Admin | Jason S. Lucas

Predicting Sustainable Development Goals with Global AI Readiness: A Hybrid Bayesian Belief Network and Artificial Neural Network Approach

Tue, 01 Sep 2026 00:00:00 +0000

Position: Breaking the Dual Curse of Multilingual AI Requires Socio-Technical Guardrails, Not Post-Hoc Alignment

Mon, 13 Jul 2026 00:00:00 +0000

Position: Multilingual AI safety cannot be retrofitted through post-hoc alignment. We identify a dual curse in current systems and argue for socio-technical guardrails built in from pre-training.

Key contributions:

Dual Curse documented: Harmful content generation rises to 35% in low-resource languages (vs. 1% in English), while instruction-following capability declines sharply across the same languages.
Systematic review of 207 studies: Reward models achieve only 49–50% accuracy in low-resource languages — equivalent to random chance — undermining post-deployment safety pipelines.
Socio-technical prescription: Pre-training interventions, community-led harm specification, and multilingual evaluation metrics that balance security and usability jointly, rather than trading one off for the other.
Call to action: Treat multilingual safety as a first-class design constraint, not a downstream patch applied through RLHF or filtering.

Resources:

Dagstuhl Seminar 26252 — From Speech Translation to Multilingual Communication

Sun, 14 Jun 2026 09:00:00 +0000

I am honored to be invited to Dagstuhl Seminar 26252: “From Speech Translation to Multilingual Communication – New Research Challenges” taking place June 14–17, 2026 at Schloss Dagstuhl in Germany.

This invitation-only seminar will bring together an interdisciplinary group of researchers from speech translation, interpretation studies, and human-computer interaction to explore how AI can better support multilingual communication in real-world scenarios.

The seminar’s focus on bridging the gap between technical advances and end-user needs resonates deeply with my research on addressing the digital language divide.

Organized by Marine Carpuat, Claudio Fantinuoli, Ge Gao, and Jan Niehues.

Mentorship Statement

Sun, 03 May 2026 00:00:00 +0000

Jason Samuel Lucas · Assistant Professor of Information Science · University of Colorado Boulder · Director, Secure and Ethical AI Lab (SEAL)

Philosophy

My mentorship philosophy centers on a single idea: meet students where they are and scaffold pathways to independence. Students arrive at research with different preparation, different starting points, and different reasons to be there. The work of a mentor is to take that seriously, calibrate accordingly, and build the runway each student needs without flattening the standards the work deserves.

Track Record

Through Penn State’s Millennium Scholars program, I have mentored undergraduate researchers on progressively complex projects:

Kendall Reed II advanced from analyzing existing datasets to leading original research, with a forthcoming co-authored publication.
Lia Carin Djaouga progressed from literature reviews to designing cross-lingual transfer frameworks for low-resource language settings.

In both cases, the trajectory was the point: each milestone was scoped just beyond current capability, never punitively, and never below the standard of the work. I have additionally guided junior PhD students on research scoping, mentored undergraduate teams on collaborative robotics with an emphasis on inclusive problem-solving, and coordinated ENVISION: STEM Career Day, an annual event reaching 500+ young women across central Pennsylvania.

SEAL’s Mentoring Model

The Secure and Ethical AI Lab (SEAL) at CU Boulder is built around three commitments:

1. Substantive independence within a coherent program. SEAL students lead their own research questions, but those questions sit inside a shared intellectual frame: AI safety and equity across the digital language divide. Independence is not isolation. Coherence is not constraint.

2. Visibility matters. Students need to see scholars who navigated paths similar to their own. I share my trajectory openly, from Caribbean island student to R1 doctoral candidate, navigating a learning disability and limited resources, because students from underrepresented backgrounds often do not have models for the path they are on. Visibility is not a substitute for support; it is a condition for it.

3. Community over hierarchy. SEAL operates as a research community, not a chain of command. Senior students mentor junior students. Postdocs co-advise undergraduates. The PI is the scaffolding, not the ceiling. Ideas come from everywhere, and credit is distributed accordingly.

Who SEAL Looks For

I welcome PhD students, MS researchers, and undergraduates motivated by AI safety, multilingual NLP, low-resource and dialect-aware language technology, adversarial evaluation, or the broader question of who AI systems serve and who they fail. Strong candidates need not arrive with all the technical pieces in place. They need curiosity, rigor, and a willingness to engage with both the technical and the social dimensions of the work. The technical pieces, we build together.

An Open Invitation

If your research interests intersect with SEAL’s mission, I encourage you to reach out. I am especially committed to mentoring students from Caribbean and African diaspora communities, first-generation graduate students, and others underrepresented in AI research. The lab’s work is improved when its membership reflects the linguistic and cultural diversity of the communities the work is meant to serve.

