bluuebunny/crossref_metadata_2025 huggingface dataset:
Thirty-six new species records of moths (Lepidoptera) for Colombia
Authors: Martínez Blanca , Rodríguez-Mahecha José Vicente , Bernal Rodrigo
Published: November 2024

Thirty-six species and three genera of moths are recorded for the ﬁrst time in Colombia, in the families Notodontidae (31 spp., 2 genera), Saturniidae (4 spp., 1 genus), and Sphingidae (1 sp.). All records are based on photographs of live individuals attracted to white and UV lights.
On The Stability of Moral Preferences: A Problem with Computational Elicitation Methods
Authors: Boerstler Kyle , Keswani Vijay , Chan Lok , Schaich Borg Jana , Conitzer Vincent , Heidari Hoda , Sinnott-Armstrong Walter
Published: November 2024

Preference elicitation frameworks feature heavily in the research on participatory ethical AI tools and provide a viable mechanism to enquire and incorporate the moral values of various stakeholders. As part of the elicitation process, surveys about moral preferences, opinions, and judgments are typically administered only once to each participant. This methodological practice is reasonable if participants’ responses are stable over time such that, all other things being held constant, their responses today will be the same as their responses to the same questions at a later time. However, we do not know how often that is the case. It is possible that participants’ true moral preferences change, are subject to temporary moods or whims, or are influenced by environmental factors we don’t track. If participants’ moral responses are unstable in such ways, it would raise important methodological and theoretical issues for how participants’ true moral preferences, opinions, and judgments can be ascertained. We address this possibility here by asking the same survey participants the same moral questions about which patient should receive a kidney when only one is available ten times in ten different sessions over two weeks, varying only presentation order across sessions. We measured how often participants gave different responses to simple (Study One) and more complicated (Study Two) controversial and uncontroversial repeated scenarios. On average, the fraction of times participants changed their responses to controversial scenarios (i.e., were unstable) was around 10-18% (±14-15%) across studies, and this instability is observed to have positive associations with response time and decision-making difficulty. We discuss the implications of these results for the efficacy of common moral preference elicitation methods, highlighting the role of response instability in potentially causing value misalignment between the stakeholders and AI tools trained on their moral judgments.
Ecosystem Graphs: Documenting the Foundation Model Supply Chain
Authors: Bommasani Rishi , Soylu Dilara , Liao Thomas I. , Creel Kathleen A. , Liang Percy
Published: November 2024

Foundation models (e.g. GPT-4, Gemini, Llama 3) pervasively influence society, warranting greater understanding. While the models garner much attention, accurately characterizing their impact requires considering the broader sociotechnical ecosystem in which they are created and deployed. We propose Ecosystem Graphs as a documentation framework to centralize knowledge of this ecosystem. Ecosystem Graphs is composed of assets (datasets, models, applications) linked together by dependencies that indicate technical and social relationships. To supplement the graph structure, each asset is further enriched with fine-grained metadata, such as the model’s estimated training emissions or licensing guidelines. Since its release in March 2023, Ecosystem Graphs represents an ongoing effort to document 568 assets (112 datasets, 359 models, 97 applications) from 117 organizations. Ecosystem Graphs functions as a multifunctional resource: we discuss two major uses by the 2024 AI Index and the UK’s Competition and Markets Authority that demonstrate the value of Ecosystem Graphs.
Trustworthy Social Bias Measurement
Authors: Bommasani Rishi , Liang Percy
Published: November 2024

How do we design measures of social bias that we trust?
While prior work has introduced several measures, no measure has gained widespread trust: instead, mounting evidence argues we should distrust these measures. In this work, we design bias measures that warrant trust based on the cross-disciplinary theory of measurement modeling. To combat the frequently fuzzy treatment of social bias in natural language processing, we explicitly define social bias, grounded in principles drawn from social science research. We operationalize our definition by proposing a general bias measurement framework DivDist, which we use to instantiate 5 concrete bias measures. To validate our measures, we propose a rigorous testing protocol with 8 testing criteria (e.g. predictive validity: do measures predict biases in US employment?). Through our testing, we demonstrate considerable evidence to trust our measures, showing they overcome conceptual, technical, and empirical deficiencies present in prior measures.
Algorithm-Assisted Decision Making and Racial Disparities in Housing: A Study of the Allegheny Housing Assessment Tool
Authors: Cheng Lingwei , Drayton Cameron , Chouldechova Alexandra , Vaithianathan Rhema
Published: November 2024

