bluuebunny/crossref_metadata_2025 huggingface dataset:
Un acercamiento al patrimonio cultural inmaterial su salvaguarda y patrimonialización
Authors: Rebollo Cruz Montserrat Patricia
Published: November 2024

The Intangible Cultural Heritage (ICH) is one of the main topics in the international political agenda. This is because it intervenes in several governmental levels or aspects such as social, cultural, economical and political, creating multiple impacts within the policy implementation topics. Therefore, several questions arise. Firstly, what exactly is the ICH and what does it mean when talking about safeguarding it? Secondly, what is a heritage process? Lastly, how has the list of representation of Intangible Cultural Heritage of humanity changed up to date? The present article seeks to approach to answer these questions through the experience adopted of the international policy of safeguard of PCI adopted in Mexico.
МЕХАНОХИМИЯ ГИДРОКСИАПАТИТА И ЕГО ЗАМЕЩЕННЫХ ФОРМ
Authors: Н.В. БУЛИНА , М.В. ЧАЙКИНА
Published: November 2024

Данная обзорная работа посвящена достижениям в области механохимии гидроксиапатита. Рассматриваются особенности механохимического способа получения данного минерала, реакции, происходящие в реакционной камере, параметры, влияющие на эффективность процесса, перечисляются достоинства метода. Показано, что в высокоэнергетических планетарных мельницах, с большим числом оборотов барабана, можно осуществлять быстрый механохимический синтез не только стехиометрического гидроксиапатита, но и разнообразных вариантов замещений в его структуре в широком интервале концентраций.

The present review is concerned with advances in the area of hydroxyapatite mechanochemistry. The features of the mechanochemical method for obtaining this mineral, the reactions proceeding in the reaction chamber, the parameters affecting the process efficiency are considered, and the advantages of the method are listed. It has been shown that rapid mechanochemical synthesis of not only stoichiometric hydroxyapatite, but also various options of substitution in its structure within a wide range of concentrations can be carried out in a high-energy planetary mill at a high frequency of vial rotation.
“A Word About the Great Artist” by Fyodor Dostoevsky: the Beginning of the Literary Path of Lawyer Anatoly Koni
Authors: Andrianova Irina , Buteneva Daria
Published: November 2024

Anatoly Koni was not only a judicial, state, public and scientific figure, a teacher, but also a writer who left his memoirs of figures of Russian culture and deserved the recognition of his contemporaries. They nominated him for the Nobel Prize in Literature, elected him a member of the jury of the Pushkin Prize in Literature, an honorary academician of the Imperial Academy of Sciences in the category of fine literature, an honorary member of the Pushkin Lyceum Society and a freelance employee of the Pushkin House. The article pays special attention to the beginning of the literary activity of lawyer Anatoly Koni. The reason this talented judicial speaker tried his hand at writing was the sudden death of Fyodor Dostoevsky. These two outstanding contemporaries were linked by a friendship that lasted from 1873 until the writer’s death. They met at court sessions at the trials of Vera Zasulich and Ekaterina Kornilova, made a joint trip to the colony of juvenile lawbreakers, and in the late 1870s lived nearby and had the opportunity to visit each other. The lawyer’s literary career began in 1881, with a speech about Dostoevsky as a criminologist, which later became a repeatedly reprinted essay. The article examines the autograph of the speech, preserved thanks to the writer’s widow Anna Dostoevskaya, and reveals the main features of Kony’s artistic skill, reflected in his subsequent creative works: the influence of his legal profession on literary work, his preference for an impassioned style and didactic pathos, the use of figurative means of expression, and biblical allusions. Koni constantly reworked, supplemented and repeatedly republished his memoirs about Dostoevsky, but they were generally structured into three original essays included in his collected works: “Fyodor Mikhailovich Dostoevsky,” “F. M. Dostoevsky,” “More About Dostoevsky.” These texts analyze the work of the author of “Crime and Punishment” from a legal professional’s point of view, and also note the characteristic features of the writer and the person — “a model and a great teacher.” It was in the essay on Dostoevsky that Kony’s talent of an inspired writer was discovered, but Anatoly Fedorovich himself also played an important role in Dostoevsky’s life and work, honestly revealing to the writer the contradictory features of the Russian justice system and being an example of an excellent lawyer. The study uses a wide range of archival and memoir sources: the autograph of the speech of Anatoly Koni about Dostoevsky, his memoirs, the lawyer’s correspondence with members of the Dostoevsky family, notebooks and memoirs of Anna Dostoevskaya.
AS RELAÇÕES INTERPESSOAIS NO ENVELHECIMENTO SAUDÁVEL: COMBATENDO A SOLIDÃO E O ESTRESSE
Authors: Ludwig da Costa Frederico , Luiz Bonamigo Erik , de Souza Pinto Danilo
Published: November 2024