Prospective students can reach me directly, or apply to the PhD program in Information Science at CU Boulder and indicate interest in SEAL in their application materials.

Last updated: May 2026 · ·

Research Statement

Sun, 03 May 2026 00:00:00 +0000

Jason Samuel Lucas · Assistant Professor of Information Science · University of Colorado Boulder · Director, Secure and Ethical AI Lab (SEAL)

Vision: Trustworthy AI Through Multilingual NLP and Security

The world is multilingual; its digital infrastructure is not. Of the roughly seven thousand languages spoken today, fewer than twenty account for the overwhelming majority of digital content, and English alone accounts for over half. This asymmetry, which I call the digital language divide, is the foundation of my research program: a gap between linguistic reality and digital representation that shapes who AI systems protect, who they fail, and who they leave exploitable.

Large language models have transformed how we process information, enabling sophisticated applications from machine translation to content generation. But the same advances create vulnerabilities. Adversaries weaponize AI to generate harmful content at scale. Information-based attacks undermine public trust and democratic institutions. Safety systems fail to protect diverse linguistic communities. My research addresses these challenges at the intersection of AI, NLP, and security, developing trustworthy AI systems that protect both technology and the communities that use it, regardless of language or resources.

The Technical Problem

Modern LLMs are built through a three-phase training pipeline: pre-training for language semantics and syntax, instruction-tuning for human instruction-following, and alignment-tuning for human values and safety. Each phase inherits the long-tail distribution of its training data, where high-resource languages dominate and low-resource languages, dialects, and creoles sit at the margins. The result is a class of fundamental vulnerabilities that adversaries exploit: safety guardrails that fail in dialects they were never evaluated on, alignment that breaks under code-switching, and instruction-following that degrades in precisely the languages most exposed to harmful content online.

The communities affected are simultaneously under-protected, because the systems were not built to recognize harmful content in their varieties, and over-exposed, because the same gaps that produce inequitable protection produce exploitable attack surfaces. My work refuses the equity-safety separation: these are not parallel problems but the same problem manifesting on two faces of the digital language divide.

Research Questions

My research program investigates three interconnected questions:

RQ1. How can we understand and mitigate vulnerabilities in AI systems, particularly for security-critical applications?
RQ2. How can we extend AI and NLP capabilities to low-resource multilingual communities?
RQ3. Can we develop robust, trustworthy AI systems that maintain effectiveness against adaptive adversaries across languages, domains, and modalities?

The Secure and Ethical AI Lab (SEAL)

These questions structure the work of the Secure and Ethical AI Lab (SEAL) at CU Boulder. SEAL builds AI systems that are safe, interpretable, and equitable across all languages and communities, advancing four interconnected research directions:

Multilingual NLP and Low-Resource AI. Extending model capabilities to underserved languages, dialects, and creoles, with particular emphasis on Caribbean and African diaspora varieties historically absent from benchmark resources.
Trustworthy AI and Information Integrity. Investigating how foundation models generate, propagate, and detect harmful content, building on my F3 framework (EMNLP 2023) for understanding the dual role of LLMs as both source and shield.
Ethical, Equitable, and Human-Centered AI. Developing socio-technical frameworks for responsible model development, articulated through my Dual Curse theory connecting colonial epistemology to multilingual AI safety failures.
AI Safety and Robustness. Building dialect-aware safety guardrails (DIA-Guard), contamination-resistant benchmarks (BLUFF), and adversarial evaluations that surface where models break before deployment does.

Methodological Commitments

SEAL’s work bridges AI innovation with security and equity, ensuring advances benefit all populations regardless of language or resources. Treating equity and safety as one problem rather than two yields different research choices: benchmarks built with affected communities rather than scraped around them, defense models distilled to run where the harm actually occurs, and evaluations designed to catch the failures that matter rather than the failures that are convenient to measure. The lab’s methodology is interdisciplinary by necessity, spanning adversarial machine learning, multilingual NLP, knowledge distillation, and security systems, with deployment in diverse real-world contexts as the standard against which results are judged.

Outlook

Over the next five to seven years, I will build SEAL into a hub for multilingual AI safety research, with sustained focus on contamination-resistant evaluation, dialect-robust safety conditioning, and AI systems for low-resource and Caribbean language communities. The work is supported through a portfolio that spans NSF programs, industry partnerships, and national-laboratory collaborations. The goal is concrete: AI systems whose protection extends to the speakers, dialects, and registers the field has historically left at the margins.

