Abstract
PRESENTATIONS & LECTURES
Dissertation Defense, 2025
In my dissertation defense, I describe the motivation for my PhD work, the five research studies included in my dissertation, and the implications and lessons I learned during my degree.
Dissertation Title
Measuring the Immeasurable: Perspectives on Best Practices When Operationalizing Machine Learning Fairness for Recommender Systems
FAccT Conference, 2023
In this paper talk, I describe four fairness logics (definitions) that were uncovered during our research interview study with Kiva employees, and the implications of these logics as they are enacted through recommender system design.
The Web Conference, 2023
In this sneak-peak video, I describe some of the motivations, methods, and key findings from our published paper: Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners’ Perspective.
Data Ethics Masterclass, 2023
In this 2-part lecture, I go through the basics of data ethics, how to do data ethics work, the history of big data, and scandals that motivate this work. The lecture is a great resource for a data ethics module and includes discussions and a case-study activity for students to complete.
RecSys Conference, 2022
In this presentation, I explain the ins and outs of algorithmic hate for our Late Breaking Work: Recommender Systems and Algorithmic Hate.
RecSys Conference, 2022
In this presentation, I describe the early stages of my dissertation research for the RecSys Doctoral Symposium.
FAccT Conference, 2022
In this presentation, I detail the key findings from our FAccT22 paper and tool -- REAL ML: Recognizing, Exploring, and Articulating Limitations in Machine Learning.
UMAP Conference, 2021
In this presentation, Nasim Sonboli and I detail the key findings from our UMAP 21 paper -- Fairness and Transparency in Recommendation: The Users' Perspective.
NCWIT Webinar, 2021
In this NCWIT Webinar, I was invited to speak about my journey into the field of CS Ethics, what this field is, and why it is so important for our future.
FAccT Conference, 2021
Building ethics into introductory coding assignments: my presentation from our Tutorial, "A Crash Course in Motivating, Supporting and Expanding Ethical Thinking in the Tech Classroom"
FAT* Conference, 2020
How can we create "fair" algorithms when we disagree on what it means for something to be "fair"?
Coding For Social Impact, 2019
The main lecture from thie workshop I planned and led in Santiago de Cali, Colombia. The workshop is partially in Spanish and English.
Topics: corruption, crime, open data, data science, coding, dataviz
Ethics of Machine Learning Talk, 2018
Topics: ML fairness, training data bias, fairness metrics, performance metrics, social responsibility for data scientists
Tutorial from Jess' "Data Science for Social Impact" workshop in Cali, Colombia 2019.
Public Scholarship on Tech Ethics
The Radical AI Podcast
Interviewing experts on the most pressing issues in AI Ethics while building a movement to address the rooted issues embedded in AI systems.
SciFi in Real Life
Taking ethical dilemmas introduced in SciFi stories and applying them to contemporary issues with technology through the power of storytelling and case-studies.