Mackenzie Jorgensen

Seattle Villanova Kassel Oxford Münster Villanova London
PhD Candidate in Computer Science · King's College London

I am a Computer Science PhD Candidate funded by the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence at King’s College London. My research focus is on mitigating the harms that can result from algorithmic decision making systems. I am also interested in the intersection of AI, ethics, and technology policy.

Previously, I graduated Phi Beta Kappa and Magna Cum Laude from Villanova University with a Bachelor of Science in May 2020. I studied Computer Science and Philosophy as a Presidential Scholar. As an undergraduate, I completed research projects in the U.S. and Germany in the fields of big data analytics, multi-agent communication and coordination, and hate speech moderation through machine learning.

| April 2024: My EAAMO blog post with HURIDOCS' Director of Development & Communications, Yolanda Booyzen, was published!
| March 2024: I co-presented my research for the KCL School of Cancer and Pharmaceutical Science Humanising Healthcare seminar series.
| March 2024: I co-lectured with my supervisor at the KCL Law School for the LLM in Law and Technology programme--I presented my IEEE TSM publication.
| February 2024: My Humanising Healthcare Podcast episode on on AI, Bias, and Women is out!
| January 2024: My blog post from my AI Summer School team on chatbots and public authorities is published on the Law, Ethics, and Policy of AI Blog!
| December 2023: My paper on the legality of bias mitigation methods in the UK has been published in the IEEE Technology & Society Magazine!
| November 2023: My blog post about my interview with Amnesty Tech's Damini Satija is published.
| September 2023: I'm joining the EAAMO Bridges Conversations with Practitioners working group leadership.
| August 2023: I presented my AAAI/ACM Conference on AI, Ethics, and Society (AIES) paper in Montreal!
| June 2023: I was accepted to and joined the ACM FAccT 2023 Doctoral Colloquium in Chicago!
Email: mackenzie(dot)jorgensen(at)kcl(dot)ac(dot)uk


King's College London

PhD Candidate in Computer Science

I am working to mitigate the harms that AI decision-making systems have on people under the supervision of Dr. Elizabeth Black, Dr. Jose M. Such, and Dr. Natalia Criado Pacheco. I am funded by the UKRI Centre for Doctoral Training (CDT) in Safe and Trusted AI (STAI) and a member of the Distributed AI Research Group. I am a Student Representative on the STAI CDT Staff-Student Liaison Committee representing my 2020 STAI cohort. I am also a visiting postgraduate student at Imperial College London through the STAI CDT.

October 2020 - September 2024

KU Leuven, Faculty of Law and Criminology

Summer School Student on the Law, Ethics, and Policy of AI

I attended the 10 day intensive summer school at KU Leuven on the Law, Ethics, and Policy of AI which is described on their website: The Summer School provided a comprehensive overview of the various legal, ethical and policy-related issues around AI and algorithm-driven processes more broadly. As these technologies have an impact on all domains of our lives, it is important to map, understand and assess the challenges and opportunities they raise. This requires an interdisciplinary approach--the programme gave participants the latest insights on AI from various perspectives. The lectures provided were from renowned academics, policy-makers from EU and international institutions as well as practitioners. I presented some of my interdisciplinary research on the intersection of UK anti-discrimination law at the summer school. I also worked on a group project that culminated in a blog post where my colleagues and I considered the technical, ethical, legal, economic, and societal challenges around a public authority adopting a chatbot to give housing guidance.

September 2023

Villanova University

Bachelor of Science, Magna Cum Laude, Phi Beta Kappa

I completed a Computer Science Major with a Philosophy Minor. I am incredibly grateful to my amazing mentors from Villanova, including Dr. Robert Beck (now retired), the late Dr. Anany Levitin, Associate Computer Science Professor Edward Kim (now at Drexel University), and Computer Science Lecturer Kristin Obermyer. Some of my most informative and fascinating liberal arts courses were taught by Peace & Justice (P&J) Lecturer Carol Anthony, Associate History Professor Hibba Abugideiri, and P&J Professor Timothy Horner. These mentors and professors instilled in me a passion to develop technology that makes our world more socially just.

August 2016 - May 2020

St. Annes College, University of Oxford

Visiting Student

I studied Computer Science & Philosophy during my junior year abroad. I am thankful for Dr. Wendelin Böhmer who was my Personal Tutor at the time; he is now working at TU Delft.

October 2018 - June 2019


Peer Reviewed Publications


Bias Detection Tool Cohort Member

Algorithm Audit, Netherlands

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.

January 2024 - June 2024

Graduate Teaching Assistant

Informatics Department, King's College London, London, UK
January 2024 - March 2024

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.

January 2023 - March 2023

As a GTA for Machine Learning, I ran two weekly labs 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.

September 2021 - December 2021

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.

January 2021 - April 2021

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.

January 2021 - March 2024

PhD Mentor for DAAD Rise Worldwide Recipient

Informatics Department, King's College London, London, UK

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.

August 2022 - September 2022

PhD Intern

Responsible Technology Adoption Unit (prev. Centre for Data Ethics and Innovation), UK Department for Science, Innovation, and Technology, London, UK

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.

February 2022 - July 2022

AI Robustness Researcher/Developer

Ernst & Young UK and STAI CDT Group Project Collaboration, London, UK

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.

January 2021 - April 2021

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.

May 2020 - August 2020

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.

June 2019 - August 2019

Undergraduate Researcher on Artificial Intelligence (AI)

RISE Germany at Universität Kassel, Kassel, Germany

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.

June 2018 - August 2018

Undergraduate Researcher on ML and Big Data

During my first summer of research in Boston, I analyzed Big Data by contributing to the Northeastern Interactive Clustering Engine (NICE) framework. I worked in the Northeastern University Computer Architecture Research Lab, where I programmed a Python interface and Machine Learning algorithms in C++ for NICE data analysis. I was mentored by Computer Science Professor David Kaeli, Computer Engineer Dr. Shi Dong, & PhD Candidate Zulqarnain Khan.

June 2017 - August 2017

Research Presentations

Conference Presentations

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

Leadership & Outreach

London Community

King's College London (Oct 2020-Present)

Villanova University


Awards & Recognition


I love staying active by swimming, running, Irish dancing, hiking, and walking neighborhood dogs (very important). You will typically find me out and about at restaurants, coffeeshops, musicals and theatre, bookshops, queer spaces, and food markets. When I'm not out and about, I'm probably reading, listening to podcasts and music, watching shows, or meditating.