O envelhecimento populacional é consequência inerente ao desenvolvimento socioeconômico e científico da sociedade moderna. Nas últimas décadas, observa-se o fenômeno de inversão da pirâmide etária em países em desenvolvimento e, com isso, o aumento da expectativa de vida (“World Population Prospects 2022: Summary of Results”, 2022).
Análisis comparativo de la normativa contra el acoso sexual vigente en universidades públicas de Centroamérica
Authors: García Zamora Éricka
Published: November 2024

El objetivo de este artículo es comparar la normativa sobre acoso sexual de las principales universidades públicas de Guatemala, Honduras, El Salvador, Nicaragua, Costa Rica y Panamá, con el fin de establecer su alcance en tres líneas centrales: a) existencia de una regulación específica sobre este tipo de violencia, b) conceptualización del acoso sexual y, c) valoración de los mecanismos establecidos en relación con el enfoque de derecho que predomina en lo que atañe a derechos de las víctimas, la prescripción de este delito y, por último, las  condiciones para la elevación y ratificación de las denuncias presentadas. El  abordaje metodológico es cualitativo y la investigación es documental con base  en fuentes primarias relacionadas con las normas y protocolos promulgados en las instituciones de educación superior y con fuentes secundarias. La investigación se circunscribe al periodo 1996-2020. Se concluye que la normativa existente responde a un modelo garantista de los procedimientos disciplinarios,  enfocado en lo procedimental con base en normas consideradas apolíticas, el cual no toma en cuenta la variable sexo ni la asimetría de poder recurrente en las  relaciones entre las partes involucradas.
Value-driven training of leaders in the service of Hungarian denominational (Reformed) public education
Authors: Szontagh Pál Iván , van der Vloed Kees
Published: November 2024

Our paper focuses on a specific subsystem of the Hungarian public education system, the Reformed educational network. After a theoretical foundation, the study will review the characteristics and legislative environment of Reformed public education management and leadership training, and then examine the concept, reception, results and further development potentials of the accredited training in Value-Driven Leadership, a relatively new element of the range of trainings. The training was developed in collaboration between Dutch and Hungarian specialists, taking into account the international context of Christian education. Our study uses a variety of research methods, such as document analysis, statistical analysis and analysis of a questionnaire survey on further needs. Some basic conclusions can be drawn from the survey as: (1) The target group is highly interested in modern knowledge about Christian leadership, which is considered a priority area, which also coincides with the needs of the maintenance. (2) The management powers of headmasters of ecclesiastical institutions are much broader than those of headmasters of public institutions, but their knowledge and interest in these areas are insufficient. (3) More than ever before, the organisation of training should aim at modularity and a variety of organisational methods. The tools of combined training organisation, which have already proved their worth in higher education, should also be applied to 30-h continuing training, possibly with the addition of distance learning elements.
Mássalhangzók pereskedése
Authors: Mészáros Tamás
Published: November 2024

Jelen dolgozat célja, hogy bemutassa a samosatai Lukianos Mássalhangzók pereskedése című művét. A cselekmény szerint a megszemélyesített Sigma betű vádat emel Tau ellen javainak erőszakos elrablása miatt a magánhangzók bírósága előtt. A nyelvi jelenségek bemutatásán alapuló humoros mű valójában támadás az atticista stílusirányzat szélsőséges képviselői ellen.
Filamentous cyanobacteria growth assessment using fluorinated ethylene propylene microcapillaries
Authors: Silva David M. S. , Amaral Raquel , Reis Nuno M. , Rocha Paulo R. F.
Published: November 2024

Filamentous cyanobacteria originate toxic harmful algal blooms (HABs) in aquatic ecosystems, severely impacting freshwater ecosystems and life. Despite being natural bloomers, these microorganisms are challenging to handle in vitro, due to the formation of aggregates with entangled filaments. Consequently, their precise growth dynamics, although vital to timely predict HABs, remains inaccessible. Here, we precisely assessed growth of the HAB forming cyanobacteria Oscillatoria nigroviridis, by cultivating filament suspensions in transparent, gas permeable, and portable fluoropolymer microcapillary strips. Direct optical observation of O. nigroviridis growth revealed shorter filaments comprising less than 50 cells grew at a slower rate, dN/dt = 0.09 cell/day compared to filaments comprising more than 50 cells, with dN/dt up to 0.47 cell/day. The fourfold increase in dN/dt is suggested as part of the blooming strategy of the microorganism. This work suggests that fluoropolymer microcapillary strips can be used for effortless sampling and high-resolution monitoring of HABs.

Impact statement

Climate change is increasing the occurrence of episodes of harmful algal bloom, where uncontrolled growth of noxious cyanobacteria such as Oscillatoria species has detrimental outcomes in both the environment and biomass production industry, consequently, impairing human and animal health due to the production of toxic or bioactive compounds. In particular, the study of growth dynamics of Oscillatoria species has been limited to unprecise methods due to complications with aliquoting filamentous biomass. Fluoropolymer microcapillary strips provide an ideal miniaturized platform for sampling, cultivation, and growth monitoring of O. nigroviridis strain UHCC 0327, which paves the way to foster better water quality management tools.

