Mackenzie Jorgensen

Peer Reviewed Publications

• Bridging Research and Practice Through Conversation: Reflecting on Our Experience
  Mayra Russo, Mackenzie Jorgensen, Kristen M. Scott, Wendy Xu, Di H.Nguyen, Jessie Finocchiaro, & Matthew Olckers
  In Proceedings of ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO 2024).
  [Paper]
• 'Frontier AI,' Power, and the Public Interest: Who Benefits, Who Decides?
  David Leslie, Carolyn Ashurst, Natalia Menéndez González, Frances Griffiths, Smera Jayadeva, Mackenzie Jorgensen, Michael Katell, Shyam Krishna, Doschmund Kwiatkowski, Carolina Iglésias Martins, Sabeehah Mahomed, Carlos Mougan, Shachi Pandit, Mark Richey, Joseph W. Sakshaug, Shannon Vallor, & Luke Vilain
  Harvard Data Science Review, Special Issue 5: Grappling With the Generative AI Revolution, June 2024
  [Paper]
• Investigating the Legality of Bias Mitigation Methods in the United Kingdom
  Mackenzie Jorgensen, Madeleine Waller, Oana Cocarascu, Natalia Criado, Odinaldo Rodrigues, Jose Such, & Elizabeth Black
  IEEE Technology and Society Magazine, Volume 42, Issue 4, December 2023
  [Paper]
• Not So Fair: The Impact of Presumably Fair Machine Learning Models
  Mackenzie Jorgensen, Hannah Richert, Elizabeth Black, Natalia Criado, & Jose Such
  AAAI/ACM Conference on AI, Ethics, and Society (AIES ‘23), Montreal, Canada
  [Paper] [Slides] [Code]
• Supposedly Fair Classification Systems and Their Impacts
  Mackenzie Jorgensen, Elizabeth Black, Natalia Criado, & Jose Such
  2nd Workshop on Adverse Impacts and Collateral Effects of Artificial Intelligence Technologies (AIofAI ‘22), Vienna, Austria
  [Paper] [Slides] [Code]
• Multi-Class Detection of Abusive Language Using Automated Machine Learning
  Mackenzie Jorgensen, Minho Choi, Marco Niemann, Jens Brunk, & Jörg Becker
  15. Internationalen Tagung Wirtschaftsinformatik, WI 2020 (The 15th International Conference on Business Informatics, WI 2020)
  [Paper] [Slides] [BibTex] [Code]
• When does communication learning need hierarchical multi-agent deep reinforcement learning
  Marie Ossenkopf, Mackenzie Jorgensen, & Kurt Geihs
  Cybernetics and Systems: An International Journal, Volume 50, Issue 8 (2019), Special Issue on Intelligent Robotics and Multi-Agent Systems
  [Article] [Citation] [Code]
• Abusive Language Detection Using Auto-Machine Learning for Multiple Languages
  Mackenzie Jorgensen, Minho Choi, & Marco Niemann
  Veritas: Villanova Research Journal, Volume 1 (2019)
  [Technical Report]
• Hierarchical Multi-Agent Deep Reinforcement Learning to Develop Long-Term Coordination
  Marie Ossenkopf, Mackenzie Jorgensen, & Kurt Geihs
  The 34th ACM/SIGAPP Symposium On Applied Computing, 2019
  [Paper] [Code]
• An interactive big data processing/visualization framework
  Mackenzie Jorgensen, Jonathan Spohn, Christopher Bunn, Shi Dong, Xiangyu Li, & David Kaeli
  IEEE/MIT Undergraduate Research Technology Conference, 2017
  [Paper] [Code]

Research Assistant

As a Research Assistant, I am working on a project relating to AI-assisted decision-making, uncertainty, and human-AI oversight. This work is a part of the EU project, ELSA (European Lighthouse on Secure and Safe AI).

Bias Detection Tool Cohort Member

I joined a cohort working remotely on Algorithm Audit's bias detection tool. Specifically, I'm contributing to the NGO's Github repository and the tool's functionality on the Dutch NGO's website. We are applying the tool to a Dutch public sector use case and will write up our findings in a paper.

Graduate Teaching Assistant

As a Graduate Teaching Assistant (GTA) for Machine Learning, I ran weekly labs and marked the coursework. I also led four labs and a couple of tutorials for Data Structures every few weeks and I marked for this course too.

As a GTA for Machine Learning, I ran two weekly labs and tutorials. In addition, I was the GTA for the AI Impact Accelerator module. I also led two tutorials and two labs for Data Structures every three weeks.

As a GTA for Artificial Intelligence, I ran two weekly online live tutorials where I made sure the students were confident with the material and the tutorial sheet answers. I also ran four virtual live labs each week where students worked on developing a successful pacman agent during the Autumn 2021 term.

As a GTA for Machine Learning, I ran two small group live sessions weekly for undergraduates covering the tutorial sheet for that week. I also was a GTA for the Big Data Technologies course. I ran three online live lab sessions weekly for masters students, helped answer any questions they had, and marked coursework.