Last updated: May 2026 · ·

Teaching Statement

Sun, 03 May 2026 00:00:00 +0000

Jason Samuel Lucas · Assistant Professor of Information Science · University of Colorado Boulder · Director, Secure and Ethical AI Lab (SEAL)

Philosophy

I teach at the intersection of artificial intelligence, language, and ethics. My classrooms are spaces where students learn to build AI systems and to interrogate them, with equal seriousness given to both. Drawing on a decade of teaching at St. George’s University School of Medicine, where I taught introductory health informatics to over 4,000 medical students, and multiple semesters as a graduate teaching assistant at Penn State across courses including IST 140: Introduction to Application Development and IST 402: Applied Generative AI, I bring three evidence-based principles to every course I teach: active learning through hands-on experience, adaptive instruction responsive to diverse learners, and authentic assessment rooted in real-world practice.

Active Learning Through Hands-On Experience

Lectures alone do not produce the engineers, researchers, and informed citizens that AI now demands. In my redesign of IST 140, I restructured lab sessions around an interactive learning cycle: a 10-minute concept introduction, live coding demonstrations where I deliberately introduce bugs to model debugging as a core skill, paired exercises of progressive difficulty, and peer-led code reviews where students explain solutions to the class. Engagement rose from 45% to 85% across semesters, with a 40% increase in students reporting confidence in their programming abilities. The pedagogy is grounded in cognitive science: immediate application strengthens memory consolidation, peer collaboration exposes students to alternative problem-solving strategies, and public review develops both technical communication and metacognitive awareness.

Adaptive Instruction

Students arrive with different cognitive approaches, languages, and prior preparation. As someone who navigated academic pathways from Grenada to an R1 doctoral program while managing a learning disability, I am committed to inclusive pedagogy that recognizes diverse learning styles. In COMP 420 (Database Systems), I present complex concepts like normalization through three parallel modalities: visual entity-relationship diagrams with color-coded relationships for visual learners, physical index-card exercises for kinesthetic learners, and formal mathematical notation for analytical learners. Students who initially struggled with one modality showed an average 23 percentage-point improvement when offered alternative presentations, while students who already understood the concept reported that alternative explanations deepened their understanding. Continuous formative assessment through in-class polls allows me to dynamically adjust pacing and depth in real time.

Authentic Assessment

Assignments in my courses mirror professional practice. In IST 402 (Applied Generative AI), students complete a semester-long project structured across four phases that mirror an industry workflow:

Problem Definition and Dataset Curation. Students identify a real-world problem domain (past examples include mental health chatbots, multilingual customer service, and accessibility tools) and curate appropriate datasets via Hugging Face.
Prompt Engineering and Baseline Development. Students implement zero-shot and few-shot baselines, document experiments in shared Jupyter notebooks, and present preliminary results in lightning talks.
Advanced Techniques. Students explore multimodal LLMs, custom embedding models, and fine-tuned classification and generation models, conducting ablation studies to understand which components drive performance.
Deployment and Documentation. Students deploy applications via Streamlit, HuggingFace Spaces, or agentic frameworks, write comprehensive documentation, and present in a poster session attended by faculty, peers, and invited industry partners.

Several past projects have evolved into deployed applications and submitted research papers. As one student wrote: “This felt like building a real product, not just completing an assignment.”

The T·I·C Framework

My approach to AI in the classroom is captured in the T·I·C framework, which I developed and have presented in faculty workshops on responsible generative AI:

Transparency. Be explicit, in writing, about when, why, and how AI is or is not permitted. Vague policies create unequal outcomes.
Intentionality. Use AI because it serves a specific pedagogical goal, not because it is convenient. The internal test: what would be lost if students did this without AI?
Criticality. Treat AI as a starting point, not an authority. Surface biases. Question hallucinations. Acknowledge what disciplinary expertise contributes that the model cannot.

The framework is short by design, portable across disciplines, and durable across model generations.

Research-Informed Pedagogy

My research on multilingual AI safety enriches my teaching directly. Students analyze real datasets from my work on harmful content detection across 70+ languages, examine where AI systems fail, and build defensive mechanisms using examples from my Fighting Fire with Fire (F3) framework. They experience firsthand the cat-and-mouse dynamics between attackers and defenders, transforming abstract concepts into tangible challenges with clear societal stakes.

Course Portfolio at CU Boulder

Through SEAL and the Department of Information Science, I am committed to teaching across the undergraduate-to-doctoral pipeline:

Foundations: Applied Generative AI; Natural Language Processing; Responsible AI.
Advanced topics: Multilingual NLP; AI Safety and Adversarial Machine Learning; Fair Machine Learning; AI for Social Good.
Doctoral seminars: Trustworthy AI Across Linguistic Diversity; Research Methods for AI Equity.

Each course integrates research directly into instruction. The classroom and the lab are not separate domains; they are continuous.