Graphical abstract
ESTUDO DE CASO: INTRODUZINDO A LINGUAGEM COMPUTACIONAL NO ENSINO BÁSICO
Authors: Santini Rachel
Published: November 2024

Este estudo de caso explora o ensino do pensamento computacional e do conceito de algoritmos para alunos do segundo ano do ensino fundamental, com atividades práticas e lúdicas baseadas em situações cotidianas. O objetivo foi introduzir as crianças ao raciocínio lógico e à estrutura sequencial necessária para o funcionamento de tecnologias digitais, como a inteligência artificial (IA). Duas atividades principais foram realizadas: uma de "Escovar os Dentes" e outra de "Chegar à Escola", nas quais os alunos organizaram passos de rotina em uma ordem lógica. Os resultados mostraram que os alunos entenderam bem a importância da sequência e do planejamento, discutindo como alterações nos passos afetam o resultado final. A conclusão destaca que, ao apresentar algoritmos de forma lúdica, a escola ajuda a formar uma base para o aprendizado de habilidades tecnológicas essenciais na sociedade atual.
Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulation
Authors: Barnett Julia , Kieslich Kimon , Diakopoulos Nicholas
Published: November 2024

The rapid advancement of AI technologies yields numerous future impacts on individuals and society. Policymakers are tasked to react quickly and establish policies that mitigate those impacts. However, anticipating the effectiveness of policies is a difficult task, as some impacts might only be observable in the future and respective policies might not be applicable to the future development of AI. In this work we develop a method for using large language models (LLMs) to evaluate the efficacy of a given piece of policy at mitigating specified negative impacts. We do so by using GPT-4 to generate scenarios both pre- and post-introduction of policy and translating these vivid stories into metrics based on human perceptions of impacts. We leverage an already established taxonomy of impacts of generative AI in the media environment to generate a set of scenario pairs both mitigated and non-mitigated by the transparency policy in Article 50 of the EU AI Act. We then run a user study (n=234) to evaluate these scenarios across four risk-assessment dimensions: severity, plausibility, magnitude, and specificity to vulnerable populations. We find that this transparency legislation is perceived to be effective at mitigating harms in areas such as labor and well-being, but largely ineffective in areas such as social cohesion and security. Through this case study we demonstrate the efficacy of our method as a tool to iterate on the effectiveness of policy for mitigating various negative impacts. We expect this method to be useful to researchers or other stakeholders who want to brainstorm the potential utility of different pieces of policy or other mitigation strategies.
An FDA for AI? Pitfalls and Plausibility of Approval Regulation for Frontier Artificial Intelligence
Authors: Carpenter Daniel , Ezell Carson
Published: November 2024

Observers and practitioners of artificial intelligence (AI) have proposed an FDA-style licensing regime for the most advanced AI models, or 'frontier' models. In this paper, we explore the applicability of approval regulation -- that is, regulation of a product that combines experimental minima with government licensure conditioned partially or fully upon that experimentation -- to the regulation of frontier AI. There are a number of reasons to believe that approval regulation, simplistically applied, would be inapposite for frontier AI risks. Domains of weak fit include the difficulty of defining the regulated product, the presence of Knightian uncertainty or deep ambiguity about harms from AI, the potentially transmissible nature of risks, and distributed activities among actors involved in the AI lifecycle. We conclude by highlighting the role of policy learning and experimentation in regulatory development, describing how learning from other forms of AI regulation and improvements in evaluation and testing methods can help to overcome some of the challenges we identify.
Coordinated Flaw Disclosure for AI: Beyond Security Vulnerabilities
Authors: Cattell Sven , Ghosh Avijit , Kaffee Lucie-Aimée
Published: November 2024

Harm reporting in Artificial Intelligence (AI) currently lacks a structured process for disclosing and addressing algorithmic flaws, relying largely on an ad-hoc approach. This contrasts sharply with the well-established Coordinated Vulnerability Disclosure (CVD) ecosystem in software security. While global efforts to establish frameworks for AI transparency and collaboration are underway, the unique challenges presented by machine learning (ML) models demand a specialized approach. To address this gap, we propose implementing a Coordinated Flaw Disclosure (CFD) framework tailored to the complexities of ML and AI issues. This paper reviews the evolution of ML disclosure practices, from ad hoc reporting to emerging participatory auditing methods, and compares them with cybersecurity norms. Our framework introduces innovations such as extended model cards, dynamic scope expansion, an independent adjudication panel, and an automated verification process. We also outline a forthcoming real-world pilot of CFD. We argue that CFD could significantly enhance public trust in AI systems. By balancing organizational and community interests, CFD aims to improve AI accountability in a rapidly evolving technological landscape.
Sponsored is the New Organic: Implications of Sponsored Results on Quality of Search Results in the Amazon Marketplace
Authors: Dash Abhisek , Ghosh Saptarshi , Mukherjee Animesh , Chakraborty Abhijnan , Gummadi Krishna P.
Published: November 2024

