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1. Towards Inclusive Political Representation

   Kaur, Komal (May 2024, Proquest Dissertations)

   My research asks the following questions: What are the ramifications of male dominance in positions of power? Do they adequately address the concerns of women constituents? How do electoral quotas influence the trajectory of political careers for underrepresented groups? Which subgroups derive the greatest benefits from quota implementation? How do voters respond to the introduction of quotas, and what factors shape their reactions? I theorize and offer causal evidence on the consequences of inequitable political representation and the role of quotas in remedying disparities in political inequality. Through a combination of observational and experimental data spanning various levels ranging from legislatures to voters, my research examines quotas (or lack thereof) from multiple perspectives using a range of methodological approaches. My research demonstrates that while there is a risk of neglecting policy issues faced by underrepresented groups in legislative spaces, indicating the need for institutionalizing quotas, quotas can have asymmetric effects on citizens’ political behavior. Quotas can bridge gaps in political participation by demobilizing dominant groups while simultaneously mobilizing underrepresented groups. However, their repercussions extend beyond the realm of politics and have implications for intergroup relations. Thus, my research highlights the nuances of political representation and examines the effects of quotas on governance structures and political behavior. My dissertation comprises three distinct yet interconnected empirical chapters, all situated within the context of India. Overall, this dissertation offers lessons not only for those interested in Indian politics but also for everyone seeking to understand the role of quotas in addressing political inequality. 

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2. Learning from Our Study Organisms about Sexual Selection: Lessons from the Ocellated Wrasse

   https://doi.org/10.1086/723109  

   Alonzo, Suzanne H. (March 2023, The American Naturalist)

   Sexual selection is a powerful force shaping not only the details but also the breadth of what we see in nature. Yet so much unexplained variation remains. Organisms often solve the “problem” of how to pass on their genes in ways that do not fit our current expectations. I argue here that integrating empirical surprises will push our understanding of sexual selection forward. Such “nonmodel” organisms (i.e., species that do not do what we think they should do) challenge us to think deeply, integrate puzzling results, question our assumptions, and consider the new (and arguably better) questions these unexpected patterns pose. In this article, I share how puzzling observations from my long-term research on the ocellated wrasse (Symphodus ocellatus) have shaped my understanding of sexual selection and suggested new questions about the interplay among sexual selection, plasticity, and social interactions. My general premise, however, is not that others should study these questions. Instead, I argue for a change in the culture of our field—to consider unexpected results a welcome opportunity to generate new questions and learn new things about sexual selection. Those of us in positions of power (e.g., as editors, reviewers, and authors) need to lead the way. 

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3. What Needs to Change in Academia to Increase the Number of Black Scientists and Engineers? A Son of Redlines.

   https://doi.org/10.1016/j.cels.2020.06.014  

   Wilson, C.J. (July 2020, Cell Systems)

   Three brothers, black males, one murdered, one incarcerated—one unscathed (me). Three cousins, black males, two incarcerated—one with his liberties intact (me). Six siblings, only two completed high school—one being me. I remember, when I was 4 years old, walking to the store with my sister and being scolded for picking up a hypodermic needle someone used to inject heroin… and we lived in the better part of my neighborhood. This is the legacy of redlining, compounded by mass incarceration. If you do not know what redlining is, look it up. Purportedly, redlining ended in 1968; however, it is still the birthright of many black Americans. My early life experience is the product of this legacy. 

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4. My Big, Fat 50-Year Journey

   https://doi.org/10.1162/coli_a_00499  

   Palmer, Martha (March 2024, Computational Linguistics)

   My most heartfelt thanks to ACL for this tremendous honor. I’m completely thrilled. I cannot tell you how surprised I was when I got Iryna’s email. It is amazing that my first ACL conference since 2019 in Florence includes this award. What a wonderful way to be back with all of my friends and family here at ACL. I’m going to tell you about my big fat 50-year journey. What have I been doing for the last 50 years? Well, finding meaning, quite literally in words. Or in other words, exploring how computational lexical semantics can support natural language understanding. This is going to be quick. Hold onto your hats, here we go. 

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5. Leveraging Human Input to Enable Robust, Interactive, and Aligned AI Systems

   https://doi.org/10.1609/aaai.v39i27.35099  

   Brown, Daniel S (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Ensuring that AI systems do what we, as humans, actually want them to do, is one of the biggest open research challenges in AI alignment and safety. My research seeks to directly address this challenge by enabling AI systems to interact with humans to learn aligned and robust behaviors. The way in which robots and other AI systems behave is often the result of optimizing a reward function. However, manually designing good reward functions is highly challenging and error prone, even for domain experts. Consider trying to write down a reward function that describes good driving behavior or how you like your bed made in the morning. While reward functions for these tasks are difficult to manually specify, human feedback in the form of demonstrations or preferences are often much easier to obtain. However, human data is often difficult to interpret, due to ambiguity and noise. Thus, it is critical that AI systems take into account epistemic uncertainty over the human's true intent. My talk will give an overview of my lab's progress along the following fundamental research areas: (1) efficiently maintaining uncertainty over human intent, (2) directly optimizing behavior to be robust to uncertainty over human intent, and (3) actively querying for additional human input to reduce uncertainty over human intent. 

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