The demand for housing assistance across the United States far exceeds the supply, leaving housing providers the task of prioritizing clients for receipt of this limited resource. To be eligible for federal funding, local homelessness systems are required to implement assessment tools as part of their prioritization processes. The Vulnerability Index Service Prioritization Decision Assistance Tool (VI-SPDAT) is the most commonly used assessment tool nationwide. Recent studies have criticized the VI-SPDAT as exhibiting racial bias, which may lead to unwarranted racial disparities in housing provision. In response to these criticisms, some jurisdictions have developed alternative tools, such as the Allegheny Housing Assessment (AHA), which uses algorithms to assess clients' risk levels. Drawing on data from its deployment, we conduct descriptive and quantitative analyses to evaluate whether replacing the VI-SPDAT with the AHA affects racial disparities in housing allocation. We find that the VI-SPDAT tended to assign higher risk scores to white clients and lower risk scores to Black clients, and that white clients were served at a higher rates pre-AHA deployment. While post-deployment service decisions became better aligned with the AHA score, and the distribution of AHA scores is similar across racial groups, we do not find evidence of a corresponding decrease in disparities in service rates. We attribute the persistent disparity to the use of Alt-AHA, a survey-based tool that is used in cases of low data quality, as well as group differences in eligibility-related factors, such as chronic homelessness and veteran status. We discuss the implications for housing service systems seeking to reduce racial disparities in their service delivery.
Scaling Laws Do Not Scale
Authors: Diaz Fernando , Madaio Michael
Published: November 2024

Recent work has advocated for training AI models on ever-larger datasets, arguing that as the size of a dataset increases, the performance of a model trained on that dataset will correspondingly increase (referred to as “scaling laws”). In this paper, we draw on literature from the social sciences and machine learning to critically interrogate these claims. We argue that this scaling law relationship depends on metrics used to measure performance that may not correspond with how different groups of people perceive the quality of models' output. As the size of datasets used to train large AI models grows and AI systems impact ever larger groups of people, the number of distinct communities represented in training or evaluation datasets grows. It is thus even more likely that communities represented in datasets may have values or preferences not reflected in (or at odds with) the metrics used to evaluate model performance in scaling laws. Different communities may also have values in tension with each other, leading to difficult, potentially irreconcilable choices about metrics used for model evaluations---threatening the validity of claims that model performance is improving at scale. We end the paper with implications for AI development: that the motivation for scraping ever-larger datasets may be based on fundamentally flawed assumptions about model performance. That is, models may not, in fact, continue to improve as the datasets get larger---at least not for all people or communities impacted by those models. We suggest opportunities for the field to rethink norms and values in AI development, resisting claims for universality of large models, fostering more local, small-scale designs, and other ways to resist the impetus towards scale in AI.
SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)ata
Authors: Diaz Mark , Dev Sunipa , Reif Emily , Denton Emily , Prabhakaran Vinodkumar
Published: November 2024

Decisions about how to responsibly collect, use and document data often rely upon understanding how people are represented in data. Yet, the unlabeled nature and scale of data used in foundation model development poses a direct challenge to systematic analyses of downstream risks, such as representational harms. We provide a framework designed to help RAI practitioners more easily plan and structure analyses of how people are represented in unstructured data and identify downstream risks. The framework is organized into groups of analyses that map to 3 basic questions: 1) Who is represented in the data, 2) What content is in the data, and 3) How are the two associated. We use the framework to analyze human representation in two commonly used datasets: the Common Crawl web corpus (C4) of 356 billion tokens, and the LAION-400M dataset of 400 million text-image pairs, both developed in the English language. We illustrate how the framework informs action steps for hypothetical teams faced with data use, development, and documentation decisions. Ultimately, the framework structures human representation analyses and maps out analysis planning considerations, goals, and risk mitigation actions at different stages of dataset and model development.
Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors
Authors: Ding Xueying , Xi Rui , Akoglu Leman
Published: November 2024

The astonishing successes of ML have raised growing concern for the fairness of modern methods when deployed in real world settings. However, studies on fairness have mostly focused on supervised ML, while unsupervised outlier detection (OD), with numerous applications in finance, security, etc., have attracted little attention. While a few studies proposed fairness-enhanced OD algorithms, they remain agnostic to the underlying driving mechanisms or sources of unfairness. Even within the supervised ML literature, there exists debate on whether unfairness stems solely from algorithmic biases (i.e. design choices) or from the biases encoded in the data on which they are trained.

To close this gap, this work aims to shed light on the possible sources of unfairness in OD by auditing detection models under different data-centric factors.By injecting various known biases into the input data---as pertain to sample size disparity, under-representation, feature measurement noise, and group membership obfuscation---we find that the OD algorithms under the study all exhibit fairness pitfalls, although differing in which types of data bias they are more susceptible to. Most notable of our study is to demonstrate that OD algorithm bias is not merely a data bias problem. A key realization is that the data properties that emerge from bias injection could as well be organic---as pertain to natural group differences w.r.t. sparsity, base rate, variance, and multi-modality. Either natural or biased, such data properties can
give rise to unfairness as they interact with certain algorithmic design choices.

