Call for Proposals

the words

Each year, the Lab releases a Call for Proposals (CFP) for the purpose of funding practical and applied interdisciplinary research in tech ethics.

The focus of the 2024 CFP is “The Ethics of Large-Scale Models.”

Information about the award winners is included below. Final deliverables—e.g., practitioner playbooks, workshops, and white papers, etc.—will be accessible through the Lab’s website after they have been completed.

*Note: While the application period for the 2024 CFP has closed, you can refer to the application packet that was used for more information about the CFP process.

Award Winners

Conflict Moderation: Implementing Bridge-Building Design in LLM Models

Awardees: Emillie de Keulenaar (University of Groningen)
Notre Dame Faculty Collaborator: Lisa Schirch, Keough School of Global Affairs

This project looks at how large language models reproduce conflicts imbued in minority language training datasets, and then proposes frameworks for training and fine-tuning models for bridge-building applications. First, it examines how five closed and open-source LLMs — GPT 3.5 and 4, Llama2, BERT, Mistral and Gemini — respond to hundreds of historically, politically, socially or morally controversial questions, or "controversy prompts", in one majority language (English) and at least two minority languages with a conflict history (for example, Armenian and Azerbaijani, Arabic and Hebrew or other). By comparing results, I look at the discursive ways in which LLMs respond to controversy prompts and pinpoint where there is significant divergence, gaps and biases that obstruct the exchange of information, context and other elements necessary for dialogue across language groups. Then, I formulate a framework to retrain or fine-tune LLMs with additional datasets and bridge-building methods, which I formulate in consultation with peacebuilding and computation experts at the Notre Dame-IBM Technology Ethics Lab, the Council for Tech and Social Cohesion and the UN DPPA Innovation Cell. Retrained or fine-tuned models can be used for content moderation in search, recommendation and other applications, which, when queried with sensitive and conflict-prone keywords ("genocide in Gaza", "Nagorno Karabakh", etc.), may prioritise content that facilitates contextual, historical and other understanding across languages.

Contextualising AI Ethics in Higher Education: Comparing the Ethical Issues Raised by Large-Scale Models in Higher Education Across Countries and Subject Domains

Awardees: Wayne Holmes, Caroline Pelletier (Institut "Jožef Stefan")
Notre Dame Faculty Collaborator: Ying (Alison) Cheng, Psychology

Many Higher Education (HE) institutions have published policies on the use of large- scale models (LSMs), a set of Artificial Intelligence (AI) technologies, in teaching and learning. Such policies appear often to focus exclusively on academic integrity but rarely acknowledge variations in the meaning of ‘integrity’ across different cultural contexts and disciplines. In response, this study aims to examine more contextual understandings of LSM/AI ethics in HE. The project will begin with a systematic comparison of LSM policies across two national contexts in which they are used extensively (US/UK). This will be followed by interviews of teaching faculty to examine how those policies are interpreted and enacted across a range of HE subject domains, from arts to natural sciences. Finally, the outcomes of the policy review and interviews will inform a scalable, mixed-methods survey of faculty from the two countries and the varied subject domains, to reveal generalisable insights.

Cultural Context-Aware Question-Answering Systems: An Application to the Colombian Truth Commission Documents

Awardees: Luis Gabriel Moreno Sandoval (Pontificia Universidad Javeriana)
Notre Dame Faculty Collaborators: Matthew Sisk and Anna Sokol, Lucy Family Institute for Data & Society, and Maria Prada Ramírez, Kroc Institute for International Peace Studies 

The goal of this project is to create a Question-Answering System for the Colombian Truth Commission, the first-ever digital archive of the peace process. However, the archive contains a vast amount of data, making manual analysis techniques impractical. Therefore, the system is necessary to facilitate navigation and understanding of documents. This initiative ensures global access to the archive and explores digital approaches used in peace processes. It contributes valuable knowledge to improve future peacebuilding and conflict resolution efforts worldwide.

