Here you can dive into the heart of our work at the Center for Responsible AI – our research.

Our ongoing investigations are organized around several major themes: data-centric AI and responsible data management, responsible AI education and training, explainability, algorithmic fairness, AI policy and regulation, privacy and data protection, and responsible ranking design.

Below, you will find an extensive compilation of our academic publications and software artifacts, providing a window into our scholarly contributions. Each entry includes an external link to the full text of the paper for a comprehensive understanding.

In addition, you can explore our ongoing technology policy and education projects, complete with detailed descriptions and direct links to the project repositories. We hope that this collection of resources will serve as a valuable tool for researchers, students, and the broader community to engage with our work.

Data-centric AI and responsible data management

Incorporating ethics and legal compliance into data-driven algorithmic systems has been attracting significant attention from the computing research community, most notably under the umbrella of fair and interpretable machine learning. While important, much of this work has been limited in scope to the last mile of data analysis and has disregarded both the system’s design, development, and use life cycle (What are we automating and why? Is the system working as intended? Are there any unforeseen consequences post-deployment?) and the data life cycle (Where did the data come from? How long is it valid and appropriate?). Our work on data-centric responsible AI and on responsible data management is based on the observation that the decisions we make during data collection and preparation profoundly impact the robustness, fairness, and interpretability of the systems we build.

2023

  1. Query Refinement for Diversity Constraint Satisfaction
    Jinyang Li, Yuval Moskovitch, Julia Stoyanovich, and H. V. Jagadish
    Proc. VLDB Endow. 2023
  2. ERICA: Query Refinement for Diversity Constraint Satisfaction
    Jinyang Li, Alon Silberstein, Yuval Moskovitch, Julia Stoyanovich, and H. V. Jagadish
    Proc. VLDB Endow. 2023
  3. Subset Modelling: A Domain Partitioning Strategy for Data-efficient Machine-Learning
    Vı́tor Ribeiro, Eduardo H. M. Pena, Raphael Freitas Saldanha, Reza Akbarinia, Patrick Valduriez, Falaah Arif Khan, Julia Stoyanovich, and Fábio Porto
    In Proceedings of the 38th Brazilian Symposium on Databases, SBBD 2023, Belo Horizonte, MG, Brazil, September 25-29, 2023 2023
  4. Automated Data Cleaning Can Hurt Fairness in Machine Learning-based Decision Making
    Shubha Guha, Falaah Arif Khan, Julia Stoyanovich, and Sebastian Schelter
    In Proceedings of the 39th International Conference on Data Engineering, ICDE 2023
  5. The Many Facets of Data Equity
    H.V. Jagadish, Julia Stoyanovich, and Bill Howe
    ACM Journal of Data and Information Quality 2023
  6. Personal Data for Personal Use: Vision or Reality?
    Xin Luna Dong, Bo Li, Julia Stoyanovich, Anthony Kum Hoe Tung, Gerhard Weikum, Alon Y. Halevy, and Wang-Chiew Tan
    In Companion of the 2023 International Conference on Management of Data, SIGMOD/PODS 2023, Seattle, WA, USA, June 18-23, 2023 2023

2022

  1. Spending Privacy Budget Fairly and Wisely
    Lucas Rosenblatt, Joshua Allen, and Julia Stoyanovich
    Theory and Practice of Differential Privacy (@ICML) 2022
  2. Responsible Data Management
    Julia Stoyanovich, Serge Abiteboul, Bill Howe, H. V. Jagadish, and Sebastian Schelter
    Communications of the ACM 2022
  3. Developing data capability with non-profit organisations using participatory methods
    Anthony McCosker, Xiaofang Yao, Kath Albury, Alexia Maddox, Jane Farmer, and Julia Stoyanovich
    Big Data & Society 2022

2020

  1. Responsible Data Management
    Julia Stoyanovich, Bill Howe, and HV Jagadish
    Proceedings of the VLDB Endowment 2020
  2. FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions
    Sebastian Schelter, Yuxuan He, Jatin Khilnani, and Julia Stoyanovich
    In Proceedings of the 23nd International Conference on Extending Database Technology, EDBT 2020
  3. Fairness-Aware Instrumentation of Preprocessing Pipelines for Machine Learning
    Ke Yang, Biao Huang, Julia Stoyanovich, and Sebastian Schelter
    In Proceedings of the Workshop on Human-In-the-Loop Data Analytics, HILDA at SIGMOD 2020
  4. Taming Technical Bias in Machine Learning Pipelines
    Sebastian Schelter, and Julia Stoyanovich
    IEEE Data Eng. Bull. 2020