Vision

Looking forward, I aim to develop courses at the intersection of AI and social impact, integrate community-engaged learning through partnerships with non-governmental organizations and newsroom collaborators, and contribute to pedagogical scholarship on inclusive practices in computing education. Just as my research seeks to democratize AI capabilities across linguistic boundaries, my teaching seeks to democratize computing education across different backgrounds, abilities, and learning styles.

What Students Take Away

By the end of a course with me, students should be able to do three things they could not do before: build a working AI system, evaluate where and why it fails, and articulate the human stakes of those failures. The first is technical. The second is methodological. The third is moral. All three are required for the field, and all three are within reach of any student willing to engage seriously with the work.

Last updated: May 2026 · ·

DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects

Wed, 08 Apr 2026 00:00:00 +0000

🏆 Social Impact Paper Award — ACL 2026, San Diego, July 2–7, 2026.

DIA-HARM investigates the robustness of harmful content detection systems across 50 English dialects, addressing critical equity gaps in automated content moderation.

Key contributions include:

Dialect-Diverse Detection (D3) Corpus: Over 195K samples derived from benchmark harmful content datasets, transformed using 189 morphosyntactic rules from eWAVE covering 50 dialects
Comprehensive Model Evaluation: 16 detection models tested — 10 fine-tuned, 5 zero-shot, and 1 in-context learning — revealing systematic performance disparities across dialect groups
D-PURIFY Validation: Quality filtering pipeline ensuring linguistic validity of dialect transformations with 97.2% average F1 for the best model (mDeBERTa)
Key Finding: Detection degradation correlates with density of morphosyntactic transformations rather than specific dialect features, with 2,450 dialect pairs analyzed

Resources:

Ethical Use of AI in Research and Teaching

Fri, 27 Mar 2026 16:00:00 +0000

Invited by Dr. Lisa Francis-Charles, Director of Faculty Development at St. George’s University, to facilitate a 50-minute Lunch & Learn session on “Ethical Use of AI in Research and Teaching”.

This session explores responsible, transparent, and ethical uses of AI in both research and teaching, with an emphasis on upholding academic integrity and maintaining professional standards. The topic is central to my current research on information integrity and safe AI.

SGU played a formative role in my own academic and professional career, and I am honored to share insights with the faculty community.

Area Chair — ACL 2026

Thu, 05 Mar 2026 00:00:00 +0000

Serving as Area Chair (OpenReview) for ACL 2026 — the 64th Annual Meeting of the Association for Computational Linguistics, taking place July 7–12, 2026 in San Diego, California.

Coordinate review and meta-review process for submissions in the assigned track
Manage reviewer assignments and discussion to ensure rigorous and fair evaluation
Provide meta-reviews and recommendations for the program committee

Learn more:

BLUFF: Benchmarking in Low-resoUrce Languages for detecting Falsehoods and Fake news

Sun, 01 Feb 2026 00:00:00 +0000

BLUFF is the largest multilingual fake news detection benchmark to date, spanning 79 languages (20 high-resource “big-head” + 59 low-resource “long-tail”) with over 202,000 samples. The benchmark combines human-written fact-checked content from 130 IFCN-certified organizations with LLM-generated content from 19 diverse models.

Key contributions include:

AXL-CoI (Adversarial Cross-Lingual Agentic Chain-of-Interactions): A multi-agentic framework using 10 fake chains and 8 real chains for controlled multilingual content generation
mPURIFY: A 4-stage quality filtering pipeline with 32 features across 5 dimensions, ensuring dataset integrity through asymmetric evaluation thresholds
Bidirectional translation: English↔X coverage across 70+ languages with 4 prompt variants
Comprehensive evaluation: State-of-the-art detectors suffer up to 25.3% Macro-F1 degradation on low-resource versus high-resource languages

Resources:

Program Committee Member — LoResLM 2026 Workshop (EACL)

Thu, 15 Jan 2026 00:00:00 +0000

Serving as Program Committee Member for the , co-located with EACL in Rabat, Morocco. ( )

Reviewed up to four submitted papers, providing rigorous and constructive feedback on research quality
Contributed to ensuring a strong review process for high-quality workshop proceedings
Promoted the workshop within research circles to increase community engagement and participation

Beyond speculation: Measuring the Growing Presence of LLM-generated texts in Multilingual Disinformation

Thu, 01 Jan 2026 00:00:00 +0000

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This work provides the first comprehensive empirical analysis of LLM-generated content in multilingual disinformation campaigns, offering crucial insights for developing cross-linguistic detection strategies.

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Status: This article has been accepted for publication in IEEE (2025). Full publication details including DOI and page numbers will be updated upon final publication.