Engaging End Users in Surfacing Harmful Algorithmic Behaviors in Large-Scale AI Models

Awardees: Wesley Hanwen Deng, Motahhare Eslami, Ken Holstein, Jason Hong (Carnegie Mellon University)
Notre Dame Faculty Collaborator: Toby Jia-Jun Li, Computer Science and Engineering

Traditional methods of testing AI models for harmful algorithmic behaviors, such as algorithm auditing, can fail to detect major issues given these methods’ reliance on small groups of AI experts. Recent research has shown that end-users, armed with their relevant cultural knowledge and lived experience, can surface harmful algorithmic behaviors that are overlooked by expert-led AI auditing and red teaming. However, there remains a notable absence of tools, guidelines, and processes to facilitate public participation in surfacing harmful algorithmic behaviors in large-scale AI models. To bridge this gap, we propose designing, developing, and evaluating a user-centered, interactive tool that effectively engages end-users in onboarding, exploring, reporting, and discussing the potential harmful AI behaviors exhibited in large-scale AI models. Our goal is to enhance meaningful public engagement to cultivate a responsible and ethical landscape for large-scale AI models.

Ethical Deployment of Generative AI systems in the Public Sector: A Practitioner's Playbook

Awardees: Dhanyashri Kamalakkannan, Shyam Krishnakumar, Titiksha Vashist (The Pranava Institute)
Notre Dame Faculty Collaborator: Notre Dame faculty member to be named

Large scale AI systems, particularly multimodal Large Language Models(LLMs), hold immense potential in transforming how governments regulate, deliver essential services, and interface with citizens. LLM-powered technology solutions are expected to play an important role in making public services more accessible, enhance and personalise public service delivery of goods such as education and health, improve hiring and personnel management, and make policy processes more participatory across governments. However, deployment of generative AI solutions in the public sector, whether to improve public service delivery, augment state capacity, or create new public goods, varies in its larger purpose from end-user or enterprise applications and give rise to substantially different ethical challenges when compared to its application in the private sector. Public deployments of AI need to be citizen-centric, and keep the public good at the core through enabling democratic values like trust, accountability, transparency, protection of rights and ensuring adequate public oversight mechanisms.This project seeks to create an ethical framework for public deployment of Generative AI which will translate ethical principles and existing global and national-level guidelines into a practical and accessible practitioner’s playbook which key decision-makers in government and companies can use as an ethical fitness check to mitigate potential harms before deployment of AI across the public sector.

Ethical LLM-based Approach to Improve Early Childhood Development in Children with Cancer in LMICs

Awardees: Horacio Márquez-González (Hospital Infantil de México Federico Gómez)
Notre Dame Faculty Collaborator: Nitesh Chawla, Computer Science and Engineering, and Angélica Garcia Martínez, Lucy Family Institute for Data & Society 

This project aims to develop a prototype integrating Large Language Models (LLM) and Automated Speech Recognition (ASR) technologies to resolve communication barriers between caregivers and teachers, inside and outside the National Institute of Pediatrics, Hospital Infantil de México Federico Gómez (HIMFG) in México City. This prototype will allow health workers and teachers to address critical early childhood development (ECD) dimensions in children with cancer in accessing community health, nutrition, education, and parental care programs. Success relies on actively engaging teachers and caregivers in assessing needs (phase 1) and ensuring continuous improvement (phase 2), fostering inclusivity, and impacting prioritized groups positively. This holistic approach to health, nutrition, education, and parenting care services is anticipated to improve social and economic development in population subgroups. The ethical implications of this technology will be explored, and we’ll discuss expansion to other countries/regions.

Generative AI and the Social Value of Artifacts: The Case for Saving Photo Morgues

Awardees: Kafui Attoh (CUNY School of Labor and Urban Studies), Jamie Kelly (Vassar College)
Notre Dame Faculty Collaborator: Don Brower, Center for Research Computing

The rise of generative AI will increase the value of physical repositories of knowledge that are not subject to digital manipulation. Going forward, we will increasingly need to be able to verify claims made in the digital world by using non-digital evidence. In some domains, verification will be impossible. However, that is not true of every domain. Given this, we call for the preservation of physical artifacts and the creation of new archives as a safeguard against the potential flood of AI-generated disinformation. Using the case of newspaper photo morgues, we argue that this is especially important in hard cases where market imperatives and copyright diverge for the public’s new interest in preservation. With the support of the Notre Dame-IBM Technology Ethics Lab, we propose developing a white paper aimed at exploring these themes.

