![Mackenzie Jorgenson](/newimages/person-profile/2022b/mackenzie-jorgensen.xdfc1de17.jpg?w=160&h=202&crop=160,160,0,3&f=webp)
Mackenzie Jorgenson
Biography
Mackenzie Jorgensen is a fourth year PhD student in Computer Science at the Informatics Department at King’s College London. Her research focuses on mitigating harm (e.g., discrimination) from automated decision-making systems and taking into consideration the legal implications of those mitigation methods. She is interested in the intersection of AI, ethics, and policy. She co-runs an online international AI Ethics Reading Group. In 2022, she undertook an internship at the UK government’s Centre for Data Ethics and Innovation, where she worked on the Bias Review and Responsible Demographic Data projects.
Before starting her PhD, she was a Machine Learning Engineer intern at HURIDOCS over the summer of 2020. She graduated magna cum laude from Villanova University with her BSc in 2020; she majored in Computer Science and minored in Philosophy. She studied abroad at St. Anne’s College at Oxford University from 2018-2019. While an undergraduate student, she completed summer research internships at Northeastern University (2017), Universität Kassel (2018), and Universität Münster (2019).
She is a member of IEEE, ACM, and MD4SG (and a co-organiser of the Conversations with Practitioners working group). She also is the Co-President of a Washington state nonprofit, Women Advancing Tomorrow’s Technologists, which teaches girls how to code at a young age. In her spare time in London, she volunteers as a UK Stem Ambassador and as a Bi the Way Co-Organiser for the Opening Doors Charity.
Research Interests
- Artificial Intelligence
- Algorithmic Fairness
- AI Ethics
Thesis Title
Predicting Negative Impacts from “Fair” Machine Learning Models
PhD supervision
Principal supervisor: Dr Elizabeth Black
Second supervisor: Dr Jose Such
More information
Research
![AI network](/ImportedImages/Schools/NMS/informatics/stai/ai-network.x4ff05e99.jpeg?w=780&h=520&crop=780,440,0,40&width=380&height=215&fit=crop&f=webp)
Distributed Artificial Intelligence
The group explores the use of AI in social and economic contexts where an intelligent entity may be interacting with other entities.
Features
Women in STEM: Bridging the gender gap
The troubling gender gap in STEM disciplines has been a topic of discussion for decades. To explore the challenges faced by Science, Technology, Engineering...
![Informatics hero](/newimages/nmes/informatics-hero.x2ec57556.png?crop=1903,558,0,15&f=webp)
Humanising Healthcare podcast - "AI Bias and Women"
Manasi is joined by PhD candidates Mackenzie Jorgensen and Tiarna Lee to discuss Artificial Intelligence (AI), algorithmic fairness and the need for bias...
![Humanising healthcare banner - ai bias and women](/newimages/folsm/hero-banner/humanising-healthcare-banner-ai-bias-and-women.x6a19a4a2.png?w=1903&h=558&f=webp)
Research
![AI network](/ImportedImages/Schools/NMS/informatics/stai/ai-network.x4ff05e99.jpeg?w=780&h=520&crop=780,440,0,40&width=380&height=215&fit=crop&f=webp)
Distributed Artificial Intelligence
The group explores the use of AI in social and economic contexts where an intelligent entity may be interacting with other entities.
Features
Women in STEM: Bridging the gender gap
The troubling gender gap in STEM disciplines has been a topic of discussion for decades. To explore the challenges faced by Science, Technology, Engineering...
![Informatics hero](/newimages/nmes/informatics-hero.x2ec57556.png?crop=1903,558,0,15&f=webp)
Humanising Healthcare podcast - "AI Bias and Women"
Manasi is joined by PhD candidates Mackenzie Jorgensen and Tiarna Lee to discuss Artificial Intelligence (AI), algorithmic fairness and the need for bias...
![Humanising healthcare banner - ai bias and women](/newimages/folsm/hero-banner/humanising-healthcare-banner-ai-bias-and-women.x6a19a4a2.png?w=1903&h=558&f=webp)