Our work provides a deeper understanding of the possible sources of OD unfairness, and
serves as a framework for assessing the unfairness of future OD algorithms under specific data-centric factors. It also paves the way for future work on mitigation strategies by underscoring the susceptibility of various design choices.
Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approach
Authors: Ghosh Sourojit , Narayanan Venkit Pranav , Gautam Sanjana , Wilson Shomir , Caliskan Aylin
Published: November 2024

Our research investigates the impact of Generative Artificial Intelligence (GAI) models, specifically text-to-image generators (T2Is), on the representation of non-Western cultures, with a focus on Indian contexts. Despite the transformative potential of T2Is in content creation, concerns have arisen regarding biases that may lead to misrepresentations and marginalizations. Through a Non-Western community-centered approach
and grounded theory analysis of 5 focus groups from diverse Indian subcultures, we explore how T2I outputs to English input prompts depict Indian culture and its subcultures, uncovering novel representational harms such as exoticism and cultural misappropriation. These findings highlight the urgent need for inclusive and culturally sensitive T2I systems. We propose design guidelines informed by a sociotechnical perspective, contributing to the development of more equitable and representative GAI technologies globally. Our work underscores the necessity of adopting a community-centered approach to comprehend the sociotechnical dynamics of these models, complementing existing work in this space while identifying and addressing the potential negative repercussions and harms that may arise as these models are deployed on a global scale.
AI Failure Loops in Feminized Labor: Understanding the Interplay of Workplace AI and Occupational Devaluation
Authors: Kawakami Anna , Taylor Jordan , Fox Sarah , Zhu Haiyi , Holstein Ken
Published: November 2024

A growing body of literature has focused on understanding and addressing workplace AI design failures. However, past work has largely overlooked the role of occupational devaluation in shaping the dynamics of AI development and deployment. In this paper, we examine the case of feminized labor: a class of devalued occupations historically misnomered as ``women's work,'' such as social work, K-12 teaching, and home healthcare. Drawing on literature on AI deployments in feminized labor contexts, we conceptualize AI Failure Loops: a set of interwoven, socio-technical failures that help explain how the systemic devaluation of workers' expertise negatively impacts, and is impacted by, AI design, evaluation, and governance practices. These failures demonstrate how misjudgments on the automatability of workers' skills can lead to AI deployments that fail to bring value and, instead, further diminish the visibility of workers' expertise. We discuss research and design implications for workplace AI, especially for devalued occupations.
Epistemic Injustice in Generative AI
Authors: Kay Jackie , Kasirzadeh Atoosa , Mohamed Shakir
Published: November 2024

This paper investigates how generative AI can potentially undermine the integrity of collective knowledge and the processes we rely on to acquire, assess, and trust information, posing a significant threat to our knowledge ecosystem and democratic discourse. Grounded in social and political philosophy, we introduce the concept of generative algorithmic epistemic injustice. We identify four key dimensions of this phenomenon: amplified and manipulative testimonial injustice, along with hermeneutical ignorance and access injustice. We illustrate each dimension with real-world examples that reveal how generative AI can produce or amplify misinformation, perpetuate representational harm, and create epistemic inequities, particularly in multilingual contexts. By highlighting these injustices, we aim to inform the development of epistemically just generative AI systems, proposing strategies for resistance, system design principles, and two approaches that leverage generative AI to foster a more equitable information ecosystem, thereby safeguarding democratic values and the integrity of knowledge production.
Observing Context Improves Disparity Estimation when Race is Unobserved
Authors: Kwegyir-Aggrey Kweku , Durvasula Naveen , Wang Jennifer , Venkatasubramanian Suresh
Published: November 2024

In many domains, it is difficult to obtain the race data that is required to estimate racial disparity. To address this problem, practitioners have adopted the use of proxy methods which predict race using non-protected covariates. However, these proxies often yield biased estimates, especially for minority groups, limiting their real-world utility. In this paper, we introduce two new contextual proxy models that advance existing methods by incorporating contextual features in order to improve race estimates. We show that these algorithms demonstrate significant performance improvements in estimating disparities, on real-world home loan and voter data. We establish that achieving unbiased disparity estimates with contextual proxies relies on mean-consistency, a calibration-like condition.
Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art
Authors: Lovato Juniper , Zimmerman Julia Witte , Smith Isabelle , Dodds Peter , Karson Jennifer L.
Published: November 2024

Generative AI tools are used to create art-like outputs and sometimes aid in the creative process. These tools have potential benefits for artists, but they also have the potential to harm the art workforce and infringe upon artistic and intellectual property rights. Without explicit consent from artists, Generative AI creators scrape artists' digital work to train Generative AI models and produce art-like outputs at scale. These outputs are now being used to compete with human artists in the marketplace as well as being used by some artists in their generative processes to create art. We surveyed 459 artists to investigate the tension between artists' opinions on Generative AI art's potential utility and harm. This study surveys artists' opinions on the utility and threat of Generative AI art models, fair practices in the disclosure of artistic works in AI art training models, ownership and rights of AI art derivatives, and fair compensation. Results show that a majority of artists believe creators should disclose what art is being used in AI training, that AI outputs should not belong to model creators, and express concerns about AI's impact on the art workforce and who profits from their art. We hope the results of this work will further meaningful collaboration and alignment between the art community and Generative AI researchers and developers.
Navigating Governance Paradigms: A Cross-Regional Comparative Study of Generative AI Governance Processes & Principles
Authors: Luna Jose , Tan Ivan , Xie Xiaofei , Jiang Lingxiao
Published: November 2024