How LLMs Modulate our Collective Memory and its Ethical Implications

Awardees: Jasna Čurković Nimac (Catholic University of Croatia)
Notre Dame Faculty Collaborator: Nuno Moniz, Notre Dame-IBM Technology Ethics Lab and Lucy Family Institute for Data & Society

This project will assess the impact of large languages models’ (LLMs) such as GPT on the formation of collective memory. In traditional terms, collective memory is a dynamic product of data selectively provided mainly from the institutions of memory (archives, museums, media, schools). However, AI changes how we access and use data in a public space. The main research of this project pivots around the social and educational uses of AI or how it reshapes the process of declarative memory-making and influences an ethically sustainable and impartial social framework for the construction of collective memory. Practically, how may our use of LLMs be shaping our collective memory concerning critical historical events? Our hypothesis suggests that the probabilistic matrix of tools such as GPT are prone to officialise the most represented narratives in their training data, opening troubling avenues of action in cognitive warfare, with a significant potential to shape our collective memory. To test this hypothesis, we will examine several controversial world events in different languages used by GPT to understand their differences concerning historical factuality and representation across languages and, if so, what are the practical and ethical implications in education, digital policy, and peacebuilding.

How Well Can GenAI Predict Human Behavior? Auditing State-of-the-Art Large Language Models for Fairness, Accuracy, Transparency, and Explainability (FATE)

Awardees: Jon Chun, Katherine Elkins (Kenyon College)
Notre Dame Faculty Collaborator: Yong Suk Lee, Keough School of Global Affairs

This research project targets a pivotal issue at the intersection of technology and ethics: surfacing how Large Language Models (LLMs) reason in high-stakes decision-making over humans. Our central challenge is enhancing the explainability and transparency of opaque black-box LLMs and our specific use-case is predicting recidivism—a real-world application that influences sentencing, bail, and early release decision. To the best of our knowledge, this is the first study to integrate and contrast three different sources of ethical decision: human, statistical machine learning (ML), and LLMs. Methodologically, we propose a novel framework that combines state-of-the-art (SOTA) qualitative analyses of LLMs with SOTA quantitative performance of traditional statistical ML models. Additionally, we compare these two approaches with documented predictions by human experts. This multi-model human-AI approach aims to surface both faulty predictions across all three as well as correlate patterns of both valid and faulty reasoning by LLMs. This configuration offers a more comprehensive evaluation of their performance, fairness, and reliability essential for building trust in LLMs. The anticipated outcomes of our project include a test pipeline to analyze and identify discrepancies and edge cases in both predictions and the reasoning behind them. This pipeline includes automated API scripts, an array of simple to complex prompt engineering strategies, and well as various statistical analyses and visualizations. The pipeline architecture will be designed to generalize to other use cases and accommodate future models and prompt strategies to provide maximal reuse for the AI safety community and future studies. This project not only seeks to advance the field of XAI but also to foster a deeper understanding of how AI can be aligned with ethical principles. By highlighting the intricacies of AI decision-making in a context fraught with moral implications, we underscore the urgent need for models that are not only technologically advanced but also ethically sound and transparent.