PhD Mentor for DAAD Rise Worldwide Recipient

I hosted a German undergraduate student researcher over 8 weeks while she worked alongside me on my PhD research project. She was funded by the DAAD RISE Worldwide program. We collaborated with one another and wrote papers on the work we completed.

PhD Intern

I completed a 5 month (~two days a week) PhD Placement at RTA (prev. CDEI)--a part of the UK's Civil Service, enabling the trustworthy use of data and AI. I worked on two different projects: the Responsible Demographic Data and the Bias Review projects. I collaborated with colleagues on both projects, reached out to stakeholders and contributed to those discussions, and lead work in the Bias Review project. For London Tech Week 2022, I co-authored a blog post about demographic data collection and bias detection. I learned a great deal about the intersection of the technology, policy, and data ethics fields and am eager to stay involved with the tech policy space.

AI Robustness Researcher/Developer

Alongside three other PhD Student colleagues, I completed a literature review of AI Robustness methods, zoning in on NLP, weight-poisoning attacks, and poisoning detection methods. We developed a project combining state of the art AI Robustness and NLP research. I was the lead point person for communicating with our stakeholder, EY, every week over the 10 week project. To learn more about what we developed, check out our GitHub repository here: RobuSTAI.

Machine Learning Engineer Intern

While at HURIDOCS, I used state of the art ML explainability techniques to learn about a topic classification model. I improved the topic classification model performance for the Office of the UN High Commissioner for Human Rights. In addition, I documented software and processes for future ML Engineers and I was mentored by the Software Engineer, Gabriel Piles and I worked remotely from Seattle, USA.

Undergraduate Researcher on ML and Natural Language Processing (NLP)

While in Münster, I developed a successful ML/NLP framework to detect abusive language in English and German on Twitter at the the European Research Center for Information Systems with another undergraduate researcher. We implemented the steps of the ML pipeline alongside H2O Auto-Machine Learning in Python. I was mentored by PhD student Marco Niemann.

Undergraduate Researcher on Artificial Intelligence (AI)

I worked in the Distributed Systems Laboratory and collaborated with PhD Candidate Marie Ossenkopf who does great research on multi-agents. We built a deep learning framework that learned communication and coordination for a lever game. More specifically, we implemented a hierarchical deep reinforcement learning framework for a multi-agent system in Lua.

Conference Presentations

• Paper Presenter [Slides] [Paper], AAAI/ACM Conference on AI, Ethics, and Society (AIES ‘23), August 2023
• Poster Presenter, on AIofAI '22 Publication [Poster], STAI Workshop at Imperial College London, November 2022
• Paper Presenter [Slides] [Paper], AIofAI '22 Workshop, July 2022
• Paper Presenter [Paper], 15^th International Conference on Business Informatics, March 2020
• Paper Presenter [Paper], IEEE/MIT Undergraduate Research Technology Conference, 2017, November 2017

Seminars and Symposiums

• Invited Speaker and Co-Presenter on AI and bias in healthcare [Slides], King's College London School of Cancer and Pharmaceutical Science, Humanising Healthcare Seminar Series, March 2024
• Invited Speaker and Co-Lecturer on Legality of Bias Mitigation Methods in UK [Slides], King's College London Law School, LLM in Law and Technology Programme, March 2024
• Paper Presenter on my IEEE TSM Publication [Slides], Imperial College London, STAI CDT Seminar Series, December 2023
• Paper Presenter on my AIES '23 Publication, King's College London Distributed AI Research Group Seminar, July 2023
• PhD Student Presenter, on Using Impact to Evaluate Fair ML Models [Slides], STAI CDT Seminar Series, February 2023
• PhD Student Presenter [Slides], Imperial College London, STAI CDT Seminar Series, May 2022
• AI Fairness Research Presenter [Slides], National Institute for Health Research (NIHR) AI for Multiple Long-Term Conditions Funding Networking Event, July 2021
• Paper Presenter [Slides] [Video], on Liu et al.'s Paper, STAI CDT Year 1 Journal Club, March 2021
• Poster Presenter [Poster], on RISE Germany 2019 Research, Villanova Student Research Symposium, September 2019
• Poster Presenter, on NSF REU 2017 Research, Villanova Student Research Symposium, September 2017
• Poster Presenter, on Villanova First-Year Match Research, Villanova Student Research Symposium, April 2017

AI Ethics Reading Group

• Paper Presenter on Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophy [Slides], October 2023
• Co-Presenter on A Taste of AI Ethics with Jazon Szabo (reading group co-organizer) [Slides], STAI CDT Summer Retreat July 2023
• Paper Presenter on Fairness in Machine Learning: Lessons from Political Philosophy [Slides], July 2023
• Paper Presenter on The Algorithmic Imprint [Slides] April 2023
• Paper Presenter on Mind the Gap: Autonomous Systems, the Responsibility Gap, and Moral Entanglement [Slides], December 2022
• Paper Presenter on Disentangling the Components of Ethical Research in Machine Learning [Slides], October 2022
• Paper Presenter on What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness [Slides], May 2022
• Paper Presenter on The (Im)possibility of Fairness: Different Value Systems Require Different Mechanisms For Fair Decision Making [Slides], October 2021