As Generative Artificial Intelligence (GenAI) technologies
evolve at an unprecedented rate, global governance approaches
struggle to keep pace with the technology, highlighting
a critical issue in the governance adaptation of significant
challenges. Depicting the nuances of nascent and
diverse governance approaches based on risks, rules, outcomes,
principles, or a mix, across different regions around
the globe, is fundamental to discern discrepancies and convergences,
and to shed light on specific limitations that need
to be addressed, thereby facilitating the safe and trustworthy
adoption of GenAI. In response to the need and the evolving
nature of GenAI, this paper seeks to provide a collective
view of different governance approaches around the world.
Our research introduces a Harmonized GenAI Framework,
“H-GenAIGF”, based on the current governance approaches
of six regions: (European Union (EU), United States (US),
China (CN), Canada (CA), United Kingdom (UK), and Singapore
(SG)). We have identified four constituents, fifteen
processes, twenty-five sub-processes, and nine principles that
aid the governance of GenAI, thus providing a comprehensive
perspective on the current state of GenAI governance. In
addition, we present a comparative analysis to facilitate identification
of common ground and distinctions based on coverage
of the processes by each region. The results show that
risk-based approaches allow for better coverage of the processes,
followed by mixed approaches. Other approaches lag
behind, covering less than 50% of the processes. Most prominently,
the analysis demonstrates that amongst the regions,
only one process aligns across all approaches, highlighting
the lack of consistent and executable provisions. Moreover,
our case study on ChatGPT reveals process coverage deficiency,
showing that harmonization of approaches is necessary
to find alignment for GenAI governance.
LLMs and Memorization: On Quality and Specificity of Copyright Compliance
Authors: Mueller Felix B , Görge Rebekka , Bernzen Anna K , Pirk Janna C , Poretschkin Maximilian
Published: November 2024

Memorization in large language models (LLMs) is a growing concern. LLMs have been shown to easily reproduce parts of their training data, including copyrighted work. This is an important problem to solve, as it may violate existing copyright laws as well as the European AI Act. In this work, we propose a systematic analysis to quantify the extent of potential copyright infringements in LLMs using European law as an example. Unlike previous work, we evaluate instruction-finetuned models in a realistic end-user scenario. Our analysis builds on a proposed threshold of 160 characters, which we borrow from the German Copyright Service Provider Act and a fuzzy text matching algorithm to identify potentially copyright-infringing textual reproductions. The specificity of countermeasures against copyright infringement is analyzed by comparing model behavior on copyrighted and public domain data. We investigate what behaviors models show instead of producing protected text (such as refusal or hallucination) and provide a first legal assessment of these behaviors. We find that there are huge differences in copyright compliance, specificity, and appropriate refusal among popular LLMs. Alpaca, GPT 4, GPT 3.5, and Luminous perform best in our comparison, with OpenGPT-X, Alpaca, and Luminous producing a particularly low absolute number of potential copyright violations. Code can be found at github.com/felixbmuller/llms-memorization-copyright.
Are Large Language Models Moral Hypocrites? A Study Based on Moral Foundations
Authors: Nunes José Luiz , Almeida Guilherme F. C. F. , Araujo Marcelo de , Barbosa Simone D. J.
Published: November 2024

Large language models (LLMs) have taken centre stage in debates on Artificial Intelligence. Yet there remains a gap in how to assess LLMs' conformity to important human values. In this paper, we investigate whether state-of-the-art LLMs, GPT-4 and Claude 2.1 (Gemini Pro and LLAMA 2 did not generate valid results) are moral hypocrites. We employ two research instruments based on the Moral Foundations Theory: (i) the Moral Foundations Questionnaire (MFQ), which investigates which values are considered morally relevant in abstract moral judgements; and (ii) the Moral Foundations Vignettes (MFVs), which evaluate moral cognition in concrete scenarios related to each moral foundation. We characterise conflicts in values between these different abstractions of moral evaluation as hypocrisy. We found that both models displayed reasonable consistency within each instrument compared to humans, but they displayed contradictory and hypocritical behaviour when we compared the abstract values present in the MFQ to the evaluation of concrete moral violations of the MFV.
CIVICS: Building a Dataset for Examining Culturally-Informed Values in Large Language Models
Authors: Pistilli Giada , Leidinger Alina , Jernite Yacine , Kasirzadeh Atoosa , Luccioni Alexandra Sasha , Mitchell Margaret
Published: November 2024

