Contributions in Merlin

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The Creator Economy faces concerning levels of unfairness. Content creators (CCs) publicly accuse platforms of purposefully reducing the visibility of their content based on protected attributes, while platforms place the blame on viewer biases. Meanwhile, prior work warns about the “rich-get-richer” effect perpetuated by existing popularity biases in recommender systems: Any initial advantage in visibility will likely be exacerbated over time. What remains unclear is how the biases based on protected attributes from platforms and viewers interact and contribute to the observed inequality in the context of popularity-biased recommender systems. The difficulty of the question lies in the complexity and opacity of the system. To overcome this challenge, we design a simple agent-based model (ABM) that unifies the platform systems which allocate the visibility of CCs (e.g., recommender systems, moderation) into a single popularity-based function, which we call the visibility allocation system (VAS). Through simulations, we find that although viewer homophilic biases do alone create inequalities, small levels of additional biases in VAS are more harmful. From the perspective of interventions, our results suggest that (a) attempts to reduce attribute-biases in moderation and recommendations should precede those reducing viewers’ homophilic tendencies, (b) decreasing the popularity-biases in VAS decreases but not eliminates inequalities, (c) boosting the visibility of protected CCs to overcome viewers’ homophily with respect to one fairness metric is unlikely to produce fair outcomes with respect to all metrics, and (d) the process is also unfair for viewers and this unfairness could be overcome through the same interventions. More generally, this work demonstrates the potential of using ABMs to better understand the causes and effects of biases and interventions within complex sociotechnical systems.

News recommender systems are an increasingly popular field of study that attracts a growing interdisciplinary research community. As these systems play an essential role in our daily lives, the mechanisms behind their curation processes are under scrutiny. In the area of personalized news, many platforms make design choices driven by economic incentives. In contrast to such systems that optimize for financial gain, there can be norm-driven diversity systems that prioritize normative and democratic goals. However, their impact on users in terms of inducing behavioral change or influencing knowledge is still understudied. In this paper, we contribute to the field of news recommender system design by conducting a user study that examines the impact of these normative approaches. We a.) operationalize the notion of a deliberative public sphere for news recommendations, show b.) the impact on news usage, and c.) the influence on political knowledge, attitudes and voting behavior. We find that exposure to small parties is associated with an increase in knowledge about their candidates and that intensive news consumption about a party can change the direction of attitudes of readers towards the issues of the party.

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A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its objective better but that it optimizes a better objective. OC decomposes the problem into planning and control with an explicit intermediate representation, such as a trajectory, that serves as an interface. This decomposition limits the range of behaviors that can be expressed by the controller, leading to inferior control performance when facing unmodeled effects. In contrast, RL can directly optimize a task-level objective and can leverage domain randomization to cope with model uncertainty, allowing the discovery of more robust control responses. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour. Our policy achieved superhuman control within minutes of training on a standard workstation. This work presents a milestone in agile robotics and sheds light on the role of RL and OC in robot control.

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What are the implications of an aging population for financial stability? To examine this question, we exploit geographic variation in aging across U.S. counties. We establish that banks with higher exposure to aging counties increase loan-to-income ratios. Laxer lending standards lead to higher nonperforming loans during downturns, suggesting higher credit risk. Inspecting the mechanism shows that aging drives risk-taking through two contemporaneous channels: deposit inflows due to seniors’ propensity to save in deposits; and depressed local investment opportunities due to seniors’ lower credit demand. Banks thus look for riskier clients, especially in counties where they operate no branches.

Large language models (LLMs) like ChatGPT recently gained interest across all walks of life with their human-like quality in textual responses. Despite their success in research, healthcare, or education, LLMs frequently include incorrect information, called hallucinations, in their responses. These hallucinations could influence users to trust fake news or change their general beliefs. Therefore, we investigate mitigation strategies desired by users to enable identification of LLM hallucinations. To achieve this goal, we conduct a participatory design study where everyday users design interface features which are then assessed for their feasibility by machine learning (ML) experts. We find that many of the desired features are well-perceived by ML experts but are also considered as difficult to implement. Finally, we provide a list of desired features that should serve as a basis for mitigating the effect of LLM hallucinations on users.

This thesis aims to analyze the impact of occlusion on the quality of semantic segmentation methods for point cloud data. Occlusion is a prevalent phenomenon in 3D scenes, where objects often overlap or obstruct each other. This can significantly compromise the quality and integrity of data, leading to inaccuracies in semantic segmentation. While the issue of occlusion has garnered attention in 3D data processing, current research on how different occlusion levels impact the quality of semantic segmentation is rare. Specifically, there is a palpable gap in understanding how to quantify occlusion in the scene and how this characteristic influence the performance of advanced semantic segmentation software like the Minkowski Engine. To bridge the research gap, we proposed a novel metric to quantify the occlusion level of a scene. We then applied this metric to analyze the impact of occlusion on the quality of semantic segmentation methods for point cloud data. Our results show that the occlusion level of a scene has limited impact to the quality of semantic segmentation.

The rise of Blockchain technology and cryptocurrencies has enabled the creation of decentralized alternative payment systems, without the need for third party financial institutions. However, this decentralization and pseudo-anonymity have also facilitated the emergence of darknet markets (DNMs) offering illicit products. This study introduces the"grayscale diffusion framework", with the aim of modeling and understanding the propagation of dark assets in the Bitcoin network. Formulated and implemented with a combination of on-chain and off-chain data, this approach utilizes address clustering, haircut tainting, and community partitioning to offer a unique analysis perspective on dark asset proliferation across the Bitcoin blockchain. The framework uncovers interesting patterns in the assortative nature of Bitcoin transactions based on the darkness level of assets, pointing to the existence of non-random clusters and communities that facilitate dark asset diffusion. Our research not only addresses key questions related to the effective modeling and tracking of tainted asset flow, but also provides valuable insights. Keywords: Bitcoin, Address Clustering, Darknet Marketplaces, Gray-scale Diffusion, User Transaction Networks.