Interleaving sponsored results (advertisements) amongst organic results on search engine result pages (SERP) has become a common practice across multiple digital platforms. Advertisements have catered to consumer satisfaction and fostered competition in digital public spaces; making them an appealing gateway for businesses to reach their consumers. However, especially in the context of digital marketplaces, due to the competitive nature of the sponsored results with the organic ones, multiple unwanted repercussions have surfaced affecting different stakeholders. From the consumers' perspective the sponsored ads/results may cause degradation of search quality and nudge consumers to potentially irrelevant and costlier products. The sponsored ads may also affect the level playing field of the competition in the marketplaces among sellers. To understand and unravel these potential concerns, we analyse the Amazon digital marketplace in four different countries by simulating 4,800 search operations. Our analyses over SERPs consisting 2M organic and 638K sponsored results show items with poor organic ranks (beyond 100th position) appear as sponsored results even before the top organic results on the first page of Amazon SERP. Moreover, we also observe that in majority of the cases, these top sponsored results are costlier and are of poorer quality than the top organic results. We believe these observations can motivate researchers for further deliberation to bring in more transparency and guard rails in the advertising practices followed in digital marketplaces.
Representation Magnitude Has a Liability to Privacy Vulnerability
Authors: Fang Xingli , Kim Jung-Eun
Published: November 2024

The privacy-preserving approaches to machine learning (ML) models have made substantial progress in recent years. However, it is still opaque in which circumstances and conditions the model becomes privacy-vulnerable, leading to a challenge for ML models to maintain both performance and privacy. In this paper, we first explore the disparity between member and non-member data in the representation of models under common training frameworks.We identify how the representation magnitude disparity correlates with privacy vulnerability and address how this correlation impacts privacy vulnerability. Based on the observations, we propose Saturn Ring Classifier Module (SRCM), a plug-in model-level solution to mitigate membership privacy leakage. Through a confined yet effective representation space, our approach ameliorates models’ privacy vulnerability while maintaining generalizability. The code of this work can be found here:
https://github.com/JEKimLab/AIES2024SRCM
Surviving in Diverse Biases: Unbiased Dataset Acquisition in Online Data Market for Fair Model Training
Authors: Gao Jiashi , Wang Ziwei , Zhao Xiangyu , Yao Xin , Wei Xuetao
Published: November 2024

The online data markets have emerged as a valuable source of diverse datasets for training machine learning (ML) models. However, datasets from different data providers may exhibit varying levels of bias with respect to certain sensitive attributes in the population (such as race, sex, age, and marital status). Recent dataset acquisition research has focused on maximizing accuracy improvements for downstream model training, ignoring the negative impact of biases in the acquired datasets, which can lead to an unfair model. Can a consumer obtain an unbiased dataset from datasets with diverse biases? In this work, we propose a fairness-aware data acquisition framework (FAIRDA) to acquire high-quality datasets that maximize both accuracy and fairness for consumer local classifier training while remaining within a limited budget. Given the biases of data commodities remain opaque to consumers, the data acquisition in FAIRDA employs explore-exploit strategies. Based on whether exploration and exploitation are conducted sequentially or alternately, we introduce two algorithms: the knowledge-based offline data acquisition (KDA) and the reward-based online data acquisition algorithms (RDA). Each algorithm is tailored to specific customer needs, giving the former an advantage in computational efficiency and the latter an advantage in robustness. We conduct experiments to demonstrate the effectiveness of the proposed data acquisition framework in steering users toward fairer model training compared to existing baselines under varying market settings.
LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI's ChatGPT Plugins
Authors: Iqbal Umar , Kohno Tadayoshi , Roesner Franziska
Published: November 2024

Large language model (LLM) platforms, such as ChatGPT, have recently begun offering an app ecosystem to interface with third-party services on the internet. While these apps extend the capabilities of LLM platforms, they are developed by arbitrary third parties and thus cannot be implicitly trusted. Apps also interface with LLM platforms and users using natural language, which can have imprecise interpretations. In this paper, we propose a framework that lays a foundation for LLM platform designers to analyze and improve the security, privacy, and safety of current and future third-party integrated LLM platforms. Our framework is a formulation of an attack taxonomy that is developed by iteratively exploring how LLM platform stakeholders could leverage their capabilities and responsibilities to mount attacks against each other. As part of our iterative process, we apply our framework in the context of OpenAI's plugin (apps) ecosystem. We uncover plugins that concretely demonstrate the potential for the types of issues that we outline in our attack taxonomy. We conclude by discussing novel challenges and by providing recommendations to improve the security, privacy, and safety of present and future LLM-based computing platforms. The full version of this paper is available online at https://arxiv.org/abs/2309.10254
As an AI Language Model, "Yes I Would Recommend Calling the Police": Norm Inconsistency in LLM Decision-Making
Authors: Jain Shomik , Calacci D. , Wilson Ashia
Published: November 2024

