A.4 EstimatingparameterswhenY(t)isunavailable New parameter estimators that leverage only the available data need to be derived whenY(t) is unavailable. The derivation goes as follows: first, we eliminateY(t) from the model equations. The squared error of the estimated parameters are shown in Figure 1. First, we estimated the parameters separately for each individual. Second, we performed statistical analysis to find associations between the estimated parameters and the demographic variables.

The rapid integration of Large Language Models (LLMs) into various domains raises concerns about societal inequalities and information bias. This study examines biases in LLMs related to background, gender, and age, with a focus on their impact on decision-making and summarization tasks. Additionally, the research examines the cross-lingual propagation of these biases and evaluates the effectiveness of prompt-instructed mitigation strategies. Using an adapted version of the dataset by Tamkin et al. (2023) translated into Dutch, we created 151,200 unique prompts for the decision task and 176,400 for the summarisation task. Various demographic variables, instructions, salience levels, and languages were tested on GPT-3.5 and GPT-4o. Our analysis revealed that both models were significantly biased during decision-making, favouring female gender, younger ages, and certain backgrounds such as the African-American background. In contrast, the summarisation task showed minimal evidence of bias, though significant age-related differences emerged for GPT-3.5 in English. Cross-lingual analysis showed that bias patterns were broadly similar between English and Dutch, though notable differences were observed across specific demographic categories. The newly proposed mitigation instructions, while unable to eliminate biases completely, demonstrated potential in reducing them. The most effective instruction achieved a 27\% mean reduction in the gap between the most and least favorable demographics. Notably, contrary to GPT-3.5, GPT-4o displayed reduced biases for all prompts in English, indicating the specific potential for prompt-based mitigation within newer models. This research underscores the importance of cautious adoption of LLMs and context-specific bias testing, highlighting the need for continued development of effective mitigation strategies to ensure responsible deployment of AI.

Large Language Models (LLMs) are capable of generating opinions and propagating bias unknowingly, originating from unrepresentative and non-diverse data collection. Prior research has analysed these opinions with respect to the West, particularly the United States. However, insights thus produced may not be generalized in non-Western populations. With the widespread usage of LLM systems by users across several different walks of life, the cultural sensitivity of each generated output is of crucial interest. Our work proposes a novel method that quantitatively analyzes the opinions generated by LLMs, improving on previous work with regards to extracting the social demographics of the models. Our method measures the distance from an LLM's response to survey respondents, through Hamming Distance, to infer the demographic characteristics reflected in the model's outputs. We evaluate modern, open LLMs such as Llama and Mistral on surveys conducted in various global south countries, with a focus on India and other Asian nations, specifically assessing the model's performance on surveys related to religious tolerance and identity. Our analysis reveals that most open LLMs match a single homogeneous profile, varying across different countries/territories, which in turn raises questions about the risks of LLMs promoting a hegemonic worldview, and undermining perspectives of different minorities. Our framework may also be useful for future research investigating the complex intersection between training data, model architecture, and the resulting biases reflected in LLM outputs, particularly concerning sensitive topics like religious tolerance and identity.

Alternative data provides valuable insights for lenders to evaluate a borrower's creditworthiness, which could help expand credit access to underserved groups and lower costs for borrowers. But some forms of alternative data have historically been excluded from credit underwriting because it could act as an illegal proxy for a protected class like race or gender, causing redlining. We propose a method for applying causal inference to a supervised machine learning model to debias alternative data so that it might be used for credit underwriting. We demonstrate how our algorithm can be used against a public credit dataset to improve model accuracy across different racial groups, while providing theoretically robust nondiscrimination guarantees.

Generating social networks is essential for many applications, such as epidemic modeling and social simulations. Prior approaches either involve deep learning models, which require many observed networks for training, or stylized models, which are limited in their realism and flexibility. In contrast, LLMs offer the potential for zero-shot and flexible network generation. However, two key questions are: (1) are LLM's generated networks realistic, and (2) what are risks of bias, given the importance of demographics in forming social ties? To answer these questions, we develop three prompting methods for network generation and compare the generated networks to real social networks. We find that more realistic networks are generated with "local" methods, where the LLM constructs relations for one persona at a time, compared to "global" methods that construct the entire network at once. We also find that the generated networks match real networks on many characteristics, including density, clustering, community structure, and degree. However, we find that LLMs emphasize political homophily over all other types of homophily and overestimate political homophily relative to real-world measures.

Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities. Digitally acquired data can accurately capture complex human activity, but it lacks the interpretability of demographic data. In this paper, we study a privacy-enhanced dataset of the mobility visitation patterns of 1.2 million people to 1.1 million places in 11 metro areas in the U.S. to detect the latent mobility behaviors and lifestyles in the largest American cities. Despite the considerable complexity of mobility visitations, we found that lifestyles can be automatically decomposed into only 12 latent interpretable activity behaviors on how people combine shopping, eating, working, or using their free time. Rather than describing individuals with a single lifestyle, we find that city dwellers' behavior is a mixture of those behaviors. Those detected latent activity behaviors are equally present across cities and cannot be fully explained by main demographic features. Finally, we find those latent behaviors are associated with dynamics like experienced income segregation, transportation, or healthy behaviors in cities, even after controlling for demographic features. Our results signal the importance of complementing traditional census data with activity behaviors to understand urban dynamics.

With the severity of the COVID-19 outbreak, we characterize the nature of the growth trajectories of counties in the United States using a novel combination of spectral clustering and the correlation matrix. As the U.S. and the rest of the world are experiencing a severe second wave of infections, the importance of assigning growth membership to counties and understanding the determinants of the growth are increasingly evident. Subsequently, we select the demographic features that are most statistically significant in distinguishing the communities. Lastly, we effectively predict the future growth of a given county with an LSTM using three social distancing scores. This comprehensive study captures the nature of counties' growth in cases at a very micro-level using growth communities, demographic factors, and social distancing performance to help government agencies utilize known information to make appropriate decisions regarding which potential counties to target resources and funding to.

The COVID-19 pandemic has magnified an already existing trend of people looking for healthcare solutions online. One class of solutions are symptom checkers, which have become very popular in the context of COVID-19. Traditional symptom checkers, however, are based on manually curated expert systems that are inflexible and hard to modify, especially in a quickly changing situation like the one we are facing today. That is why all COVID-19 existing solutions are manual symptom checkers that can only estimate the probability of this disease and cannot contemplate alternative hypothesis or come up with a differential diagnosis. While machine learning offers an alternative, the lack of reliable data does not make it easy to apply to COVID-19 either. In this paper we present an approach that combines the strengths of traditional AI expert systems and novel deep learning models. In doing so we can leverage prior knowledge as well as any amount of existing data to quickly derive models that best adapt to the current state of the world and latest scientific knowledge. We use the approach to train a COVID-19 aware differential diagnosis model that can be used for medical decision support both for doctors or patients. We show that our approach is able to accurately model new incoming data about COVID-19 while still preserving accuracy on conditions that had been modeled in the past. While our approach shows evident and clear advantages for an extreme situation like the one we are currently facing, we also show that its flexibility generalizes beyond this concrete, but very important, example.

Machine learning is increasingly being used to predict individuals' attitudes, behaviors, and preferences across an array of applications -- from personalized marketing to precision medicine. Unsurprisingly, given the speed of change and ever-increasing complexity, there have been several recent high-profile examples of "machine learning gone wrong." A chatbot trained using Twitter was shut down after only a single day because of its obscene and inflammatory tweets. Machine learning models used in a popular search engine struggle to differentiate human images from those of gorillas, and show female searchers ads for lower paying jobs relative to male users. More recently, a study compared the commonly used crime risk analysis tool COMPAS against recidivism predictions from 400 untrained workers recruited via Amazon Mechanical Turk.

A number of approaches have been advanced for taking data about a user's likes and dislikes and generating a general profile of the user. These profiles can be used to retrieve documents matching user interests; recommend music, movies, or other similar products; or carry out other tasks in a specialized fashion. This article presents a fundamentally new method for generating user profiles that takes advantage of a large-scale database of demographic data. These data are used to generalize user-specified data along the patterns common across the population, including areas not represented in the user's original data. The input data most often take the form of samples of the user's interests or preferences in a given area, and the profile is a generalization of these data that can be used generatively to carry out tasks on behalf of the user.