2019

  1. Transparency, Fairness, Data Protection, Neutrality: Data Management Challenges in the Face of New Regulation
    Serge Abiteboul, and Julia Stoyanovich
    ACM Journal of Data and Information Quality 2019
  2. MithraLabel: Flexible Dataset Nutritional Labels for Responsible Data Science
    Chenkai Sun, Abolfazl Asudeh, H. V. Jagadish, Bill Howe, and Julia Stoyanovich
    In Proceedings of the 28th International Conference on Information and Knowledge Management, CIKM 2019
  3. The Responsibility Challenge for Data
    H. V. Jagadish, Francesco Bonchi, Tina Eliassi-Rad, Lise Getoor, Krishna P. Gummadi, and Julia Stoyanovich
    In Proceedings of the 2019 International Conference on the Management of Data, SIGMOD 2019
  4. TransFAT: Translating Fairness, Accountability, and Transparency into Data Science Practice
    Julia Stoyanovich
    In Proceedings of the 1st International Workshop on Processing Information Ethically, PIE at CAiSE 2019

Explainability

There is a variety of terms associated with this topic: transparency, interpretability, explainability, intelligibility. But let’s not get too tangled up in terminology. The main point is that we need to allow people to understand the data, the operation, and the decisions or predictions of an AI system, and to also understand why these decisions or predictions are made. This understanding is critical because it allows people to exercise agency and take control over their interactions with AI systems. And so, no matter what terminology we use, the overarching idea behind transparency & friends is to expose the “knobs of responsibility” to people, as a means to support the responsible design, development, use, and oversight of AI systems.

2023

  1. Introducing contextual transparency for automated decision systems
    Mona Sloane, Ian Solano-Kamaiko, Jun Yuan, Aritra Dasgupta, and Julia Stoyanovich
    Nature Machine Intelligence 2023
  2. Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance
    Andrew Bell, Oded Nov, and Julia Stoyanovich
    Data & Policy 2023

2022

  1. Rankers, Rankees, & Rankings: Peeking into the Pandora’s Box from a Socio-Technical Perspective
    Jun Yuan, Julia Stoyanovich, and Aritra Dasgupta
    CoRR 2022
  2. It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy
    Andrew Bell, Ian Solano-Kamaiko, Oded Nov, and Julia Stoyanovich
    In Proceedings of the 5th Annual ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022

2020

  1. The Imperative of Interpretable Machines
    Julia Stoyanovich, Jay J. Van Bavel, and Tessa V. West
    Nature Machine Intelligence 2020

2019

  1. Nutritional Labels for Data and Models
    Julia Stoyanovich, and Bill Howe
    IEEE Data Eng. Bull. 2019
  2. MithraLabel: Flexible Dataset Nutritional Labels for Responsible Data Science
    Chenkai Sun, Abolfazl Asudeh, H. V. Jagadish, Bill Howe, and Julia Stoyanovich
    In Proceedings of the 28th International Conference on Information and Knowledge Management, CIKM 2019

Fairness

Algorithmic fairness is a central topic in responsible AI. Fairness is a complex concept, and we treat it through a socio-legal-technical lens, in ways that are domain- and context-specific. We always start with a statement of the normative criteria (what are we trying to accomplish and why?), and propose technical solutions only as appropriate, in ways that align with the normative criteria. Highlights of our work include fairness in ranking, connections between equality of opportunity from political philosophy and algoroithmic fairness, and investigating the trade-offs between fairness and other normative dimensions of responsible AI.