We investigate the phenomenon of norm inconsistency: where LLMs apply different norms in similar situations. Specifically, we focus on the high-risk application of deciding whether to call the police in Amazon Ring home surveillance videos. We evaluate the decisions of three state-of-the-art LLMs — GPT-4, Gemini 1.0, and Claude 3 Sonnet — in relation to the activities portrayed in the videos, the subjects' skin-tone and gender, and the characteristics of the neighborhoods where the videos were recorded. Our analysis reveals significant norm inconsistencies: (1) a discordance between the recommendation to call the police and the actual presence of criminal activity, and (2) biases influenced by the racial demographics of the neighborhoods. These results highlight the arbitrariness of model decisions in the surveillance context and the limitations of current bias detection and mitigation strategies in normative decision-making.
Reflection of Its Creators: Qualitative Analysis of General Public and Expert Perceptions of Artificial Intelligence
Authors: Jensen Theodore , Theofanos Mary , Greene Kristen , Williams Olivia , Goad Kurtis , Bih Fofang Janet
Published: November 2024

The increasing prevalence of artificial intelligence (AI) will likely lead to new interactions and impacts for the general public. An understanding of people’s perceptions of AI can be leveraged to design and deploy AI systems toward human needs and values. We conducted semi-structured interviews with 25 individuals in the general public and 20 AI experts in the United States (U.S.) to assess perceptions of AI across levels of expertise. Qualitative analysis revealed that ideas about humanness and ethics were central to perceptions of AI in both groups. Humanness, the set of traits considered to distinguish humans from other intelligent actors, was used to articulate beliefs about AI’s characteristics. Ethics arose in discussions of the role of technology in society and centered around views of AI as made and used by people. General public and expert participants expressed similar perceptions of AI, but articulated beliefs slightly differently. We discuss the implications of humanness-related beliefs and ethical concerns for AI development and deployment.
Virtual Assistants Are Unlikely to Reduce Patient Non-Disclosure
Authors: Jorgenson Corinne , Ozkes Ali I. , Willems Jurgen , Vanderelst Dieter
Published: November 2024

The ethical use of AI typically involves setting boundaries on its deployment. Ethical guidelines advise against practices that involve deception, privacy infringement, or discriminatory actions. However, ethical considerations can also identify areas where using AI is desirable and morally necessary. For instance, it has been argued that AI could contribute to more equitable justice systems. Another area where ethical considerations can make AI deployment imperative is healthcare. For example, patients often withhold pertinent details from healthcare providers due to fear of judgment. However, utilizing virtual assistants to gather patients' health histories could be a potential solution. Ethical imperatives support using such technology if patients are more inclined to disclose information to an AI system. This article presents findings from several survey studies investigating whether virtual assistants can reduce non-disclosure behaviors. Unfortunately, the evidence suggests that virtual assistants are unlikely to minimize non-disclosure. Therefore, the potential benefits of virtual assistants due to reduced non-disclosure are unlikely to outweigh their ethical risks.
Do Responsible AI Artifacts Advance Stakeholder Goals? Four Key Barriers Perceived by Legal and Civil Stakeholders
Authors: Kawakami Anna , Wilkinson Daricia , Chouldechova Alexandra
Published: November 2024

The responsible AI (RAI) community has introduced numerous processes and artifacts---such as Model Cards, Transparency Notes, and Data Cards---to facilitate transparency and support the governance of AI systems. While originally designed to scaffold and document AI development processes in technology companies, these artifacts are becoming central components of regulatory compliance under recent regulations such as the EU AI Act. Much of the existing literature has focussed primarily on the design of new RAI artifacts, or an examination of their use by practitioners within technology companies. However, as RAI artifacts begin to play key roles in enabling external oversight, it becomes critical to understand how stakeholders---particularly stakeholders situated outside of technology companies who govern and audit industry AI deployments---perceive the efficacy of RAI artifacts. In this study, we conduct semi-structured interviews and design activities with 19 government, legal, and civil society stakeholders who inform policy and advocacy around responsible AI efforts. While participants believe that RAI artifacts are a valuable contribution to the RAI ecosystem, many have concerns around their potential unintended and longer-term impacts on actors outside of technology companies (e.g., downstream end-users, policymakers, civil society stakeholders). We organized these beliefs into four barriers that help explain how RAI artifacts may (inadvertently) reconfigure power relations across civil society, government, and industry, impeding civil society and legal stakeholders' ability to protect downstream end-users from potential AI harms. Participants envision how structural changes, along with changes in how RAI artifacts are designed, used, and governed, could help re-direct the role and impacts of artifacts in the RAI ecosystem. Drawing on these findings, we discuss research and policy implications for RAI artifacts.
Algorithmic Fairness From the Perspective of Legal Anti-discrimination Principles
Authors: Keswani Vijay , Celis L. Elisa
Published: November 2024

Real-world applications of machine learning (ML) algorithms often propagate negative stereotypes and social biases against marginalized groups. In response, the field of fair machine learning has proposed technical solutions for a variety of settings that aim to correct the biases in algorithmic predictions. These solutions remove the dependence of the final prediction on the protected attributes (like gender or race) and/or ensure that prediction performance is similar across demographic groups. Yet, recent studies assessing the impact of these solutions in practice demonstrate their ineffectiveness in tackling real-world inequalities. Given this lack of real-world success, it is essential to take a step back and question the design motivations of algorithmic fairness interventions.