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Chain-of-Interactions: Iterative ICL Framework for Abstractive Task-Oriented Dialogue Summarization of Conversational AI Interactions

Sun, 31 Aug 2025 00:00:00 +0000

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Graph-based Molecular In-context Learning Grounded on Morgan Fingerprints

Fri, 01 Aug 2025 00:00:00 +0000

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Code and materials are available on .

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Organizing Committee Member — 12th MASC-SLL

Fri, 01 Aug 2025 00:00:00 +0000

Serving as Organizing Committee Member for the at University Park, PA. ( )

Assisted Conference Chairs with abstracts and paper submission
Participated in the presentation, abstract, and manuscript review processes
Assisted with organization and logistics of conference

Beemo - Benchmark of Expert-edited Machine-generated Outputs

Tue, 24 Jun 2025 14:00:00 +0000

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Talk Overview

This presentation introduces Beemo, a groundbreaking benchmark that addresses a critical gap in machine-generated text (MGT) detection research. Unlike traditional benchmarks that only consider single-author scenarios, Beemo captures the reality of human-AI collaboration in text creation.

Key Contributions

1. Multi-Author Benchmark Design

19.6k total texts across five use cases: open-ended generation, rewriting, summarization, open QA, and closed QA
Expert-edited content: 2,187 machine-generated texts refined by professional editors
LLM-edited variants: 13.1k texts edited by GPT-4o and Llama3.1-70B-Instruct using diverse prompts

2. Comprehensive Evaluation

33 MGT detector configurations tested across multiple scenarios
Zero-shot and pretrained detectors including Binoculars, DetectGPT, RADAR, and MAGE
Novel task formulations examining detection performance on edited content

3. Critical Findings

Expert editing evades detection: AUROC scores drop by up to 22% for edited content
LLM-edited texts remain detectable: Less likely to be classified as human-written
Category-specific challenges: Detection performance varies significantly across text types

Implications for AI Safety

This research reveals significant vulnerabilities in current MGT detection systems, particularly relevant for:

Content moderation and misinformation detection
Academic integrity in educational settings
Publishing and journalism authenticity verification
Legal and regulatory compliance for AI-generated content

Future Directions

Beemo opens new research avenues in developing more robust detection methods that account for the collaborative nature of human-AI text creation in real-world applications.

This work was conducted at The Pennsylvania State University in collaboration with Toloka AI, MIT Lincoln Laboratory, and University of Oslo.

Penn State connections lead doctoral student to interdisciplinary College of IST

Mon, 19 May 2025 00:00:00 +0000

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Academic Journey

This feature story traces the interdisciplinary path that led Jason Lucas from the West Indies to Penn State’s College of Information Sciences and Technology, where he’s pursuing groundbreaking research in multilingual AI and harmful content detection.

Key Highlights

Multidisciplinary Foundation

Information Technology: Bachelor’s degree at St. George’s University in Grenada, West Indies
Health Informatics: Master’s in computer information systems at Boston University
Public Health: Master of public health back at St. George’s University
Current Focus: Doctoral degree in informatics at Penn State’s College of IST

Mentorship Network

Theodore Hollis: Professor emeritus from Penn State’s Eberly College of Science, mentor at SGU
Dongwon Lee: Professor and director of doctoral programs, current graduate adviser
LinDiV Program: Transdisciplinary fellowship enhancing linguistic knowledge
PIKE Research Group: Collaborative environment for data mining and security applications

Research Evolution

Perfect alignment: Informatics field matching his interdisciplinary background
AI and NLP solutions: Addressing societal challenges in healthcare and information integrity
Multilingual focus: Developing technologies for languages with limited digital resources
Social impact: Protecting vulnerable communities from manipulation and harmful content

Current Research Impact

PIKE Research Group

Lucas works within the Penn State Information Knowledge and Web (PIKE) Research Group, studying data management and mining across diverse forms with focus on social and security applications.

Research Goals

“Success in my work would look like advancing research for social good and developing deployable technologies that protect vulnerable populations from manipulation and harmful content. My ultimate goal is to further the development of inclusive language technology that not only bridges the digital language divide but also protects people from hidden manipulations that exploit psychosocial biases.”

LinDiV Fellowship Impact

Filling gaps: Providing depth in linguistics and language sciences
Transdisciplinary approach: Combining computational methods with linguistic theory
Community building: Connecting with researchers across disciplines
Research enhancement: Strengthening multilingual NLP capabilities

Looking Forward

Lawrence Livermore National Laboratory Opportunity

Lucas is preparing for the Data Science Summer Institute at LLNL, where he will:

Apply research: Work on projects of national importance
Collaborate: Partner with leading scientists and engineers
Bridge academia: Connect academic research with practical applications
Gain experience: Access high-performance computing resources

Vision for Impact

Lucas aims to develop inclusive language technologies that can detect and counter harmful content across multiple languages, with particular relevance for defending communities whose languages aren’t prioritized by major tech platforms.

