This work studies publications in the field of cognitive science and utilizes natural language processing (NLP) and graph theoretical techniques to connect the analysis of the papers' content (abstracts) to the context (citation, journals). We apply hierarchical topic modeling on the abstracts and community detection algorithms on the citation network, and measure content-context discrepancy to find academic fields that study similar topics but do not cite each other or publish in the same venues. These results show a promising, systemic framework to identify opportunities for scientific collaboration in highly interdisciplinary fields such as cognitive science and machine learning.

AI, machine learning, and data science methods are already pervasive in our society and technology, affecting all of our lives in many subtle ways. Trustworthy AI has become an important topic because trust in AI systems and their creators has been lost, or was never present in the first place. Researchers, corporations, and governments have long and painful histories of excluding marginalized groups from technology development, deployment, and oversight. As a direct result of this exclusion, these technologies have long histories of being less useful or even harmful to minoritized groups. This infuriating history illustrates that industry cannot be trusted to self-regulate and why trust in commercial AI systems and development has been lost. We argue that any AI development, deployment, and monitoring framework that aspires to trust must incorporate both feminist, non-exploitative participatory design principles and strong, outside, and continual monitoring and testing. We additionally explain the importance of considering aspects of trustworthiness beyond just transparency, fairness, and accountability, specifically, to consider justice and shifting power to the people and disempowered as core values to any trustworthy AI system. Creating trustworthy AI starts by funding, supporting, and empowering groups like Queer in AI so the field of AI has the diversity and inclusion to credibly and effectively develop trustworthy AI. Through our years of work and advocacy, we have developed expert knowledge around questions of if and how gender, sexuality, and other aspects of identity should be used in AI systems and how harms along these lines should be mitigated. Based on this, we discuss a gendered approach to AI, and further propose a queer epistemology and analyze the benefits it can bring to AI.

This submission is a poetry reflecting on classification and belongingness.

A substantial majority of the world’s languages have no language technologies and NLP toolkits at all. With an increasing reliance on technology and the web, depriving people access to technology in their native language is indirectly causing a loss of language, culture, traditions, linguistic information, and a diminishing richness of the human experience. This harsh reality marks the 21st century as a pivotal time for researchers and engineers in NLP. As per linguists, nearly half of the world's 7000 languages will be extinct before the end of this very century. But what if the advances in natural language processing and computational linguistics could help us change course? There has been a wide range of efforts by research groups on low-resource and resource-poor languages for the purposes of machine translation, and on endangered languages for the purposes of documentation and preservation. But despite numerous efforts in the field, there is a lack of a clear sense of direction and a unified front to tackle this problem. This paper hopes to unravel the diverse computational efforts being undertaken for low-resource, resource-poor and endangered language research, the different data resource creation and extraction techniques, and modern deep learning and statistical models being used specifically for this domain

I will present work previously accepted at IQCE 2021, which built on a presentation given at Trans Math Day 2020. In this work, I used mixed methods to model my first year on feminizing hormone replacement therapy. I draw heavily on Quantitative Ethnography, Analytic Autoethnography, and use a desire-based framework. Key to this work was relating (a) "time" as a thing experienced nonlinearly during a life transition and (b) other qualitative variables. The result is a confident telling of my first year of HRT, organized around a critical event that had a structural effect on the data: my doctor moving me to a three month dose so I could visit the pharmacist less often, in turn being able to live my damn life, escape unhealthy cycles of behavior, and finally start making progress towards my transition goals.

We present Mementorium, an interactive, branching narrative told in immersive, virtual reality (VR). The player uncovers the narrator’s memories of gender and sexuality-based marginalizations in STEM learning environments, moving from childhood to early adulthood. Mementorium’s design builds upon our previous designs and research on queer reorientations to computing and queer approaches to embodied learning in VR. When LGBTQ+ people’s exclusion is even acknowledged, approaches to addressing the problem often treat LGBTQ+ people as the problem: “We become a problem when we describe a problem” (Ahmed, 2017, p. 39). Framing LGBTQ+ people as the cause of their exclusion leads to solutions to entice and retain LGBTQ+ people in STEM. However, this fails to address issues that keep LGBTQ+ people from STEM fields. Mementorium aims to increase understanding of interpersonal and systemic factors contributing to LGBTQ+ exclusion from STEM learning and professions and encourage more expansive thinking and action in solidarity with LGBTQ+ people. Mementorium tells the story of a queer, nonbinary person interested in learning about technology but faces barriers to participation due to normative and oppressive ideas about gender and sexuality. Each of the memories that the player uncovers has three branching points in the narrative. First, the player uncovers the memory, revealing the harm caused by marginalization. Next, the player chooses their reaction to the situation that reorient players to the narrator’s experiences. Finally, the player chooses a future-oriented response to direct the narrator’s actions, offering choices for individual or group-oriented action or action on a larger scale of social change. We are researching Mementorium to see how players make sense of LGBTQ+ marginalizations as individual and systemic issues and how to reorient players toward counter-hegemonic actions that support marginalized people.