2023

  1. Setting the Right Expectations: Algorithmic Recourse Over Time
    João Fonseca, Andrew Bell, Carlo Abrate, Francesco Bonchi, and Julia Stoyanovich
    In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO 2023, Boston, MA, USA, 30 October 2023 - 1 November 2023 2023
  2. Query Refinement for Diversity Constraint Satisfaction
    Jinyang Li, Yuval Moskovitch, Julia Stoyanovich, and H. V. Jagadish
    Proc. VLDB Endow. 2023
  3. ERICA: Query Refinement for Diversity Constraint Satisfaction
    Jinyang Li, Alon Silberstein, Yuval Moskovitch, Julia Stoyanovich, and H. V. Jagadish
    Proc. VLDB Endow. 2023
  4. The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice
    Andrew Bell, Lucius Bynum, Nazarii Drushchak, Tetiana Herasymova, Lucas Rosenblatt, and Julia Stoyanovich
    Proceedings of the Conference on Fairness, Accountability, and Transparency (ACM FAccT) 2023
  5. Counterfactual Fairness Is Basically Demographic Parity
    Lucas Rosenblatt, and R Teal Witter
    Proceedings of the AAAI Conference on Artificial Intelligence 2023
  6. The Unbearable Weight of Massive Privilege: Revisiting Bias-Variance Trade-Offs in the Context of Fair Prediction
    Falaah Arif Khan, and Julia Stoyanovich
    CoRR 2023
  7. On Fairness and Stability: Is Estimator Variance a Friend or a Foe?
    Falaah Arif Khan, Denys Herasymuk, and Julia Stoyanovich
    CoRR 2023
  8. Fairness in Ranking: From Values to Technical Choices and Back
    Julia Stoyanovich, Meike Zehlike, and Ke Yang
    In Companion of the 2023 International Conference on Management of Data, SIGMOD/PODS 2023, Seattle, WA, USA, June 18-23, 2023 2023
  9. Counterfactuals for the Future
    Lucius E. J. Bynum, Joshua R. Loftus, and Julia Stoyanovich
    In Proceedings of the AAAI Conference on Artificial Intelligence 2023

2022

  1. Spending Privacy Budget Fairly and Wisely
    Lucas Rosenblatt, Joshua Allen, and Julia Stoyanovich
    Theory and Practice of Differential Privacy (@ICML) 2022
  2. Critical Perspectives: A Benchmark Revealing Pitfalls in PerspectiveAPI
    Lucas Rosenblatt, Lorena Piedras, and Julia Wilkins
    In Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI) 2022
  3. Spending Privacy Budget Fairly and Wisely
    Lucas Rosenblatt, Joshua Allen, and Julia Stoyanovich
    CoRR 2022
  4. Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines
    Falaah Arif Khan, Eleni Manis, and Julia Stoyanovich
    In Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO 2022, Arlington, VA, USA, October 6-9, 2022 2022

2021

  1. The Many Facets of Data Equity
    H. V. Jagadish, Julia Stoyanovich, and Bill Howe
    In Proceedings of the Workshops of the EDBT/ICDT 2021 Joint Conference, Nicosia, Cyprus, March 23, 2021 2021
  2. COVID-19 Brings Data Equity Challenges to the Fore
    H. V. Jagadish, Julia Stoyanovich, and Bill Howe
    Digit. Gov. Res. Pract. 2021
  3. Comparing Apples and Oranges: Fairness and Diversity in Ranking (Invited Talk)
    Julia Stoyanovich
    In 24th International Conference on Database Theory, ICDT 2021, March 23-26, 2021, Nicosia, Cyprus 2021

2020

  1. Responsible Data Management
    Julia Stoyanovich, Bill Howe, and HV Jagadish
    Proceedings of the VLDB Endowment 2020
  2. Fairness-Aware Instrumentation of Preprocessing Pipelines for Machine Learning
    Ke Yang, Biao Huang, Julia Stoyanovich, and Sebastian Schelter
    In Proceedings of the Workshop on Human-In-the-Loop Data Analytics, HILDA at SIGMOD 2020
  3. Taming Technical Bias in Machine Learning Pipelines
    Sebastian Schelter, and Julia Stoyanovich
    IEEE Data Eng. Bull. 2020

2019

  1. Transparency, Fairness, Data Protection, Neutrality: Data Management Challenges in the Face of New Regulation
    Serge Abiteboul, and Julia Stoyanovich
    ACM Journal of Data and Information Quality 2019
  2. MithraRanking: A System for Responsible Ranking Design
    Yifan Guan, Abolfazl Asudeh, Pranav Mayuram, H. V. Jagadish, Julia Stoyanovich, Gerome Miklau, and Gautam Das
    In Proceedings of the 2019 International Conference on the Management of Data, SIGMOD 2019

2017

  1. Measuring Fairness in Ranked Outputs
    Ke Yang, and Julia Stoyanovich
    In Proceedings of the 29th International Conference on Scientific and Statistical Database Management, SSDBM 2017

Policy

We engage in technology policy work in the US and internationally. These practical engagements are based on our research, highlighted below.