Impact of Generative Artificial Intelligence - ChatGPT - on Higher Education in the Global South: Ethics and Sustainability

Awardees: Helen Titilola Olojede, Felix Kayode Olakulehin (National Open University of Nigeria)
Notre Dame Faculty Collaborator: Nitesh Chawla, Computer Science and Engineering

The ubiquitous nature of artificial intelligence permeates all aspects of social life, and education is not exempted. Academic and information integrity, lack of diversity and bias, and privacy and security of learners are some ethical issues in using Generative AI (GenAI), such as ChatGPT, in education. This raises concerns about how educators should use GenAI to design instructional materials that facilitate authentic assessment? And in what ways can AI be employed to promote critical thinking? This study addresses the lack of ethical and effective ways to use GenAI, especially ChatGPT, in teaching, learning and assessment. Through intervention design, survey, Focused Group Discussion (FGD) and a series of structured and unstructured interviews, the study targets higher education lecturers, especially open and distance learning institutions, the major components of the Nigerian higher education sub-sector. The proposed project seeks to respond to the dearth of systematic training on applying GenAI in education among university lecturers in Nigeria, with the ultimate view of suggesting sustainable strategies for promoting ethical, effective and enduring use of GenAI across the Nigerian higher education sub- sector.

LLMs and a Well-Rounded Approach to Human Flourishing

Awardees: Avigail Ferdman (Technion-Israel Institute of Technology)
Notre Dame Faculty Collaborator: Don Howard, Philosophy

Human flourishing ought to be an important ethical concern in Large-Scale Models, but it has yet to receive systematic scholarly attention. This research offers to address this gap, by combining perfectionism—an ethical approach to human flourishing—with an analysis of Large Language Model (LLM) environments. According to developmental perfectionism, humans flourish when they develop and exercise their capacities (to know, create, be sociable, exercise willpower) in well-rounded ways. Capacities are shaped by affordances—action possibilities in the environment. The research will analyze properties of LLM environments (e.g. content generation; speedy data analysis; user interface), according to their affordances (or constraints) for the competent exercise of a well-rounded combination of human capacities. This will provide a new lens from which to evaluate the goodness of LLM design, deployment and use, as well as an opportunity to offer an LLM ethics that goes beyond its current focus on risks and harms.

Mitigating Ethical Risks in Large Language Models through Localized Unlearning

Awardees: Alberto Blanco-Justicia, Josep Domingo-Ferrer, Najeeb Jebreel, David Sánchez (Universitat Rovira i Virgili)
Notre Dame Faculty Collaborator: Nuno Moniz, Notre Dame-IBM Technology Ethics Lab and Lucy Family Institute for Data & Society

During training, large language models (LLMs) can memorize sensitive information or capture biased/harmful patterns present in their training data, which can then be delivered to end users at inference time. These undesirable behaviors undermine societal values and raise ethical risks. The overarching goal of our proposal is to develop an effective and efficient localized unlearning method that mitigates ethical risks in LLMs without compromising their utility. To achieve this goal, we plan to: i) precisely locate the minimal internal components of LLMs responsible for undesirable behaviors; ii) implement efficient target interventions on these components to unlearn those behaviors; and iii) evaluate our method using standard LLMs and data sets. Our expected outcome is to make LLMs more ethically compliant.

Research-Based Theater: An Innovative Method for Communicating and Co-Shaping AI Ethics Research & Development

Awardees: Anastasia Aritzi, Christoph Lütge, Franziska Poszler (Peter Löscher Chair of Business Ethics & Institute for Ethics in Artificial Intelligence, Technical University of Munich)
Notre Dame Faculty Collaborator: Carys Kresny, Film, Television, and Theatre

This project aims to develop and implement an innovative and participatory methodology for teaching, research, and science communication in the field of artificial moral agents (AMAs). Within a hands-on research internship for students at TUM, the project team and students will conduct qualitative interviews on ChatGPT’s role as an AMA, corresponding societal implications and recommended system requirements. These findings will be translated into a theatre script and (immersive) performance. This performance seeks to effectively educate civil society on up-to-date research in an engaging manner and facilitate joint discussions (e.g., on preferred system requirements). The insights from these discussions, in turn, are intended to inform the scientific community and thereby, facilitate a human-centered/value-based development of AMAs. This project should serve as a proof of concept for innovative teaching, science communication and co-design in AI ethics research, thereby acting as the starting point for similar projects in the future.