The Power of Interdisciplinary Education

“Throughout this journey, I was unconsciously building a multidisciplinary background that would later become my greatest strength. Learning directly from leading clinicians, public health professionals and technology experts across these institutions gave me a unique perspective on solving complex problems at the intersection of these fields.”

Lucas’s story demonstrates how diverse educational experiences, combined with strong mentorship, can create researchers uniquely positioned to tackle complex, real-world challenges.

Read the complete story to learn more about Jason Lucas’s academic journey and his vision for developing inclusive language technologies that protect vulnerable communities worldwide.

Beemo: Benchmark of Expert-edited Machine-generated Outputs

Tue, 01 Apr 2025 00:00:00 +0000

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Beemo benchmark and all materials are publicly available for research use.

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Semantic Captioning: Benchmark Dataset and Graph-Aware Few-Shot In-Context Learning for SQL2Text

Sat, 01 Feb 2025 00:00:00 +0000

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Dataset and codes are accessible for research use.

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Leadership Examples in Tech Rarely Look Like Me

Sun, 01 Dec 2024 00:00:00 +0000

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Student Spotlight

This powerful profile examines how Informatics PhD student Jason Lucas is using his experiences as an underrepresented student to drive both his research and his commitment to mentoring the next generation of tech leaders.

Key Highlights

The Power of Mentorship

Mentoring philosophy rooted in guidance from Professor Emeritus Theodore Hollis
Community engagement through educational programs using dance as a gateway
Student support for undergraduates working on AI for social good and multilingual NLP
Building independence while providing consistent advocacy and support

Navigating Representation Challenges

Stark realities: Being one of the few Black men in his PhD program
Conference experiences: Representing less than 1% of attendees at academic conferences
Identity transition: From Grenada’s diverse environment to academia’s homogeneous spaces
Finding community: Transformative experience at the BLK Men in Tech THRIVE Conference

Research with Purpose

Addressing gaps: How current AI models fail to incorporate Black Afro-Caribbean perspectives
Language inclusion: The absence of Caribbean vernacular and languages like Haitian Creole
Community impact: How low-resource communities are disproportionately affected by tech manipulation
Driving change: Using personal identity as motivation for more inclusive technology

Academic Impact

Current Research Focus

Lucas mentors undergraduate students on projects spanning:

AI for social good applications
Multilingual NLP research through the PIKE Research Lab
Deepfake detection technologies
Millennium Scholars program contributions

The PIKE Research Lab

The Penn State Information, Knowledge, and wEb (PIKE) group focuses on data management and mining across diverse forms, with particular attention to social and security applications—work that directly benefits from Lucas’s inclusive perspective.

Transformative Experiences

BLK Men in Tech THRIVE Conference 2024

Lucas describes this experience as “profound in two ways”:

Community building: Bonding with IST’s Black undergraduate community as their chaperone
Professional networking: Connecting with Black leadership in tech and renewing belief in his potential to excel

The conference highlighted both the community Lucas found and the research gaps his work addresses.

Philosophy in Action

“Having navigated challenges in my own educational journey, I find deep fulfillment in helping students discover their strengths and develop strategies to overcome obstacles. I understand firsthand how important tailored guidance can be, especially for students from underrepresented backgrounds or those facing additional challenges.”

Lucas’s approach focuses on building independence while ensuring students have reliable support, fostering both problem-solving skills and curiosity in the next generation of tech leaders.

Read the full profile to learn more about Jason Lucas’s journey, his research impact, and his vision for creating more inclusive technology that serves all communities.

Generative AI Disproportionately Harms Long Tail Users

Fri, 01 Nov 2024 00:00:00 +0000

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This work examines how Generative AI systems disproportionately impact longtail users, highlighting critical equity concerns in AI deployment.

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How is artificial intelligence being used in journalism now? What's coming next?

Fri, 11 Oct 2024 14:32:00 -0400

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Program Overview

This episode of WXXI’s “Connections” brings together leading researchers to examine artificial intelligence’s transformative impact on journalism and its potential for misuse in disinformation campaigns.