This paper introduces the "CIVICS: Culturally-Informed \& Values-Inclusive Corpus for Societal impacts" dataset, designed to evaluate the social and cultural variation of Large Language Models (LLMs) towards socially sensitive topics across multiple languages and cultures. The hand-crafted, multilingual dataset of statements addresses value-laden topics, including LGBTQI rights, social welfare, immigration, disability rights, and surrogacy. CIVICS is designed to elicit responses from LLMs to shed light on how values encoded in their parameters shape their behaviors. Through our dynamic annotation processes, tailored prompt design, and experiments, we investigate how open-weight LLMs respond to these issues, exploring their behavior across diverse linguistic and cultural contexts.
Using two experimental set-ups based on log-probabilities and long-form responses, we show social and cultural variability across different LLMs. Specifically, different topics and sources lead to more pronounced differences across model answers, particularly on immigration, LGBTQI rights, and social welfare. Experiments on generating long-form responses from models tuned for user chat demonstrate that refusals are triggered disparately across different models, but consistently and more frequently in English or translated statements. As shown by our initial experimentation, the CIVICS dataset can serve as a tool for future research, promoting reproducibility and transparency across broader linguistic settings, and furthering the development of AI technologies that respect and reflect global cultural diversities and value pluralism.
The CIVICS dataset and tools are made available under open licenses at hf.co/CIVICS-dataset.
Fairness in AI-Based Mental Health: Clinician Perspectives and Bias Mitigation
Authors: Sogancioglu Gizem , Mosteiro Pablo , Salah Albert Ali , Scheepers Floortje , Kaya Heysem
Published: November 2024

There is limited research on fairness in automated decision-making systems in the clinical domain, particularly in the mental health domain. Our study explores clinicians' perceptions of AI fairness through two distinct scenarios: violence risk assessment and depression phenotype recognition using textual clinical notes. We engage with clinicians through semi-structured interviews to understand their fairness perceptions and to identify appropriate quantitative fairness objectives for these scenarios. Then, we compare a set of bias mitigation strategies developed to improve at least one of the four selected fairness objectives. Our findings underscore the importance of carefully selecting fairness measures, as prioritizing less relevant measures can have a detrimental rather than a beneficial effect on model behavior in real-world clinical use.
Enhancing Equitable Access to AI in Housing and Homelessness System of Care through Federated Learning
Authors: Taib Musa , Wu Jiajun , Drew Steve , Messier Geoffrey G.
Published: November 2024

The top priority of a Housing and Homelessness System of Care (HHSC) is to connect people experiencing homelessness to supportive housing. An HHSC typically consists of many agencies serving the same population. Information technology platforms differ in type and quality between agencies, so their data are usually isolated from one agency to another. Larger agencies may have sufficient data to train and test artificial intelligence (AI) tools but smaller agencies typically do not. To address this gap, we introduce a Federated Learning (FL) approach enabling all agencies to train a predictive model collaboratively without sharing their sensitive data. We demonstrate how FL can be used within an HHSC to provide all agencies equitable access to quality AI and further assist human decision-makers in the allocation of resources within HHSC. This is achieved while preserving the privacy of the people within the data by not sharing identifying information between agencies without their consent. Our experimental results using real-world HHSC data from a North American city demonstrate that our FL approach offers comparable performance with the idealized scenario of training the predictive model with data fully shared and linked between agencies.
Medical AI, Categories of Value Conflict, and Conflict Bypasses
Authors: Victor Gavin , Bélisle-Pipon Jean-Christophe
Published: November 2024

It is becoming clear that, in the process of aligning AI with human values, one glaring ethical problem is that of value conflict. It is not obvious what we should do when two compelling values (such as autonomy and safety) come into conflict with one another in the design or implementation of a medical AI technology. This paper shares findings from a scoping review at the intersection of three concepts—AI, moral value, and health—that have to do with value conflict and arbitration. The paper looks at some important and unique cases of value conflict, and then describes three possible categories of value conflict: personal value conflict, interpersonal or intercommunal value conflict, and definitional value conflict. It then describes three general paths forward in addressing value conflict: additional ethical theory, additional empirical evidence, and bypassing the conflict altogether. Finally, it reflects on the efficacy of these three paths forward as ways of addressing the three categories of value conflict, and motions toward what is needed for better approaching value conflicts in medical AI.
PICE: Polyhedral Complex Informed Counterfactual Explanations
Authors: Villani Mattia Jacopo , Albini Emanuele , Sharma Shubham , Mishra Saumitra , Ibrahim Amoukou Salim , Magazzeni Daniele , Veloso Manuela
Published: November 2024