We use popular legal anti-discriminatory principles, specifically anti-classification and anti-subordination principles, to study the motivations of fairness interventions and their applications. The anti-classification principle suggests addressing discrimination by ensuring that decision processes and outcomes are independent of the protected attributes of individuals. The anti-subordination principle, on the other hand, argues that decision-making policies can provide equal protection to all only by actively tackling societal hierarchies that enable structural discrimination, even if that requires using protected attributes to address historical inequalities. Through a survey of the fairness mechanisms and applications, we assess different components of fair ML approaches from the perspective of these principles. We argue that the observed shortcomings of fair ML algorithms are similar to the failures of anti-classification policies and that these shortcomings constitute violations of the anti-subordination principle. Correspondingly, we propose guidelines for algorithmic fairness interventions to adhere to the anti-subordination principle. In doing so, we hope to bridge critical concepts between legal frameworks for non-discrimination and fairness in machine learning.
Anticipating the Risks and Benefits of Counterfactual World Simulation Models (Extended Abstract)
Authors: Kirfel Lara , MacCoun Rob , Icard Thomas , Gerstenberg Tobias
Published: November 2024

This paper examines the transformative potential of Counterfactual World Simulation Models (CWSMs). CWSMs use pieces of multi-modal evidence, such as the CCTV footage or sound recordings of a road accident, to build a high-fidelity 3D reconstruction of the scene. They can also answer causal questions, such as whether the accident happened because the driver was speeding, by simulating what would have happened in relevant counterfactual situations. CWSMs will enhance our capacity to envision alternate realities and investigate the outcomes of counterfactual alterations to how events unfold. This also, however, raises questions about what alternative scenarios we should be considering and what to do with that knowledge. We present a normative and ethical framework that guides and constrains the simulation of counterfactuals. We address the challenge of ensuring fidelity in reconstructions while simultaneously preventing stereotype perpetuation during counterfactual simulations. We anticipate different modes of how users will interact with CWSMs and discuss how their outputs may be presented. Finally, we address the prospective applications of CWSMs in the legal domain, recognizing both their potential to revolutionize legal proceedings as well as the ethical concerns they engender. Anticipating a new type of AI, this paper seeks to illuminate a path forward for responsible and effective use of CWSMs.
On Feasibility of Intent Obfuscating Attacks
Authors: Li Zhaobin , Shafto Patrick
Published: November 2024

Intent obfuscation is a common tactic in adversarial situations, enabling the attacker to both manipulate the target system and avoid culpability. Surprisingly, it has rarely been implemented in adversarial attacks on machine learning systems. We are the first to propose using intent obfuscation to generate adversarial examples for object detectors: by perturbing another non-overlapping object to disrupt the target object, the attacker hides their intended target. We conduct a randomized experiment on 5 prominent detectors---YOLOv3, SSD, RetinaNet, Faster R-CNN, and Cascade R-CNN---using both targeted and untargeted attacks and achieve success on all models and attacks. We analyze the success factors characterizing intent obfuscating attacks, including target object confidence and perturb object sizes. We then demonstrate that the attacker can exploit these success factors to increase success rates for all models and attacks. Finally, we discuss main takeaways and legal repercussions. If you are reading the AAAI/ACM version, please download the technical appendix on arXiv at https://arxiv.org/abs/2408.02674
Lessons from Clinical Communications for Explainable AI
Authors: Menon Alka V. , Abba Omar Zahra , Nahar Nadia , Papademetris Xenophon , Fiellin Lynn E. , Kästner Christian
Published: November 2024

One of the major challenges in the use of opaque, complex AI models is the need or desire to provide an explanation to the end-user (and other stakeholders) as to how the system arrived at the answer it did. While there is significant research in the development of explainability techniques for AI, the question remains as to who needs an explanation, what an explanation consists of, and how to communicate this to a lay user who lacks direct expertise in the area. In this position paper, an interdisciplinary team of researchers argue that the example of clinical communications offers lessons to those interested in improving the transparency and interpretability of AI systems. We identify five lessons from clinical communications: (1) offering explanations for AI systems and disclosure of their use recognizes the dignity of those using and impacted by it; (2) AI explanations can be productively targeted rather than totally comprehensive; (3) AI explanations can be enforced through codified rules but also norms, guided by core values; (4) what constitutes a “good” AI explanation will require repeated updating due to changes in technology and social expectations; 5) AI explanations will have impacts beyond defining any one AI system, shaping and being shaped by broader perceptions of AI. We review the history, debates and consequences surrounding the institutionalization of one type of clinical communication, informed consent, in order to illustrate the challenges and opportunities that may await attempts to offer explanations of opaque AI models. We highlight takeaways and implications for computer scientists and policymakers in the context of growing concerns and moves toward AI governance.
Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits
Authors: Mun Jimin , Jiang Liwei , Liang Jenny , Cheong Inyoung , DeCario Nicole , Choi Yejin , Kohno Tadayoshi , Sap Maarten
Published: November 2024