2023

  1. Introducing contextual transparency for automated decision systems
    Mona Sloane, Ian Solano-Kamaiko, Jun Yuan, Aritra Dasgupta, and Julia Stoyanovich
    Nature Machine Intelligence 2023
  2. The Algorithmic Transparency Playbook: A Stakeholder-first Approach to Creating Transparency for Your Organization’s Algorithms
    Andrew Bell, Oded Nov, and Julia Stoyanovich
    In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023, Hamburg, Germany, April 23-28, 2023 2023
  3. Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance
    Andrew Bell, Oded Nov, and Julia Stoyanovich
    Data & Policy 2023

2022

  1. It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy
    Andrew Bell, Ian Solano-Kamaiko, Oded Nov, and Julia Stoyanovich
    In Proceedings of the 5th Annual ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022
  2. Developing data capability with non-profit organisations using participatory methods
    Anthony McCosker, Xiaofang Yao, Kath Albury, Alexia Maddox, Jane Farmer, and Julia Stoyanovich
    Big Data & Society 2022

2019

  1. Transparency, Fairness, Data Protection, Neutrality: Data Management Challenges in the Face of New Regulation
    Serge Abiteboul, and Julia Stoyanovich
    ACM Journal of Data and Information Quality 2019

2018

  1. Follow the Data! Algorithmic Transparency Starts with Data Transparency
    Julia Stoyanovich, and Bill Howe
    The Ethical Machine Nov 2018

2017

    Privacy

    Our work in privacy includes peer-reviewed papers, as well as tools and benchmarks. Take a look at the Data Synthesizer tool that we've been using to teach differentially private data synthesis. And check out our latest DP synethetic data benchmarking package, SynRD, posing the question: "Can a DP synthesizer produce private (tabular) data that preserves scientific findings?" In other words, do DP synthesizers satisfy epistemic parity?

    2023

    1. Personal Data for Personal Use: Vision or Reality?
      Xin Luna Dong, Bo Li, Julia Stoyanovich, Anthony Kum Hoe Tung, Gerhard Weikum, Alon Y. Halevy, and Wang-Chiew Tan
      In Companion of the 2023 International Conference on Management of Data, SIGMOD/PODS 2023, Seattle, WA, USA, June 18-23, 2023 2023
    2. Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy
      Lucas Rosenblatt, Bernease Herman, Anastasia Holovenko, Wonkwon Lee, Joshua R. Loftus, Elizabeth Mckinnie, Taras Rumezhak, Andrii Stadnik, Bill Howe, and Julia Stoyanovich
      Proc. VLDB Endow. 2023

    2022

    1. Spending Privacy Budget Fairly and Wisely
      Lucas Rosenblatt, Joshua Allen, and Julia Stoyanovich
      Theory and Practice of Differential Privacy (@ICML) 2022
    2. Spending Privacy Budget Fairly and Wisely
      Lucas Rosenblatt, Joshua Allen, and Julia Stoyanovich
      CoRR 2022

    2020

    1. Differentially private synthetic data: Applied evaluations and enhancements
      Lucas Rosenblatt, Xiaoyan Liu, Samira Pouyanfar, Eduardo Leon, Anuj Desai, and Joshua Allen
      arXiv preprint arXiv:2011.05537 2020

    2018

    1. MobilityMirror: Bias-Adjusted Transportation Datasets
      Luke Rodriguez, Babak Salimi, Haoyue Ping, Julia Stoyanovich, and Bill Howe
      In Big Social Data and Urban Computing - First Workshop, BiDU@VLDB 2018, Rio de Janeiro, Brazil, August 31, 2018, Revised Selected Papers 2018

    2017

    1. DataSynthesizer: Privacy-Preserving Synthetic Datasets
      Haoyue Ping, Julia Stoyanovich, and Bill Howe
      In Proceedings of the 29th International Conference on Scientific and Statistical Database Management, SSDBM 2017

    Ranking

    One kind of algorithm that is at once especially obscure, powerful, and common is the ranking algorithm. Algorithms rank individuals to determine credit worthiness, desirability for college admissions and employment, and compatibility as dating partners. They encode ideas of what counts as the best schools, neighborhoods, and technologies. Despite their importance, we actually can know very little about why one person was ranked higher than another in a dating app, or why one school has a better rank than that one. This is true even if we have access to the ranking algorithm, for example, if we have complete knowledge about the factors used by the ranker and their relative weights, as is the case for US News ranking of colleges. We have been working on several aspects of responsible ranking design and use, including fairness, transparency and interpretability, and stability.