Polyhedral geometry can be used to shed light on the behaviour of piecewise linear neural networks, such as ReLU-based architectures.
Counterfactual explanations are a popular class of methods for examining model behaviour by comparing a query to the closest point with a different label, subject to constraints.
We present a new algorithm, Polyhedral-complex Informed Counterfactual Explanations (PICE), which leverages the decomposition of the piecewise linear neural network into a polyhedral complex to find counterfactuals that are provably minimal in the Euclidean norm and exactly on the decision boundary for any given query.
Moreover, we develop variants of the algorithm that target popular counterfactual desiderata such as sparsity, robustness, speed, plausibility, and actionability.
We empirically show on four publicly available real-world datasets that our method outperforms other popular techniques to find counterfactuals and adversarial attacks by distance to decision boundary and distance to query.
Moreover, we successfully improve our baseline method in the dimensions of the desiderata we target, as supported by experimental evaluations.
How Do AI Companies “Fine-Tune” Policy? Examining Regulatory Capture in AI Governance
Authors: Wei Kevin , Ezell Carson , Gabrieli Nick , Deshpande Chinmay
Published: November 2024

Industry actors in the United States have gained extensive influence in conversations about the regulation of general-purpose artificial intelligence (AI) systems. Although industry participation is an important part of the policy process, it can also cause regulatory capture, whereby industry co-opts regulatory regimes to prioritize private over public welfare. Capture of AI policy by AI developers and deployers could hinder such regulatory goals as ensuring the safety, fairness, beneficence, transparency, or innovation of general-purpose AI systems. In this paper, we first introduce different models of regulatory capture from the social science literature. We then present results from interviews with 17 AI policy experts on what policy outcomes could compose regulatory capture in US AI policy, which AI industry actors are influencing the policy process, and whether and how AI industry actors attempt to achieve outcomes of regulatory capture. Experts were primarily concerned with capture leading to a lack of AI regulation, weak regulation, or regulation that over-emphasizes certain policy goals over others. Experts most commonly identified agenda-setting (15 of 17 interviews), advocacy (13), academic capture (10), information management (9), cultural capture through status (7), and media capture (7) as channels for industry influence. To mitigate these particular forms of industry influence, we recommend systemic changes in developing technical expertise in government and civil society, independent funding streams for the AI ecosystem, increased transparency and ethics requirements, greater civil society access to policy, and various procedural safeguards.
Automate or Assist? The Role of Computational Models in Identifying Gendered Discourse in US Capital Trial Transcripts
Authors: Wen-Yi Andrea W , Adamson Kathryn , Greenfield Nathalie , Goldberg Rachel , Babcock Sandra , Mimno David , Koenecke Allison
Published: November 2024

The language used by US courtroom actors in criminal trials has long been studied for biases. However, systematic studies for bias in high-stakes court trials have been difficult, due to the nuanced nature of bias and the legal expertise required. Large language models offer the possibility to automate annotation. But validating the computational approach requires both an understanding of how automated methods fit in existing annotation workflows and what they really offer. We present a case study of adding a computational model to a complex and high-stakes problem: identifying gender-biased language in US capital trials for women defendants.
Our team of experienced death-penalty lawyers and NLP technologists pursue a three-phase study: first annotating manually, then training and evaluating computational models, and finally comparing expert annotations to model predictions. Unlike many typical NLP tasks, annotating for gender bias in months-long capital trials is complicated, with many individual judgment calls. Contrary to standard arguments for automation that are based on efficiency and scalability, legal experts find the computational models most useful in providing opportunities to reflect on their own bias in annotation and to build consensus on annotation rules. This experience suggests that seeking to replace experts with computational models for complex annotation is both unrealistic and undesirable. Rather, computational models offer valuable opportunities to assist the legal experts in annotation-based studies.
When and Why is Persuasion Hard? A Computational Complexity Result
Authors: Wojtowicz Zachary
Published: November 2024

As generative foundation models improve, they also tend to become more persuasive, raising concerns that AI automation will enable governments, firms, and other actors to manipulate beliefs with unprecedented scale and effectiveness at virtually no cost. The full economic and social ramifications of this trend have been difficult to foresee, however, given that we currently lack a complete theoretical understanding of why persuasion is costly for human labor to produce in the first place. This paper places human and AI agents on a common conceptual footing by formalizing informational persuasion as a mathematical decision problem and characterizing its computational complexity. A novel proof establishes that persuasive messages are challenging to discover (NP-Hard) but easy to adopt if supplied by others (NP). This asymmetry helps explain why people are susceptible to persuasion, even in contexts where all relevant information is publicly available. The result also illuminates why litigation, strategic communication, and other persuasion-oriented activities have historically been so human capital intensive, and it provides a new theoretical basis for studying how AI will impact various industries.
Representation Bias of Adolescents in AI: A Bilingual, Bicultural Study
Authors: Wolfe Robert , Dangol Aayushi , Howe Bill , Hiniker Alexis
Published: November 2024

