Individual fairness methods depart from the idea that similar observations should be treated similarly by a machine learning model, circumventing some of the shortcomings of group fairness tools. Nevertheless, many existing individual fairness approaches are either tailored to specific models or rely on a series of ad hoc decisions to determine model bias. In this paper, we propose an individual fairness-inspired, inference-based bias detection pipeline. Our method is model-agnostic, suited for all data types, avoids commonly used ad-hoc thresholds and decisions, and provides an intuitive scale to indicate how biased the assessed model is. We propose a model ensemble approach for our bias detection tool, consisting of: (i) building a proximity matrix with random forests based on features and output; (ii) inputting it into a bayesian network method to cluster similar observations; (iii) performing within-cluster inference to test the hypothesis that the model is treating similar observations similarly; and (iv) aggregating the cluster tests up with multiple hypothesis test correction. In addition to providing a single statistical p-value for the null hypothesis that the model is unbiased based on individual fairness, we further create a scale that measures the amount of bias against minorities carried by the model of interest, making the overall p-value more interpretable to decision-makers. We apply our methodology to assess bias in the mortgage industry, and we provide an open-source Python package for our methods.

Targeted ads are important sources of information that shape how individuals get access to various opportunities and resources. Targeted advertising also introduces bias and discrimination against groups of users along the lines of race, gender, and so on. Audits of targeted advertising algorithms in various domains like employment have provided evidence for gender-based and race-based discrimination in ad delivery by focusing on the association between user profile and ad type and content. Building on prior work, our work provides an account for how biases reflected in targeted advertising reinforce historical inequalities in the housing and mortgage sectors in metropolitan cities. To demonstrate, we collected web traffic data within Google’s search engine results page at the zip code level in New York City in 2020 from a third party vendor. We combined the data with recently released appraisal record data at the census tract level, which serves as a proxy for home values. By triangulating those datasets with census data, we analyze how communities are differentially targeted using spatial analysis, clustering, and regression models. This work reveals how biased distribution of information and resources impacts individuals’ access to economic opportunities.

Social media have been widely used for election campaigning in the past two decades. Prior work on political communication in India has focused mainly on social media activity and interactions between political elites at the national level. Yet, social media are also significant in state-level Indian politics in ways that have been largely overlooked in scholarship. For instance, coalitions among parties are an important factor in Indian electoral politics where intra-party relations among state-level parties may differ from those at the national level. Our study fills this void with a descriptive and causal analysis of how an electoral alliance’s break-up shaped its supporters’ behavior on social media and how they engage with parties and politicians.

We use data from the four major parties in the Indian state of Maharashtra — the BJP, the Shiv Sena, the INC, and the NCP — during the 2019-2020 period, to analyze how online partisans responded to the formation of an alliance between the latter three parties. We test our hypotheses using two methods: (1) a partisanship model based on a vectorized representation of users’ liking behavior, and (2) proximity to parties in the retweet network of users. Based on how users’ partisan lean and their retweet proximity to parties change in response to the coalition realignment, we find that supporters of the Shiv Sena became more aligned with the INC/NCP, and away from their traditional ally — the BJP. In contrast with European settings, our findings suggest that partisan loyalties remained largely intact when ideologically divergent coalitions were formed in India. Finally, we find that among non-elites in India, party loyalty was a stronger predictor of social media liking behavior, compared to ideological preferences.

Advances in data science, computation, and Ai are expanding the presence and reach of surveillance systems in society. While there is existing literature concerning school-based surveillance of students by educators and school systems, there has been limited attention given to the ethical implications of anonymous report systems which leverage student reporting on themselves and their peers. This is concerning given that 50% of K-12 schools employ one or more anonymous reporting systems (ARS) to enhance safety and monitor potential risks for students, educators, and the broader school community. These ARS platforms operate on a “see-something-say-something” principle, encouraging students to share their knowledge of concerning behaviors or events, which is then forwarded to the respective school, emergency and law enforcement authorities. Information submitted through these systems may encompass identifying details (such as names), socio-demographic data (including race and gender), health-related information (such as suicidality), and behavioral observations (such as carrying a weapon). While ARS submissions have demonstrated efficacy in preventing school shootings and adolescent suicides, there are anecdotal instances where ARS unintentionally exposed information about a student to the wider community. Beyond unintentional exposure, data collected through state-owned ARS could potentially be exploited by school systems in ways not originally proposed, as has been evident in other school-surveillance programs such as Ai assisted social media monitoring. Further, as ARS uses a peer-report system and leverages the in-depth knowledge that peers have of one another, unintended use of ARS could further elevate risk of victimization for BIPOC students, LGBTQIA+ students, and those seeking reproductive healthcare. This presentation aims to elucidate the dichotomy between the benefits offered by ARS and peer-reported systems and the genuine risks faced by youth who submit reports and are subject to them.

