Jessie J. Smith

Jessie J. SmithJessie J. SmithJessie J. Smith

Jessie J. Smith

Jessie J. SmithJessie J. SmithJessie J. Smith
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    • Home
    • Portfolio
      • Abstract
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  • Home
  • Portfolio
    • Abstract
    • My Story
    • Interviews
    • Radical AI
    • Medium
    • YouTube
    • GitHub
  • Publications
  • Contact

Current research projects

The Machine Learning Research Limitations Tool

Computer Science Ethics Pedagogy and Evaluation

Computer Science Ethics Pedagogy and Evaluation

Collaboration with Microsoft Research

Paper writing phase

Study type: interview and design

Methods: thematic analysis, critical design, interviews

Key words: ML, AI, research ethics, limitations, reflection, critical HCI


Interviewed 31 ML researchers and experts to prototype and iterate on an "ML Research Limitations Tool" that can be used to help identify and express limitations appropriately in peer-reviewed papers.

Computer Science Ethics Pedagogy and Evaluation

Computer Science Ethics Pedagogy and Evaluation

Computer Science Ethics Pedagogy and Evaluation

Collaboration with CU Boulder

Data analysis phase

Study type: systematic lit review

Key words: ethics, computer science, education

Methods: grounded theory, thematic analysis


Summative study seeking to explore the ways that ethics pedagogy has evolved in computer science classrooms in the SIGCHI , CS, and ACM communities.

Assessing Educator Attitudes for CS Ethics

Computer Science Ethics Pedagogy and Evaluation

Fairness-Aware Recommender System Transparency for Kiva

Collaboration with CU Boulder

Study design phase

Study type: survey study

Key words: ethics, computer science, education, attitudes

Methods: survey, quantitative analysis, thematic analysis


Formative study seeking to explore the attitudes that computing educators have towards incorporating ethics in their classroom, including roadblocks and challenges.


Fairness-Aware Recommender System Transparency for Kiva

Fairness-Aware Recommender System Transparency for Kiva

Fairness-Aware Recommender System Transparency for Kiva

Collaboration with CU Boulder and Kiva

Study design phase

Study type: semi-structured interviews

Methods: Interview, thematic analysis, algorithmic design


Assessing various approaches for algorithmic design to promote provider fairness in a multistakeholder, high-stakes recommendation ML system. Designing and evaluating transparency design interventions for users.

Measuring Success in AI for a More Positive Future

Fairness-Aware Recommender System Transparency for Kiva

Measuring Success in AI for a More Positive Future

Collaboration with IEEE Standards Assocation

Data collection phase

Study type: semi-structured interviews and webinars

Methods: Interview, thematic analysis, group discussions


Analyzing current approaches towards measuring success in AI and discussing alternative metrics for AI that include the triple bottom line (people, planet, profit) in order to prepare for a better future.

selected Previous research projects

Transparency in Fairness-Aware Algorithms

Transparency in Fairness-Aware Algorithms

Transparency in Fairness-Aware Algorithms

Study Completed: Spring 2021

Study type: semi-structured interview

Key words: folk theories, fairness, recommender systems, algorithms, transparency, explanations

Methods: interviews, thematic analysis, design


Exploratory, formative study seeking to explore what users need from explanations in a fairness-aware recommender system.

Human-Centered Explainable AI (XAI)

Transparency in Fairness-Aware Algorithms

Transparency in Fairness-Aware Algorithms

Study Completed: Spring 2021

Study type: semi-structured interview

Key words: XAI, transparency, explanations, predictive resource allocation

Methods: Interview, thematic analysis, design


Exploratory formative study seeking to explore what users of a predictive maintenance system need from XAI while preserving institutional logic.

Integrating Ethics into CS Classrooms

Transparency in Fairness-Aware Algorithms

Participatory Design Towards AI Education

Study Completed: Fall 2020

Study type: curriculum development and evaluation

Key words: Computer science, curriculum, pedagogy, ethics, evaluation

Methods: curriculum development, survey design, teaching and evaluation


Formative study to incorporate ethics curricula and assignments into introductory CS classes. Future studies are being planned.

Participatory Design Towards AI Education

Participatory Design Towards AI Education

Participatory Design Towards AI Education

Study Paused: Spring 2021

Study type: Participatory, Community-Based Design Workshops

Key words: participatory design, explanation, transparency, education

Methods: design workshops, survey, community based design, interviews


Working with high schoolers to explore folk theories of algorithmic literacy and speculative futures for transparency / explainability design.

Co-Defining Radical AI as a Community

Participatory Design Towards AI Education

Auditing ML Algorithms for Discrimination

Study Paused: Fall 2020

Study type: structured interview

Key words: radical, AI, resistance, power, ethics, responsible technology

Methods: Interview, thematic analysis, systematic literature review


Exploratory summative study seeking to co-define "Radical AI" with the responsible technology community from interviews on the Radical AI podcast.

Auditing ML Algorithms for Discrimination

Participatory Design Towards AI Education

Auditing ML Algorithms for Discrimination

Study Completed: Fall 2019

Study type: empirical machine learning experiments and statistical analysis

Methods: Recommender system algorithmic design and deployment, statistical testing, coding


Exploratory study testing various metrics for auditing a recommender system for sexist discrimination and exploring potential causes and solutions.

Curriculum Development for AI Fairness Models

Curriculum Development for AI Fairness Models

Curriculum Development for AI Fairness Models

Study Completed: Fall 2018

Study type: curriculum development

Methods: algorithmic development, curriculum development, design


Creating lesson plans to teach introductory data scientists about fairness concerns, fairness metrics, and auditing ML classification systems for fairness.



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