Popular and news media often portray teenagers with sensationalism, as both a risk to society and at risk from society. As AI begins to absorb some of the epistemic functions of traditional media, we study how teenagers in two countries speaking two languages: 1) are depicted by AI, and 2) how they would prefer to be depicted. Specifically, we study the biases about teenagers learned by static word embeddings (SWEs) and generative language models (GLMs), comparing these with the perspectives of adolescents living in the U.S. and Nepal. We find English-language SWEs associate teenagers with societal problems, and more than 50% of the 1,000 words most associated with teenagers in the pretrained GloVe SWE reflect such problems. Given prompts about teenagers, 30% of outputs from GPT2-XL and 29% from LLaMA-2-7B GLMs discuss societal problems, most commonly violence, but also drug use, mental illness, and sexual taboo. Nepali models, while not free of such associations, are less dominated by social problems. Data from workshops with N=13 U.S. adolescents and N=18 Nepalese adolescents show that AI presentations are disconnected from teenage life, which revolves around activities like school and friendship. Participant ratings of how well 20 trait words describe teens are decorrelated from SWE associations, with Pearson's rho=.02, n.s. in English FastText and rho=.06, n.s. GloVe; and rho=.06, n.s. in Nepali FastText and rho=-.23, n.s. in GloVe. U.S. participants suggested AI could fairly present teens by highlighting diversity, while Nepalese participants centered positivity. Participants were optimistic that, if it learned from adolescents, rather than media sources, AI could help mitigate stereotypes. Our work offers an understanding of the ways SWEs and GLMs misrepresent a developmentally vulnerable group and provides a template for less sensationalized characterization.
Non-linear Welfare-Aware Strategic Learning
Authors: Xie Tian , Zhang Xueru
Published: November 2024

This paper studies algorithmic decision-making in the presence of strategic individual behaviors, where an ML model is used to make decisions about human agents and the latter can adapt their behavior strategically to improve their future data. Existing results on strategic learning have largely focused on the linear setting where agents with linear labeling functions best respond to a (noisy) linear decision policy. Instead, this work focuses on general non-linear settings where agents respond to the decision policy with only "local information" of the policy. Moreover, we simultaneously consider objectives of maximizing decision-maker welfare (model prediction accuracy), social welfare (agent improvement caused by strategic behaviors), and agent welfare (the extent that ML underestimates the agents). We first generalize the agent best response model in previous works to the non-linear setting and then investigate the compatibility of welfare objectives. We show the three welfare can attain the optimum simultaneously only under restrictive conditions which are challenging to achieve in non-linear settings. The theoretical results imply that existing works solely maximizing the welfare of a subset of parties usually diminish the welfare of others. We thus claim the necessity of balancing the welfare of each party in non-linear settings and propose an irreducible optimization algorithm suitable for general strategic learning. Experiments on synthetic and real data validate the proposed algorithm.
Algorithmic Decision-Making under Agents with Persistent Improvement
Authors: Xie Tian , Tan Xuwei , Zhang Xueru
Published: November 2024

This paper studies algorithmic decision-making under human strategic behavior, where a decision-maker uses an algorithm to make decisions about human agents, and the latter with information about the algorithm may exert effort strategically and improve to receive favorable decisions. Unlike prior works that assume agents benefit from their efforts immediately, we consider realistic scenarios where the impacts of these efforts are persistent and agents benefit from efforts by making improvements gradually. We first develop a dynamic model to characterize persistent improvements and based on this construct a Stackelberg game to model the interplay between agents and the decision-maker. We analytically characterize the equilibrium strategies and identify conditions under which agents have incentives to invest efforts to improve their qualifications. With the dynamics, we then study how the decision-maker can design an optimal policy to incentivize the largest improvements inside the agent population. We also extend the model to settings where 1) agents may be dishonest and game the algorithm into making favorable but erroneous decisions; 2) honest efforts are forgettable and not sufficient to guarantee persistent improvements. With the extended models, we further examine conditions under which agents prefer honest efforts over dishonest behavior and the impacts of forgettable efforts.
FLUID FLOW AND THERMAL ANALYSIS OF BLOOD FLOW IN AN AUTOMATICALLY GENERATED 2D VASCULAR NETWORK FEATURING THE POROUS MEDIA-BASED OUTFLOW BOUNDARY CONDITIONS
Authors: Adabbo Gabriele , Andreozzi Assunta , Iasiello Marcello , Napoli Giovanni , Vanoli Giuseppe Peter
Published: November 2024