Summary: Antibiotic resistance (AR) is a pressing global health concern; new treatments are urgently needed. Drug combinations are a promising solution to this problem, but they are designed empirically, driven by clinical intuition, leading to suboptimal results and increased AR. The combinatorial explosion further aggravates the problem. Therefore, there is a need for an efficient data-driven approach, to facilitate the development of these treatments. We have developed a mechanistic approach that combines multi-omics data with artificial neural networks (ANN) to (a) accurately predict multi-way drug interactions, (b) accommodate drug-resistant bacterial strains, (c) gain insights into a diverse set of pathways predictive of drug combinations at the molecular level, and (d) accounts for drug toxicity profiles to design safer treatments.

Methods: The approach involves a three-step process, (1) generating feature profiles for individual drug treatments using multi-omics data such as chemogenomics and transcriptomics followed by (2) preprocessing to compute joint profiles for a combination accounting for similarity and uniqueness among drugs in the treatment, and (3) feeding the information to the metabolism-inspired neural network algorithm for model development, and evaluating performance.

Main results: The mechanistic approach accurately and significantly predicted multi-drug interactions in E. coli (R = 0.58, p~10-14) and M. tuberculosis (R = 0.42, p~10-8). It was extended to predict drug combination outcomes in critical pathogens, S. typhimurium and P. aeruginosa. It identified Alternate Carbon Metabolism in E. coli and Fatty Acid Metabolism in M. tuberculosis along with Transport mechanisms in both, as the most important pathways governing drug resistance, in agreement with literature evidence.

Impact: As novel treatments are not readily discovered, it is crucial to design treatments using approved FDA drugs. Our developed algorithm aims to provide an innovative and unique perspective on utilizing machine learning in guiding the development of multi-drug treatments using FDA drugs.

Exploratory behaviors in mice are used to obtain information about the emotional or cognitive state of the animal. Dorsal hippocampus is one brain region that controls exploratory behavior. Here we developed methods based on neural decoding and correlated neural networks to identify population dynamics of dorsal CA1 neurons underlying specific exploratory behaviors. Calcium activity of dorsal CA1 neurons was recorded using a miniature microscope in freely behaving mice during different exploratory tasks: bright open field exploration, and familiar or novel object exploration. We identified populations of neurons with activity linked to risk-taking behavior in the open field (“center” cells), and neurons linked to object exploration by taking the ratio of the average calcium amplitude in “center” to “not center” , “object exploration” to “no object exploration” time period. Neurons with a ratio greater than the 99th percentile and less than the 1st percentile of shuffle data distribution were considered behavior-sensitive neurons. The neural correlation analysis of behavior sensitive neurons revealed that behavior-sensitive neurons showed highly correlated activity during their respective behaviors; for example, center-sensitive neurons showed highly correlated activity during center exploration but not at other times. We implemented a neural decoding method based on support vector machine, supervised learning where the model where trained using behavior sensitive neurons. The proposed neural decoding method is able to predict neural activity from various behavioral states, and prior knowledge about behavioral sensitive neurons can improve the classification of neural activity. Together, these methods reveal meaningful neural activity patterns during relevant hippocampal-dependent behaviors.

Directed evolution is a widespread technique that enables the acquisition of novel functionalities and highly efficient variants of natural protein sequences. This process involves rounds of large-scale sequence expression and specifically designed activity assays. While effective, the approach requires significant time and resources for extensive explorations of sequence-function spaces and must be optimized for new target proteins. Recent endeavors have displayed that integrating machine-learning (ML) with directed evolution delivers a rapid, targeted exploration of sequence-function spaces. In this study, we incorporate a molecular modeling (MM) pipeline with machine-learning directed evolution (MLDE), aiming to minimize the necessary mutations and amplify the hit rate of observed mutations. Our MM+MLDE protocol facilitated the identification of vital second-sphere residues that modulate enantioselectivity and reactivity in fungal flavin-dependent monoxygenases, utilizing a compact set of targeted site-directed mutants. We further discuss how to integrate active learning into successive rounds of expression results by training convolutional neural networks on predicted protein-ligand structures against the experimental data, allowing for more accurate predictions for subsequent variant sets. It is hoped that our innovative protocols will encourage the broader incorporation of MM and ML in directed evolution campaigns.