General purpose AI, such as ChatGPT, seems to have lowered the barriers for the public to use AI and harness its power. However, the governance and development of AI still remain in the hands of a few, and the pace of development is accelerating without a comprehensive assessment of risks. As a first step towards democratic risk assessment and design of general purpose AI, we introduce PARTICIP-AI, a carefully designed framework for laypeople to speculate and assess AI use cases and their impacts. Our framework allows us to study more nuanced and detailed public opinions on AI through collecting use cases, surfacing diverse harms through risk assessment under alternate scenarios (i.e., developing and not developing a use case), and illuminating tensions over AI devel- opment through making a concluding choice on its development. To showcase the promise of our framework towards informing democratic AI development, we run a medium-scale study with inputs from 295 demographically diverse participants. Our analyses show that participants’ responses emphasize applications for personal life and society, contrasting with most current AI development’s business focus. We also surface diverse set of envisioned harms such as distrust in AI and institutions, complementary to those defined by experts. Furthermore, we found that perceived impact of not developing use cases significantly predicted participants’ judgements of whether AI use cases should be developed, and highlighted lay users’ concerns of techno-solutionism. We conclude with a discussion on how frameworks like PARTICIP-AI can further guide democratic AI development and governance.
Disengagement through Algorithms: How Traditional Organizations Aim for Experts' Satisfaction
Authors: Poiroux Jérémie
Published: November 2024

This study examines the use of algorithmic tools in traditional organizational decision-making processes. Through forty semi-structured interviews with managers, engineers, and (expert) users across six European projects, we suggest that initiators deploy algorithms not to automate actions or replace users, but to disengage themselves from prescriptive decision-making. Consequently, the responsibility to choose, select, and decide falls upon the users; they become engaged. Therefore, algorithm evaluation is oriented towards utility, interpretability, and, more broadly, user satisfaction. Further research is encouraged to analyze the advent of a 'satisfaction regime', from platforms to traditional organizations.
Not Oracles of the Battlefield: Safety Considerations for AI-Based Military Decision Support Systems
Authors: Probasco Emelia , Burtell Matthew , Toner Helen , Rudner Tim G. J.
Published: November 2024

AI-based military decision support systems that help commanders observe, orient, decide, and act on the battlefield are highly sought after by military leadership. With the advent of large language models, AI developers have begun advertising automated AI-based decision support systems designed to both analyze and act on data from the battlefield. While the desire to use decision support systems to make better decisions on the battlefield is unsurprising, the responsible deployment of such systems requires a clear understanding of the capabilities and limitations of modern machine learning models. This paper reviews recently proposed uses of AI-enables decision support systems (DSS), provides a simplified framework for considering AI-DSS capabilities and limitations, and recommends practical risk mitigations commanders might employ when operating with an AI-enabled DSS.
A Human-in-the-Loop Fairness-Aware Model Selection Framework for Complex Fairness Objective Landscapes
Authors: Robertson Jake , Schmidt Thorsten , Hutter Frank , Awad Noor
Published: November 2024

Fairness-aware Machine Learning (FairML) applications are often characterized by complex social objectives and legal requirements, frequently involving multiple, potentially conflicting notions of fairness. Despite the well-known Impossibility Theorem of Fairness and extensive theoretical research on the statistical and socio-technical trade-offs between fairness metrics, many FairML tools still optimize or constrain for a single fairness objective. However, this one-sided optimization can inadvertently lead to violations of other relevant notions of fairness. In this socio-technical and empirical study, we frame fairness as a Many-Objective (MaO) problem by treating fairness metrics as conflicting objectives in a multi-objective (MO) sense. We introduce ManyFairHPO, a human-in-the-loop, fairness-aware model selection framework that enables practitioners to effectively navigate complex and nuanced fairness objective landscapes. ManyFairHPO aids in the identification, evaluation, and balancing of fairness metric conflicts and their related social consequences, leading to more informed and socially responsible model-selection decisions. Through a comprehensive empirical evaluation and a case study on the Law School Admissions problem, we demonstrate the effectiveness of ManyFairHPO in balancing multiple fairness objectives, mitigating risks such as self-fulfilling prophecies, and providing interpretable insights to guide stakeholders in making fairness-aware modeling decisions.
The Problems with Proxies: Making Data Work Visible through Requester Practices
Authors: Rothschild Annabel , Wang Ding , Jayakumar Vilvanathan Niveditha , Wilcox Lauren , DiSalvo Carl , DiSalvo Betsy
Published: November 2024