Blood flow and thermal analyses in biological tissues are utterly important to better understand the transport
phenomena in human tissues with reference to cardiovascular diseases, drug delivery, and thermal ablation. In the
existing literature, there is room for new computationally lighter numerical analyses, including both fluid flow and
heat transfer. This paper presents an analysis of blood thermo-fluid dynamics within an automatically generated two-dimensional (2D) vascular network, employing the constrained constructive optimization algorithm for
structure generation, the porous media assumption for outflow boundary conditions, and heat transfer coefficient
analysis for terminal vessels. Through comparisons with theoretical results, the model demonstrates mathematical
robustness. Results of the simulations show that blood velocity decreases with increasing number of bifurcations,
offering quantitative insights into its decay in magnitude and on its impact on heat transfer. Blood temperature rises
in vessels with low velocity, hindering its cooling effects in the surrounding tissues. The study highlights the influence of bifurcation levels on heat transfer coefficient reduction, suggesting longer pathways and time periods to reach high temperature within the blood vessels, due to the cooling effect of pulsating blood flow in larger vessels. The quantitative analysis of the heat transfer coefficient and Nusselt number provides insights into heat transfer between blood and the surrounding tissue, offering also valuable information for numerical bioheat models in thermal therapy simulations.
General asymptotic representations of indexes based on the functional empirical process and the residual functional empirical process and applications
Authors: Lo Gane Samb , Kpanzou Tchilabalo Abozou , Da Gandasor Bonyiri Onesiphore
Published: November 2024

The objective of this paper is to establish a general asymptotic representation (GAR) for a wide range of statistics, employing two fundamental processes: the functional empirical process (fep) and the residual functional empirical process introduced by Lo and Sall (2010a, 2010b), denoted as (lrfep). The functional empirical process (fep) is defined as follows: $$ \mathbb{G}_n(h)=\frac{1}{\sqrt{n}} \sum_{j=1}^{n} \{h(X_j)-\mathbb{E}h(X_j)\}, $$ where \(X\), \(X_1\), \(\cdots\), \(X_n\) is a sample from a random \(d\)-vectors \(X\) of size \((n+1)\) with \(n\geq 1\) and \(h\) is a measurable function defined on \(\mathbb{R}^d\) such that \(\mathbb{E}h(X)^2\) is finite. It is a powerful tool for deriving asymptotic laws. An earlier and simpler version of this paper focused on the application of the (fep) to statistics \(J_n\) that can be turned into an asymptotic algebraic expression of empirical functions of the form: $$ J_n=\mathbb{E}h(X) + n^{-1/2} \mathbb{G}_n(h) + o_{\mathbb{P}}(n^{-1/2}). \ \ \ \textit{SGAR} $$ However, not all statistics, in particular welfare indexes, conform to this form. In many scenarios, functions of the order statistics \(X_{1,n}\leq, \cdots, \leq X_{n,n}\) are involved, resulting in \(L\)-statistics. In such cases, the (fep) can still be utilized, but in combination with the related residual functional empirical process introduced by Lo and Sall (2010a, 2010b). This combination leads to general asymptotic representations (GAR) for a wide range of statistical indexes $$ J_n=\mathbb{E}h(X) + n^{-1/2} \biggr(\mathbb{G}_n(h) + \int_{0}^{1} \mathbb{G}_n(\tilde{f}_s) \ell(s) \ ds + o_{\mathbb{P}}(1)\biggr), \ \ \textit{FGAR} $$ where \(\tilde{f}_s=1_{]-\infty, F^{-1}(s)]}\) is the indicator function of the interval \(]-\infty, F^{-1}(s)]\) of \(\mathbb{R}\), \(h \in L^2(\mathbb{P}_X)\) and \(\ell(\circ)\) is a measurable function of \(s \in (0,1)\). Such representations, when associated with copulas, provide a robust framework for comparing indices over the time or across different areas. The comprehensive theory is presented alongside explicit examples, facilitating the utilization by researchers.
Country-Specific Effects of Euro-Area Monetary Policy: The Role of Sectoral Differences
Authors: Datsenko Ruslana , Fleck Johannes
Published: November 2024

Economic growth in some euro area countries has been lackluster since the COVID-19 pandemic. Concurrently, the ECB hiked its policy rate to fight inflation. In this note, we show that high interest rates have depressed economic activity more in those euro-area countries with large manufacturing sectors.
A Mediterranean gatecrasher: Neoaliturus inscriptus (Haupt, 1927) new to the Carpathian Basin (Hemiptera: Auchenorrhyncha: Cicadellidae)
Authors: Schlitt Bence Péter , Horváth Áron , Orosz András
Published: November 2024

Neoaliturus inscriptus (Haupt, 1927) (Hemiptera: Auchenorrhyncha: Cicadellidae: Deltocephalinae: Opsiini: Circuliferina) is reported from Hungary and the Carpathian Basin for the first time.
La calidad de los procesos interactivos en entornos virtuales: aprendizaje de los estudiantes en el enfoque de una Educación Flexible de calidad
Authors:   Investigador y Consultor Independiente, U. Botelho Francisco V. , Farfán Pablo ,   Universidad Politécnica Salesiana, Ecuador
Published: November 2024

Los procesos de aprendizaje ocupan un lugar central en la acción educativa, y el aprendizaje está relacionado con la forma en que las personas interactúan entre sí y con el entorno. Mucho más allá de los procesos sensoriales elementales y los reflejos, el aprendizaje forma parte de procesos mentales superiores que son mediados por herramientas y signos.