Deep linear networks have proven to be powerful models for solving low-rank matrix recovery problems. Their effectiveness is partially attributed to the implicit bias in the learning dynamics of over-parameterized models, which favors certain low-rank solutions that generalize well. However, such over-parameterization often leads to a substantial increase in computational complexity, limiting their applicability to real-world problems at scale. In this paper, we propose a simple, yet effective technique to compress deep linear networks, which significantly reduces computational overheads without compromising model quality. Our approach involves projecting the deep linear network onto carefully constructed low-dimensional subspaces, drawing inspiration from its learning dynamics. Remarkably, our compressed network converges faster than the original network, consistently yielding smaller recovery errors throughout all iterations of gradient descent. We substantiate this observation by developing theory focused on deep matrix factorization problem, and by conducting empirical evaluations on two canonical matrix recovery problems: matrix sensing and completion. Further, we demonstrate how the use of compressed network can improve the generalization of deep nonlinear networks as well. Overall, we observed that our compression technique accelerates the training process by more than $2\times$ across a broad range of problems.

An accurate and robust traffic light handling model is crucial for the safe and efficient operation of autonomous vehicles (AVs). Current L4 AVs on the market leverage Vehicle-to-Everything (V2X) infrastructure in the form of Signal Phase and Timing (SPaT) messages to receive crucial information for path planning, such as states of traffic lights and how long they will stay in those states. However, current state-of-the-art perception systems, particularly those with a heavy emphasis on V2X infrastructure and SPaT messages, have several limitations, such as missing SPaT messages or incorrect SPaT messages. In these situations, deep learning models can be utilized to handle predictions more accurately and efficiently. To create a more robust traffic light handling system, a deep learning model was developed by leveraging semantic segmentation algorithms to detect and classify traffic lights. A variety of architectures were tested to achieve a model that performed with high accuracy on proprietary testing data. The deep learning model was ultimately integrated with the existing V2X infrastructure, and a control flow logic was created to evaluate the perception model under both ideal and realistic conditions for accuracy and efficiency. By developing an accurate and efficient deep learning model to handle situations that lack accurate SPaT messages, a more accurate perception model was achieved that further improves the safety of an autonomous vehicle.

In this study, we explore multi-label learning, an important subfield of supervised learning that aims to predict multiple labels from a single input data point. This research investigates the training of deep neural networks for multi-label learning through the lens of neural collapse, an intriguing phenomenon that occurs during the terminal phase of training. Previously, neural collapse (NC) has been investigated both theoretically and empirically in the context of multi-class classification. For last-layer features, it has been demonstrated that (i) the variability of features within classes collapses to zero, and (ii) the feature means between classes become maximally and equally separated. In this work, we demonstrate that the NC phenomenon can be extended to multi-label learning, revealing that the “pick-all-label” training formulation for multi-label learning exhibits the NC phenomenon in a more general context. Specifically, under the natural analog of the unconstrained feature model, we establish that the only global minimizers of the pick-all-label loss display the same equi-angular tight frame (ETF) geometry. Additionally, scaled average of the ETF are used to represent the features of samples with multiple labels. We also provide empirical evidence to support our investigation into training deep neural networks on multi-label datasets, resulting in improved training efficiency.