Key Discussion Points

AI in Newsrooms Today

Content automation and efficiency improvements
Fact-checking systems and verification tools
Editorial decision support and audience analytics
Challenges in implementation across different newsroom sizes

The Disinformation Threat

Crisis exploitation: How bad actors target vulnerable moments
Sophisticated generation: Advanced AI creating convincing false content
Detection challenges: The arms race between generation and detection
Community impact: Real-world consequences during emergencies

Research Insights

Jason Lucas presents findings on:

Longtail events: High-impact, resource-limited scenarios where disinformation causes maximum harm
Detection evasion: How AI-generated content is becoming harder to identify
Multilingual campaigns: Cross-linguistic disinformation strategies
Policy implications: Recommendations for media organizations and regulators

Featured Research

The discussion draws from recent academic work including:

Studies on expert-edited machine-generated outputs and detection challenges
Analysis of multilingual disinformation campaigns
Research on longtail impacts of generative AI in crisis situations

Collaborative Perspectives

Felix M. Simon

Research fellow at the Reuters Institute for the Study of Journalism, University of Oxford, sharing insights from “Artificial Intelligence in the News” - a comprehensive four-year study of AI adoption in journalism.

Jason Lucas

Ph.D. student in Informatics at Penn State University, presenting research on AI-driven disinformation tactics and their exploitation during crisis events.

Program Innovation

The show features an AI-generated podcast segment discussing Simon’s research paper, demonstrating both the capabilities and potential concerns around synthetic media in journalism contexts.

Host

David Streever guest hosts this episode, bringing his background in journalism and public media to facilitate this crucial conversation about technology’s role in information integrity.

Listen to the full discussion on WXXI’s “Connections” to hear detailed insights on navigating the complex intersection of artificial intelligence, journalism, and public trust.

Program Committee Member — DAGGenC Workshop (COLING 2025)

Tue, 01 Oct 2024 00:00:00 +0000

Served as Program Committee Member for the , co-located with COLING 2025 in Abu Dhabi, UAE. ( )

Reviewed submitted papers and provided constructive feedback to ensure high-quality contributions
Helped shape the workshop program by evaluating research on advancements and challenges in AI-generated content detection
Participated in discussions and networking with fellow experts in the field

The Longtail Impact of Generative AI on Disinformation: Harmonizing Dichotomous Perspectives

Sun, 01 Sep 2024 00:00:00 +0000

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This work provides a comprehensive analysis of Generative AI’s impact on disinformation in critical contexts where traditional mitigation strategies may be insufficient.

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Graduate Student Bill of Rights Committee — College of IST

Wed, 01 May 2024 00:00:00 +0000

Served on the Graduate Student Bill of Rights Committee at the , Penn State.

Met with key stakeholders
Developed students’ bill of rights

Cohere For AI Invited Talk

Thu, 11 Apr 2024 09:00:00 +0000

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Lightening Talk for the CRA-WP Grad Cohort Workshop for IDEALS

Thu, 11 Apr 2024 09:00:00 +0000

Note

Click on the Slides button above to view the built-in slides feature.

Slides can be added in a few ways:

Create slides using Hugo Blox Builder’s feature and link using slides parameter in the front matter of the talk file
Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
Embed your slides (e.g. Google Slides) or presentation video on this page using .

Further event details, including such as image galleries, can be added to the body of this page.

Conference Reviewer — ACL, EACL, EMNLP

Mon, 15 Jan 2024 00:00:00 +0000

Served as Conference Reviewer for leading NLP venues (2024–2025):

ACL conference review
EACL conference review
EMNLP conference review

Authorship Obfuscation in Multilingual Machine-Generated Text Detection

Fri, 05 Jan 2024 00:00:00 +0000

Note

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Note

Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including .

Presented at the EMNLP '23 Main Conference Proceedings

Sat, 09 Dec 2023 09:00:00 +0000

Note

Click on the Slides button above to view the built-in slides feature.

Slides can be added in a few ways:

Create slides using Hugo Blox Builder’s feature and link using slides parameter in the front matter of the talk file
Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
Embed your slides (e.g. Google Slides) or presentation video on this page using .

Further event details, including such as image galleries, can be added to the body of this page.

Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation

Tue, 05 Dec 2023 00:00:00 +0000

Note

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Note

Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including .

MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark

Tue, 05 Dec 2023 00:00:00 +0000

Note

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Note

Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including .

St. George's University Invited Talk on Artifical Intelligence and Latest AI Research

Thu, 16 Nov 2023 09:00:00 +0000

Note

Click on the Slides button above to view the built-in slides feature.

Slides can be added in a few ways:

Create slides using Hugo Blox Builder’s feature and link using slides parameter in the front matter of the talk file
Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
Embed your slides (e.g. Google Slides) or presentation video on this page using .

Further event details, including such as image galleries, can be added to the body of this page.

Presented a poster at NRT Annual Meeting Arizona '23

Mon, 30 Oct 2023 09:00:00 +0000

Note

Click on the Slides button above to view the built-in slides feature.

