Our commitment to responsible AI extends beyond academic research and into the policy landscape. Meaningful change comes from a robust dialogue among researchers, policymakers, community members, and non-profit organizations. Through governmental outreach, we strive to influence AI policies that prioritize fairness, transparency, and the betterment of society. We see ourselves as the bridge that connects the world of AI research with the realms of public policy and community engagement.
AI governance
Statement of Julia Stoyanovich at the U.S. Senate AI Insight Forum on High Impact AI
@inproceedings{DBLP:conf/chi/BellNS23,author={Bell, Andrew and Nov, Oded and Stoyanovich, Julia},title={The Algorithmic Transparency Playbook: {A} Stakeholder-first Approach
to Creating Transparency for Your Organization's Algorithms},booktitle={Extended Abstracts of the 2023 {CHI} Conference on Human Factors in
Computing Systems, {CHI} {EA} 2023, Hamburg, Germany, April 23-28,
2023},pages={554:1--554:4},publisher={{ACM}},year={2023},site={https://r-ai.co/transparency-playbook},doi={10.1145/3544549.3574169},keywords={panel,policy,explanability,education,playbook,governance},addendum={peer-reviewed course},author+an={1=self;3=self}}
Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance
@article{bell_nov_stoyanovich_2023,author={Bell, Andrew and Nov, Oded and Stoyanovich, Julia},title={Think About the Stakeholders First! {T}owards an Algorithmic Transparency
Playbook for Regulatory Compliance},volume={5},journal={Data \& Policy},publisher={Cambridge University Press},year={2023},keywords={journal,policy,explainability,education,playbook,governance},}
Transparency, Fairness, Data Protection, Neutrality: Data Management Challenges in the Face of New Regulation
@article{DBLP:journals/jdiq/AbiteboulS19,author={Abiteboul, Serge and Stoyanovich, Julia},title={Transparency, Fairness, Data Protection, Neutrality: Data Management
Challenges in the Face of New Regulation},journal={ACM Journal of Data and Information Quality},volume={11},number={3},pages={15:1--15:9},year={2019},doi={10.1145/3310231},timestamp={Tue, 20 Aug 2019 08:40:18 +0200},bibsource={dblp computer science bibliography, https://dblp.org},keywords={journal,data,fairness,policy,governance},}
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
@inproceedings{DBLP:conf/fat/BellSNS22,author={Bell, Andrew and Solano{-}Kamaiko, Ian and Nov, Oded and Stoyanovich, Julia},title={It's Just Not That Simple: An Empirical Study of the Accuracy-Explainability
Trade-off in Machine Learning for Public Policy},booktitle={Proceedings of the 5th Annual {ACM} Conference on Fairness, Accountability, and
Transparency, {FAccT}},pages={248--266},publisher={{ACM}},year={2022},doi={10.1145/3531146.3533090},timestamp={Wed, 22 Jun 2022 10:20:30 +0200},bibsource={dblp computer science bibliography, https://dblp.org},keywords={explainability, transparency, policy,governance},}
Developing data capability with non-profit organisations using participatory methods
Anthony McCosker, Xiaofang Yao, Kath Albury, Alexia Maddox, Jane Farmer, and Julia Stoyanovich
@article{doi:10.1177/20539517221099882,author={McCosker, Anthony and Yao, Xiaofang and Albury, Kath and Maddox, Alexia and Farmer, Jane and Stoyanovich, Julia},title={Developing data capability with non-profit organisations using participatory methods},journal={Big Data \& Society},volume={9},number={1},pages={20539517221099882},year={2022},doi={10.1177/20539517221099882},eprint={https://doi.org/10.1177/20539517221099882},keywords={journal,data,policy,governance},}
Algorithmic hiring
Testimony of Julia Stoyanovich before the New York City Department of Consumer and Worker Protection regarding Local Law 144 of 2021 in Relation to Automated Employment Decision Tools (AEDTs)
Testimony of Julia Stoyanovich before the New York City Department of Consumer and Worker Protection regarding Local Law 144 of 2021 in Relation to Automated Employment Decision Tools (AEDTs)
Testimony of Julia Stoyanovich before New York City Council Committee on Technology regarding Int 1894-2020, Sale of automated employment decision tools
@article{NMI_2023,author={Sloane, Mona and Solano-Kamaiko, Ian and Yuan, Jun and Dasgupta, Aritra and Stoyanovich, Julia},title={Introducing contextual transparency for automated decision systems},journal={Nature Machine Intelligence},volume={5},year={2023},pages={187-–195},doi={https://doi.org/10.1038/s42256-023-00623-7},keywords={journal,explainability,policy,hiring},}
Resume Format, LinkedIn pdfs and Other Unexpected Influences on AI Personality Prediction in Hiring: Results of an Audit
Alene K. Rhea, Kelsey Markey, Lauren D’Arinzo, Hilke Schellmann, Mona Sloane, Paul Squires, and Julia Stoyanovich
In Proceedings of the Fifth AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) 2022
@inproceedings{hiringaudit,author={Rhea, Alene K. and Markey, Kelsey and D'Arinzo, Lauren and Schellmann, Hilke and Sloane, Mona and Squires, Paul and Stoyanovich, Julia},year={2022},booktitle={Proceedings of the Fifth AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES)},keywords={conference,stab,audit,hiring},}
An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring
Alene K. Rhea, Kelsey Markey, Lauren D’Arinzo, Hilke Schellmann, Mona Sloane, Paul Squires, Falaah Arif Khan, and Julia Stoyanovich
Data Mining and Knowledge Discovery, Special Issue on Bias and Fairness in AI 2022
@article{hiringaudit_journal,author={Rhea, Alene K. and Markey, Kelsey and D'Arinzo, Lauren and Schellmann, Hilke and Sloane, Mona and Squires, Paul and {Arif Khan}, Falaah and Stoyanovich, Julia},title={An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring},year={2022},journal={Data Mining and Knowledge Discovery, Special Issue on Bias and Fairness in AI},keywords={journal,stab,audit,hiring},}
The Algorithmic Transparency Playbook: A Stakeholder-first Approach to Creating Transparency for Your Organization’s AlgorithmsIn Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023, Hamburg, Germany, April 23-28, 2023 2023