Advances in 3D imaging technologies have given scientists access to terabyte-scale data on the anatomical structure (morphology) of organisms. In many ways, this exponential rise in data on organismal form parallels the orders-of-magnitude rise in genomic data availability. However, existing analytical and computational frameworks cannot cope with data at this size. In fact, it is not even clear how to represent this data such that it can be analyzed at all. Nonetheless, identifying key evolutionary features of animal form is an essential question for biologists and these high-dimensional data are highly precise representations of morphological diversity. Therefore, there exists a pressing need to address two primary questions: (1) how do we represent such complex, multidimensional data, and (2) how can we learn about the process which produced the observed form? By addressing these questions we would gain novel insights into evolutionary processes which have given rise to the tremendous diversity of living organisms that we see today. My work as a Schmidt Futures AI in Science Post-doctoral fellow is aimed at using deep-neural networks as a framework for understanding major evolutionary transitions in animal body forms. These neural networks are still in their nascent stages, but show tremendous promise for outperforming traditional methods in a variety of downstream tasks of interest to evolutionary biologists and ecologists. For example, preliminary results suggest that the non-linear representation of complex form provided by deep learning methods can more accurately estimate extinct forms than traditional methods, even when fossils are not included in the training dataset. For the first time, these methods will enable us to understand how evolution generated major innovations (e.g., novel morphologies and specializations) in a “data space” that is closely aligned with the complex geometry of real animals.

Anthropogenic pressures are causing unparalleled declines in biodiversity. Environmental resilience is closely intertwined with the protection and advancement of biodiversity across various scales, ranging from local ecosystems to the global biosphere. Biodiversity plays a pivotal role in enhancing the ability of ecosystems to endure and rebound from a variety of disturbances. Addressing this worldwide biodiversity crisis necessitates concentrated conservation initiatives, which, unfortunately, are constrained by substantial costs and limited resources. The absence of comprehensive biodiversity data can obscure declines in populations and potential extinctions. Consequently, there is an immediate requirement for cost-effective and scalable methods for monitoring biodiversity. One promising avenue in this endeavour is role of social media platforms. Social media enables the integration of diverse data types, including images, text, audio, and video, through multimodal approaches, thereby reshaping the landscape of conservation research. By harnessing AI technologies such as computer vision, natural language processing, and spatial-temporal analysis, we can extract valuable insights from social media posts. This multimodal approach to biodiversity monitoring introduces innovative possibilities, such as tracking changes in the timing and distribution of biodiversity events and monitoring areas impacted by invasive species. These insights can, in turn, facilitate the development of efficient and large-scale conservation strategies, contributing significantly to the augmentation of environmental resilience.

We present a new algorithm for incrementally updating the tensor train decomposition of a stream of tensor data. This new algorithm, called the tensor train incremental core expansion (TT-ICE) improves upon the current state-of-the-art algorithms for compressing in tensor train format by developing a new adaptive approach that incurs significantly slower rank growth and guarantees compression accuracy. This capability is achieved by limiting the number of new vectors appended to the TT-cores of an existing accumulation tensor after each data increment. These vectors represent directions orthogonal to the span of existing cores and are limited to those needed to represent a newly arrived tensor to a target accuracy. We provide two versions of the algorithm: TT-ICE and TT-ICE accelerated with heuristics (TT-ICE∗). We empirically demonstrate the performance of the algorithms in compressing large-scale video and scientific simulation datasets. Compared to existing approaches that also use rank adaptation, TT-ICE∗ achieves 57× higher compression and up to 95% reduction in computational time.

Recent advances in computing and camera technology have pushed astronomical surveys into the realm of big data. Beginning in 2023, the Legacy Survey of Space and Time (LSST) will image half of the sky every three nights, for 300 nights per year. The resulting dataset will have immense potential for discovering solar system bodies, especially if we can manage to discover objects fainter than the single-image threshold.

The traditional method for finding sub-threshold solar system bodies is a technique called shift-and-stack, in which one stacks images along the orbit of a moving object. When the signal from the object is added coherently in enough images, it becomes detectable. Shift-and-stack searches have only ever been accomplished in sets of images taken in a single night, where the apparent motion of the solar system bodies remains linear. When the apparent motion becomes nonlinear, the number of potential trajectories grows exponentially. If we remain constrained to time scales of a single night, our ability to find faint objects will be limited by the size of our telescopes. In order to make the most effective use of our data, we are developing a technique (heliostack) that uses software to combine images taken days, weeks, or even months apart. Our technique combines expert domain knowledge with recent advancements in computing and AI technology in order to overcome the computational and logistical difficulties of the search. It will be critical to develop highly efficient convolutional neural nets for rejecting false positive detections.

If this project succeeds, the potential payoff is an increase in LSST’s yield of solar system objects by as much as an order of magnitude. Such a catalog would provide invaluable insight about the formation of solar systems, the possible presence of yet-undetected planets in our own solar system, and much more.