Slides can be added in a few ways:

Create slides using Hugo Blox Builder’s feature and link using slides parameter in the front matter of the talk file
Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
Embed your slides (e.g. Google Slides) or presentation video on this page using .

Further event details, including such as image galleries, can be added to the body of this page.

Presented at the Penn State Research in Action

Sat, 09 Sep 2023 09:00:00 +0000

Note

Click on the Slides button above to view the built-in slides feature.

Slides can be added in a few ways:

Create slides using Hugo Blox Builder’s feature and link using slides parameter in the front matter of the talk file
Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
Embed your slides (e.g. Google Slides) or presentation video on this page using .

Further event details, including such as image galleries, can be added to the body of this page.

Conference Volunteer — EMNLP 2023

Tue, 01 Aug 2023 00:00:00 +0000

Served as Conference Volunteer at the Empirical Methods in Natural Language Processing (EMNLP) 2023.

Provided technical support for the main conference
Provided poster presentations support and coordination
Assisted with participant registration and check-in

IT Officer — Penn State Pan-African Professional Alliance

Tue, 01 Aug 2023 00:00:00 +0000

Serving as IT Officer for the Penn State Pan-African Professional Alliance.

Set IT goals and lead initiatives
Managed organization website
Directed IT operations for organization’s events and activities
Supported organization’s president and executive members

Lightening Talk for the CRA-WP Grad Cohort Workshop for IDEALS

Tue, 21 Mar 2023 09:00:00 +0000

Note

Click on the Slides button above to view the built-in slides feature.

Slides can be added in a few ways:

Create slides using Hugo Blox Builder’s feature and link using slides parameter in the front matter of the talk file
Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
Embed your slides (e.g. Google Slides) or presentation video on this page using .

Further event details, including such as image galleries, can be added to the body of this page.

Instructor — ENVISION: STEM Career Day Supporting Young Women

Tue, 01 Nov 2022 00:00:00 +0000

Served as Instructor for ENVISION: STEM Career Day Supporting Young Women at University Park, PA.

Led LinDiv outreach fellowship activities
Prepared presentation material and organized training

President — Graduate Student Association in IST

Tue, 01 Nov 2022 00:00:00 +0000

Served as President of the Graduate Student Association in Information Sciences & Technology (GSAIST) at Penn State.

Led an executive team of 5 members serving 200+ graduate students
Revised organization’s mission and vision towards strategic change
Conducted mass mixed-method student survey to assess needs, gaps, and college climate
Analyzed data using thematic analysis and descriptive statistics, and generated students’ feedback report
Advocated for students by presenting findings to the college deans, program directors, and administrative staff
Successfully effected change in student academics, engagement, and experience for 200+ students

Outreach Coordinator — NSF LINDIV Graduate Research Training Program

Mon, 01 Aug 2022 00:00:00 +0000

Served as Outreach Coordinator for the NSF LINDIV Graduate Research Training Program at Penn State.

Coordinated outreach for the NSF LINDIV Graduate research training program 2022
Led a team of 7 to execute a STEM career day workshop, , supporting young women

Presented at the 6th Pan African Proffessional Alliance Conference Proceedings

Mon, 09 May 2022 09:00:00 +0000

Note

Click on the Slides button above to view the built-in slides feature.

Slides can be added in a few ways:

Create slides using Hugo Blox Builder’s feature and link using slides parameter in the front matter of the talk file
Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
Embed your slides (e.g. Google Slides) or presentation video on this page using .

Further event details, including such as image galleries, can be added to the body of this page.

Presented at the ACL '22 Workshop Conference Proceedings

Mon, 09 May 2022 09:00:00 +0000

Note

Click on the Slides button above to view the built-in slides feature.

Slides can be added in a few ways:

Create slides using Hugo Blox Builder’s feature and link using slides parameter in the front matter of the talk file
Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
Embed your slides (e.g. Google Slides) or presentation video on this page using .

Further event details, including such as image galleries, can be added to the body of this page.

Detecting False Claims in Low-Resource Regions: A Case Study of Caribbean Islands

Sun, 01 May 2022 00:00:00 +0000

Note

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Note

Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including .

IT Officer — Grenada Red Cross

Wed, 01 Jul 2020 00:00:00 +0000

Served as IT Officer for the Grenada Red Cross in St. George’s, Grenada.

Led information technology initiatives
Mentored and supervised interns
Oversaw web development
Supported the president

Director & Co-founder — Fit For Life Youth Development Program

Tue, 01 Jun 2010 00:00:00 +0000

Served as Director and Co-founder of Fit For Life: Youth Development Program in St. George’s, Grenada.

Partnered with Fit For Life to develop a free youth development program for marginalized youths
Mentored youths ages 7–27 to develop leadership, social, and professional skills
Trained next-generation dancers and youth leaders