Seeing the World through LLM-Colored Glasses - Detecting Biases and Deficiencies in Language Model Presentation of Underrepresented Topics

Awardees: Muhammad Ali, Ricardo Baeza-Yates, Shiran Dudy, Resmi Ramachandranpillai, Thulasi Tholeti (Northeastern University Institute for Experiential AI)
Notre Dame Faculty Collaborator: Toby Jia-Jun Li, Computer Science and Engineering

Sources of information on the internet such as Wikipedia have exhibited long-standing disparities in representation across demographic dimensions. Women and gender non-conforming individuals, racial and ethnic minorities, and people from the Global South have all faced difficulties in finding members of their communities or related topics in resources that are considered “definitive” tools for information. The creation of large language models (LLMs) like ChatGPT has introduced a new mediator of information that is increasingly being used in education and beyond. This project will study differences in how LLMs present information about underrepresented topics. Through queries about public figures and geographic locations, we will build metrics to measure disparities in discovery rate, consistency, and sentiment of model responses. These metrics will allow us to better understand the real-world implications of adoption of LLM tools and how future access to information might be skewed or limited by this new technology.

Technology Transfer and Culture in Africa: Large Scale Models in Focus

Awardees: Catherine Botha, Franklyn Echeweodor, Anthony Isong, Edmund Ugar (University of Johannesburg)
Notre Dame Faculty Collaborator: Jaimie Bleck, Political Science

The proposed project comprises a focused, multi-disciplinary investigation of how technology transfer impacts on culture in Africa in the context of large scale models. The project will yield three deliverables: one national workshop, one international conference held in South Africa and one journal special issue devoted to the theme. The impact of technology transfer on culture is an underexplored theme in the literature, and the impact of large scale models is only recently attracting much attention, but not from the perspective of technology transfer. We contend that the theme would benefit from a multi-disciplinary interrogation, to direct policy and law-making within the African context, as well as benefit makers of technologies. A carefully considered theoretical grounding to policy and other decision- making in the area of technology transfer and its impact on culture is, in our view, a first step in understanding this rich topos.

The Ethics of Using Large-Scale Models: Investigating Literacy Interventions for Generative AI

Awardees: Ranjit Singh, Emnet Tafesse (Data & Society Research Institute)
Notre Dame Faculty Collaborator: Karla Badillo-Urquiola, Computer Science and Engineering

This project will explore literacy as a precondition for the ethical use of large-scale generative AI (GAI) models. We will investigate how literacy interventions for students and parents become a site for empirical ethics by building their capacity to handle novel concerns around the increasing use of AI in ordinary settings. We focus on two kinds of literacy efforts: (1) events for community college students that take a gamified approach to testing GAI models; and (2) surveys that document families’ anxieties and aspirations around GAI. While the first effort utilizes events as interventions that collect data on students’ concerns, the second effort collects data on the concerns of families that will, in turn, inform the design of new interventions. Our analysis will reflect on the nature of the critical thinking skills that these interventions produce, and how these skills mutually shape the ethics of using large-scale models.

The Influence of Virtual Avatar Race and Gender on Trust and Performance: Understanding How the Appearance of LLM-Enabled Avatars Influences Work in Virtual Reality

Awardees: Lisa van der Werff, Theo Lynn (Dublin City University)
Notre Dame Faculty Collaborator: Timothy Hubbard, Management & Organization

This project investigates the interactions between individuals and Large Language Models (LLMs)-embodied virtual avatars within virtual reality (VR), focusing on the influence of avatar race and gender. As new technologies like LLMs, VR, and conversational virtual avatars converge, they redefine the future of work, enabling unique collaborations between humans and AI. This project aims to understand how workplace diversity within these technological advancements impact worker dynamics. Through a series of laboratory experiments, we will explore whether and how the race and gender of LLM-embodied avatars affect user interactions, trust levels, and task performance. The study leverages a trust lens to examine biases and differences in engagement with avatars, challenging existing notions of diversity and inclusion. By addressing ethical considerations and providing evidence-based insights, the project seeks to inform future design and policy decisions regarding the deployment of virtual avatars in professional settings, informing ethical integration of AI in the workplace.