down massive amounts of LGBTQ content from its platform. Motivated in part by increasing pressures from financial institutions and a newly passed law -- SESTA / FOSTA, which made companies liable for sex trafficking online -- Tumblr implemented a strict "not safe for work" or NSFW model, whose false positives included images of fully clothed women, handmade and digital art, and other innocuous objects, such as vases. The Archive Team, in conjunction with the Internet Archive, jumped into high gear and began to scrape self-tagged NSFW blogs in the 2 weeks between Tumblr's announcement of its new policy and its algorithmic operationalization. At the time, Tumblr was considered a safe haven for the LGBTQ community and in 2013 Yahoo! bought Tumblr for 1.1 billion. In the aftermath of the so-called "Tumblr purge," Tumblr lost its main user base and, as of 2019, was valued at 3 million. This paper digs into a slice of the 90 TB of data saved by the Archive Team. This is a unique opportunity to peek under the hood of Yahoo's opennsfw model, which experts believe was used in the Tumblr purge, and examine the distribution of false positives on the Archive Team dataset. Specifically, we run the opennsfw model on our dataset and use the t-SNE algorithm to project the similarities across images on 3D space. We also labeled our 100,000 image dataset by LGBTQ community members on a Likert scale on the following dimensions: 1) does the image depict nudity or sex? 2) does the image contain or refer to LGBTQ relationships, themes, or subjects? This labeled data is only used to evaluate bias in the porn classifier and to help measure the impact of the Tumblr purge on the LGBTQ community.

From HIV/AIDS to COVID-19 to Monkeypox, outbreaks have disproportionately targeted LGBTQI+ communities. Evidence suggests that the likelihood of pandemics will only continue to increase, a possibility which highlights our need for better tools. By enabling the robust, efficient, and timely analysis of huge amounts of data, artificial intelligence has the potential to help decision-makers better respond to, manage, and even prevent infectious disease outbreaks (Malik et al., 2021; Wong, Zhou, and Zhang, 2019). This could, ultimately, reduce harm, disruption, and the loss of human life. However, AI also has a history of intensifying and perpetuating major inequities—including anti-queer bias. Considering the aforementioned disproportionate affects and epidemiology’s oppressive roots, we must be particularly thoughtful as we apply these algorithmic systems for infectious disease surveillance. Drawing from queer theory, critical race theory, and critical feminist studies, this project has adopted an Intersectional and reparative approach to studying the use of AI for such purposes. In doing so, it engages the audience in a dynamic give and take to explore how we can achieve algorithmic justice for LGBTQI+ people in the face of these emerging A-enabled tools.

The ML community recognizes the importance of anticipating and mitigating the potential negative impacts of benchmark research. In this position paper, we argue that more attention needs to be paid to areas of ethical risk that lie at the technical and scientific core of ML benchmarks. We identify overlooked structural similarities between human IQ and ML benchmarks. These share similarities in setting standards for describing, evaluating and comparing performance on tasks relevant to intelligence. Drawing on prior research on IQ benchmarks from feminist philosophy of science, we argue that values need to be considered when creating ML benchmarks and datasets, and that it is not possible to avoid this choice by creating benchmarks that are value-neutral. Finally, we outline practical recommendations for benchmark research ethics and ethics review.

Evaluating change in ranked term importance in a growing corpus is a powerful tool for understanding changes in vocabulary usage. In this paper, we analyze a corpus of free-response answers where 33,993 LGBTQ Generation Z respondents from age 13 to 24 in the United States are asked to self-describe their sexual orientation. We observe that certain labels, such as bisexual, pansexual, and lesbian, remain equally important across age groups. The importance of other labels, such as homosexual, demisexual, and omnisexual, evolve across age groups. Although Generation Z is often stereotyped as homogenous, we observe noticeably different label usage when self-describing sexual orientation within it. We urge that interested parties must routinely survey the most important sexual orientation labels to their target audience and refresh their materials (such as demographic surveys) to reflect the constantly evolving LGBTQ community and create an inclusive environment.