Fairness in AI and ML systems is increasingly linked to the proper treatment and recognition of data workers involved in training dataset development. Yet, those who collect and annotate the data, and thus have the most intimate knowledge of its development, are often excluded from critical discussions. This exclusion prevents data annotators, who are domain experts, from contributing effectively to dataset contextualization. Our investigation into the hiring and engagement practices of 52 data work requesters on platforms like Amazon Mechanical Turk reveals a gap: requesters frequently hold naive or unchallenged notions of worker identities and capabilities and rely on ad-hoc qualification tasks that fail to respect the workers’ expertise. These practices not only undermine the quality of data but also the ethical standards of AI development. To rectify these issues, we advocate for policy changes to enhance how data annotation tasks are designed and managed and to ensure data workers are treated with the respect they deserve.
Reducing Biases towards Minoritized Populations in Medical Curricular Content via Artificial Intelligence for Fairer Health Outcomes
Authors: Salavati Chiman , Song Shannon , Sosa Diaz Willmar , A. Hale Scott , E. Montenegro Roberto , Murai Fabricio , Dori-Hacohen Shiri
Published: November 2024

Biased information (recently termed bisinformation) continues to be taught in medical curricula, often long after having been debunked. In this paper, we introduce bricc, a first-in-class initiative that seeks to mitigate medical bisinformation using machine learning to systematically identify and flag text with potential biases, for subsequent review in an expert-in-the-loop fashion, thus greatly accelerating an otherwise labor-intensive process. We have developed a gold-standard bricc dataset throughout several years containing over 12K pages of instructional materials. Medical experts meticulously annotated these documents for bias according to comprehensive coding guidelines, emphasizing gender, sex, age, geography, ethnicity, and race. Using this labeled dataset, we trained, validated, and tested medical bias classifiers. We test three classifier approaches: a binary type-specific classifier, a general bias classifier; an ensemble combining bias type-specific classifiers independently-trained; and a multi-task learning (MTL) model tasked with predicting both general and type-specific biases. While MTL led to some improvement on race bias detection in terms of F1-score, it did not outperform binary classifiers trained specifically on each task.
On general bias detection, the binary classifier achieves up to 0.923 of AUC, a 27.8% improvement over the baseline.
This work lays the foundations for debiasing medical curricula by exploring a novel dataset and evaluating different training model strategies. Hence, it offers new pathways for more nuanced and effective mitigation of bisinformation.
Formal Ethical Obligations in Reinforcement Learning Agents: Verification and Policy Updates
Authors: Shea-Blymyer Colin , Abbas Houssam
Published: November 2024

When designing agents for operation in uncertain environments, designers need tools to automatically reason about what agents ought to do, how that conflicts with what is actually happening, and how a policy might be modified to remove the conflict.
These obligations include ethical and social obligations, permissions and prohibitions, which constrain how the agent achieves its mission and executes its policy.
We propose a new deontic logic, Expected Act Utilitarian deontic logic, for enabling this reasoning at design time: for specifying and verifying the agent's strategic obligations, then modifying its policy from a reference policy to meet those obligations.
Unlike approaches that work at the reward level, working at the logical level increases the transparency of the trade-offs.
We introduce two algorithms: one for model-checking whether an RL agent has the right strategic obligations, and one for modifying a reference decision policy to make it meet obligations expressed in our logic.
We illustrate our algorithms on DAC-MDPs which accurately abstract neural decision policies, and on toy gridworld environments.
Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance
Authors: Tahaei Mohammad , Wilkinson Daricia , Frik Alisa , Muller Michael , Abu-Salma Ruba , Wilcox Lauren
Published: November 2024

Calls for engagement with the public in Artificial Intelligence (AI) research, development, and governance are increasing, leading to the use of surveys to capture people's values, perceptions, and experiences related to AI. In this paper, we critically examine the state of human participant surveys associated with these topics. Through both a reflexive analysis of a survey pilot spanning six countries and a systematic literature review of 44 papers featuring public surveys related to AI, we explore prominent perspectives and methodological nuances associated with surveys to date. We find that public surveys on AI topics are vulnerable to specific Western knowledge, values, and assumptions in their design, including in their positioning of ethical concepts and societal values, lack sufficient critical discourse surrounding deployment strategies, and demonstrate inconsistent forms of transparency in their reporting. Based on our findings, we distill provocations and heuristic questions for our community, to recognize the limitations of surveys for meeting the goals of engagement, and to cultivate shared principles to design, deploy, and interpret surveys cautiously and responsibly.
The Supply Chain Capitalism of AI: A Call to (Re)think Algorithmic Harms and Resistance (Extended Abstract)
Authors: Valdivia Ana
Published: November 2024

Artificial Intelligence (AI) is woven into a supply chain of capital, resources and human labour that has been neglected in debates about the social impact of this technology. Given the current surge in generative AI—which is estimated to use more natural resources than classic machine learning algorithms—it is vital that we better understand its production networks. Building on Tsing’s concept of supply chain capitalism, this paper offers a journey through the AI industry by illustrating the complex, diverse, opaque and global structures of the AI supply chain. The paper then illustrates an ethnographic research in Latin America revealing that AI’s rapid infrastructural growth may be precipitating environmental struggles. Investigating the supply chain capitalism of AI reveals that eco-political frictions are arising. This demands broad critical perspectives on AI studies from a critical perspective by considering the entire capitalist production line of its industry.