    2023

    1. Fairness in Ranking: From Values to Technical Choices and Back
      Julia Stoyanovich, Meike Zehlike, and Ke Yang
      In Companion of the 2023 International Conference on Management of Data, SIGMOD/PODS 2023, Seattle, WA, USA, June 18-23, 2023 2023
    2. Fairness in Ranking, Part I: Score-Based Ranking
      Meike Zehlike, Ke Yang, and Julia Stoyanovich
      ACM Computing Surveys 2023
    3. Fairness in Ranking, Part II: Learning-to-Rank and Recommender Systems
      Meike Zehlike, Ke Yang, and Julia Stoyanovich
      ACM Computing Surveys 2023

    2022

    1. Rankers, Rankees, & Rankings: Peeking into the Pandora’s Box from a Socio-Technical Perspective
      Jun Yuan, Julia Stoyanovich, and Aritra Dasgupta
      CoRR 2022

    2021

    1. Causal Intersectionality and Fair Ranking
      Ke Yang, Joshua Loftus, and Julia Stoyanovich
      In Symposium on the Foundations of Responsible Computing FORC 2021
    2. Comparing Apples and Oranges: Fairness and Diversity in Ranking (Invited Talk)
      Julia Stoyanovich
      In 24th International Conference on Database Theory, ICDT 2021, March 23-26, 2021, Nicosia, Cyprus 2021

    2019

    1. Towards Responsible Data-driven Decision Making in Score-Based Systems
      Abolfazl Asudeh, H. V. Jagadish, and Julia Stoyanovich
      IEEE Data Eng. Bull. 2019
    2. Balanced Ranking with Diversity Constraints
      Ke Yang, Vasilis Gkatzelis, and Julia Stoyanovich
      In Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
    3. Designing Fair Ranking Schemes
      Abolfazl Asudeh, H. V. Jagadish, Julia Stoyanovich, and Gautam Das
      In Proceedings of the 2019 International Conference on the Management of Data, SIGMOD 2019
    4. MithraRanking: A System for Responsible Ranking Design
      Yifan Guan, Abolfazl Asudeh, Pranav Mayuram, H. V. Jagadish, Julia Stoyanovich, Gerome Miklau, and Gautam Das
      In Proceedings of the 2019 International Conference on the Management of Data, SIGMOD 2019

    2018

    1. On Obtaining Stable Rankings
      Abolfazl Asudeh, H. V. Jagadish, Gerome Miklau, and Julia Stoyanovich
      PVLDB 2018
    2. Online Set Selection with Fairness and Diversity Constraints
      Julia Stoyanovich, Ke Yang, and H. V. Jagadish
      In Proceedings of the 21th International Conference on Extending Database Technology, EDBT 2018
    3. A Nutritional Label for Rankings
      Ke Yang, Julia Stoyanovich, Abolfazl Asudeh, Bill Howe, H. V. Jagadish, and Gerome Miklau
      In Proceedings of the 2018 International Conference on the Management of Data, SIGMOD 2018
    4. Refining the Concept of a Nutritional Label for Data and Models
      Julia Stoyanovich, and Bill Howe
      Freedom to Tinker, Center for Information Technology Policy, Princeton University May 2018

    2017

    1. Measuring Fairness in Ranked Outputs
      Ke Yang, and Julia Stoyanovich
      In Proceedings of the 29th International Conference on Scientific and Statistical Database Management, SSDBM May 2017

    2016

    1. Revealing Algorithmic Rankers
      Julia Stoyanovich, and Ellen P. Goodman
      Freedom to Tinker, Center for Information Technology Policy, Princeton University Aug 2016

    2015

    1. Analyzing Crowd Rankings
      Julia Stoyanovich, Marie Jacob, and Xuemei Gong
      In Proceedings of the 18th International Workshop on Web and Databases, Melbourne, VIC, Australia, May 31, 2015 Aug 2015

    Responsible AI Education

    We cannot understand the impact – and especially the risks – of AI systems without active and thoughtful participation of everyone in society, either directly or through their trusted representatives. To think otherwise is to go against our democratic values. To enable broad participation, we have been developing responsible AI curricula and methodologies for different stakeholders: university students, working practitioners, and the public at large. In this section, you will find our publication on responsible AI education. Take a look at the education area of the site to access our courses and other open-source materials we have developed.

    2023

    1. Responsible AI literacy: A stakeholder-first approach
      Daniel Dominguez, and Julia Stoyanovich
      Big Data and Society 2023
    2. All Aboard! Making AI Education Accessible
      Falaah Arif Khan, Lucius Bynum, Amy Hurst, Lucas Rosenblatt, Meghana Shanbhogue, Mona Sloane, and Julia Stoyanovich
      Center for Responsible AI, New York University 2023
    3. The Algorithmic Transparency Playbook: A Stakeholder-first Approach to Creating Transparency for Your Organization’s Algorithms
      Andrew Bell, Oded Nov, and Julia Stoyanovich
      In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023, Hamburg, Germany, April 23-28, 2023 2023
    4. Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance
      Andrew Bell, Oded Nov, and Julia Stoyanovich
      Data & Policy 2023

    2022

    1. An Interactive Introduction to Causal Inference
      Lucius E.J. Bynum, Falaah Arif Khan, Oleksandra Konopatska, Joshua R. Loftus, and Julia Stoyanovich
      VISxAI: Workshop on Visualization for AI Explainability 2022
    2. Teaching Responsible Data Science
      Julia Stoyanovich
      In Proceedings of the 1st ACM SIGMOD International Workshop on Data Systems Education: Bridging Education Practice with Education Research, DataEd@SIGMOD 2022, 17 June 2022, Philadelphia, PA, USA 2022

    2021

    1. What is AI?
      Julia Stoyanovich, and Falaah Arif Khan
      We are AI Comic Series 2021
    2. What is AI? (Spanish Edition)
      Julia Stoyanovich, and Falaah Arif Khan
      We are AI Comic Series 2021
    3. What is AI? (Greek Edition)
      Julia Stoyanovich, and Falaah Arif Khan
      We are AI Comic Series 2021
    4. Learning from data
      Julia Stoyanovich, and Falaah Arif Khan
      We are AI Comic Series 2021
    5. Learning from data (Spanish Edition)
      Julia Stoyanovich, and Falaah Arif Khan
      We are AI Comic Series 2021
    6. Who lives, who dies, who decides?
      Julia Stoyanovich, Mona Sloane, and Falaah Arif Khan
      We are AI Comic Series 2021
    7. Who lives, who dies, who decides? (Spanish Edition)
      Julia Stoyanovich, Mona Sloane, and Falaah Arif Khan
      We are AI Comic Series 2021
    8. All about that bias
      Julia Stoyanovich, and Falaah Arif Khan
      We are AI Comic Series 2021
    9. All about that bias (Spanish Edition
      Julia Stoyanovich, and Falaah Arif Khan
      We are AI Comic Series 2021
    10. We are AI
      Julia Stoyanovich, and Falaah Arif Khan
      We are AI Comic Series 2021
    11. We are AI
      Julia Stoyanovich, and Falaah Arif Khan
      We are AI Comic Series 2021
    12. Fairness and Friends
      Falaah Arif Khan, Eleni Manis, and Julia Stoyanovich
      Data, Responsibly Comic Series 2021
    13. Teaching Responsible Data Science
      Armanda Lewis, and Julia Stoyanovich
      International Journal of Artificial Intelligence in Education (IJAIED) 2021
    14. We are AI: Taking Control of Technology
      Julia Stoyanovich, and Eric Corbett
      Center for Responsible AI, New York University 2021

    2020

    1. Mirror, Mirror
      Falaah Arif Khan, and Julia Stoyanovich
      Data, Responsibly Comic Series 2020
    2. Mirror, Mirror (French Edition)
      Falaah Arif Khan, and Julia Stoyanovich
      Data, Responsibly Comic Series 2020
    3. Mirror, Mirror (Spanish Edition)
      Falaah Arif Khan, and Julia Stoyanovich
      Data, Responsibly Comic Series 2020
    4. Mirror, Mirror (Portugueze Edition)
      Falaah Arif Khan, and Julia Stoyanovich
      Data, Responsibly Comic Series 2020