Award Abstract # 2040800
FAI: Fairness in Machine Learning with Human in the Loop
NSF Org: |
IIS
Division of Information & Intelligent Systems
|
Recipient: |
UNIVERSITY OF CALIFORNIA SANTA CRUZ
|
Initial Amendment Date:
|
January 25, 2021 |
Latest Amendment Date:
|
April 24, 2024 |
Award Number: |
2040800 |
Award Instrument: |
Standard Grant |
Program Manager: |
Todd Leen
tleen@nsf.gov
(703)292-7215
IIS
Division of Information & Intelligent Systems
CSE
Directorate for Computer and Information Science and Engineering
|
Start Date: |
February 1, 2021 |
End Date: |
January 31, 2026 (Estimated) |
Total Intended Award
Amount: |
$625,000.00 |
Total Awarded Amount to
Date: |
$625,000.00 |
Funds Obligated to Date:
|
FY 2021 = $625,000.00
|
History of Investigator:
|
-
Yang
Liu
(Principal Investigator)
yangliu@ucsc.edu
-
Mingyan
Liu
(Co-Principal Investigator)
-
Ming
Yin
(Co-Principal Investigator)
-
Parinaz
Naghizadeh Ardabili
(Co-Principal Investigator)
|
Recipient Sponsored Research
Office: |
University of California-Santa Cruz
1156 HIGH ST
SANTA CRUZ
CA
US
95064-1077
(831)459-5278
|
Sponsor Congressional
District: |
19
|
Primary Place of
Performance: |
University of California-Santa Cruz
SOE 3, UC Santa Cruz, 1156 High
Santa Cruz
CA
US
95064-1100
|
Primary Place of
Performance Congressional District: |
19
|
Unique Entity Identifier
(UEI): |
VXUFPE4MCZH5
|
Parent UEI: |
|
NSF Program(s): |
Fairness in Artificial Intelli
|
Primary Program Source:
|
01002122DB NSF RESEARCH & RELATED ACTIVIT
|
Program Reference
Code(s): |
075Z
|
Program Element Code(s):
|
114Y00
|
Award Agency Code: |
4900
|
Fund Agency Code: |
4900
|
Assistance Listing
Number(s): |
47.070
|
ABSTRACT

Despite early successes and significant potential, algorithmic decision-making systems often inherit and encode biases that exist in the training data and/or the training process. It is thus important to understand the consequences of deploying and using machine learning models and provide algorithmic treatments to ensure that such techniques will ultimately serve the social good. While recent works have looked into the fairness issues in AI concerning the ?short-term? measures, the long-term consequences and impacts of automated decision making remain unclear. The understanding of the long-term impact of a fair decision provides guidelines to policy-makers when deploying an algorithmic model in a dynamic environment and is critical to its trustworthiness and adoption. It will also drive the design of algorithms with an eye toward the welfare of both the makers and the users of these algorithms, with an ultimate goal of achieving more equitable outcomes.
This project aims to understand the long-term impact of fair decisions made by automated machine learning algorithms via establishing an analytical, algorithmic, and experimental framework that captures the sequential learning and decision process, the actions and dynamics of the underlying user population, and its welfare. This knowledge will help design the right fairness criteria and intervention mechanisms throughout the life cycle of the decision-action loop to ensure long-term equitable outcomes. Central to this project?s intellectual inquiry is the focus on human in the loop, i.e., an AI-human feedback loop with automated decision-making that involves human participation. Our focus on the long-term impacts of fair algorithmic decision-making while explicitly modeling and incorporating human agents in the loop provides a theoretically rigorous framework to understand how an algorithmic decision-maker fares in the foreseeable future.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
(Showing: 1 - 10 of 35)
(Showing: 1 - 35 of 35)
Tang, Zeyu and Wang, Jialu and Liu, Yang and Spirtes, Peter and Zhang, Kun
"Procedural Fairness Through Decoupling Objectionable Data Generating Components"
, 2024
Citation
Details
Zhu, Zhaowei and Luo, Tianyi and Liu, Yang
"The Rich Get Richer: Disparate Impact of Semi-Supervised Learning"
International Conference on Learning Representations (ICLR)
, 2022
Citation
Details
Yin, Tongxin and Raab, Reilly and Liu, Mingyan and Liu, Yang
"Long-Term Fairness with Unknown Dynamics"
, 2023
Citation
Details
Yetukuri, Jayanth and Hardy, Ian and Vorobeychik, Yevgeniy and Ustun, Berk and Liu, Yang
"Providing Fair Recourse over Plausible Groups"
, 2024
Citation
Details
Liao, Yiqiao and Naghizadeh, Parinaz
"Social Bias Meets Data Bias: The Impacts of Labeling and Measurement Errors on Fairness Criteria"
Proceedings of the AAAI Conference on Artificial Intelligence
, 2023
Citation
Details
Liu, Yang
"Understanding Instance-Level Label Noise: Disparate Impacts and Treatments"
International Conference on Machine Learning
, 2021
Citation
Details
Liu, Yang and Wang, Jialu
"Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial"
35th Conference on Neural Information Processing Systems (NeurIPS 2021)
, 2021
Citation
Details
Pang, Jinlong and Wang, Jialu and Zhu, Zhaowei and Yao, Yuanshun and Qian, Chen and Liu, Yang
"Fairness Without Harm: An Influence-Guided Active Sampling Approach"
, 2024
Citation
Details
Raab, Reilly and Boczar, Ross and Fazel, Maryam and Liu, Yang
"Fair Participation via Sequential Policies"
, 2024
Citation
Details
Raab, Reilly and Liu, Yang
"Unintended Selection: Persistent Qualification Rate Disparities and Interventions"
35th Conference on Neural Information Processing Systems (NeurIPS 2021)
, 2021
Citation
Details
Tang, Wei and Ho, Chien-Ju and Liu, Yang
"Bandit Learning with Delayed Impact of Actions"
35th Conference on Neural Information Processing Systems (NeurIPS 2021)
, 2021
Citation
Details
Tang, Wei and Ho, Chien-Ju and Liu, Yang
"Linear Models are Robust Optimal Under Strategic Behavior"
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics
, 2021
Citation
Details
Tang, Zeyu and Chen, Yatong and Liu, Yang and Zhang, Kun
"Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors"
International Conference on Learning Representations (ICLR)
, 2023
Citation
Details
Estornell, Andrew and Chen, Yatong and Das, Sanmay and Liu, Yang and Vorobeychik, Yevgeniy
"Incentivizing Recourse through Auditing in Strategic Classification"
International Joint Conference on Artificial Intelligence
, 2023
Citation
Details
Estornell, Andrew and Das, Sanmay and Liu, Yang and Vorobeychik, Yevgeniy
"Group-Fair Classification with Strategic Agents"
, 2023
https://doi.org/10.1145/3593013.3594006
Citation
Details
Gemalmaz, Meric Altug and Yin, Ming
"Understanding Decision Subjects' Fairness Perceptions and Retention in Repeated Interactions with AI-Based Decision Systems"
Proceedings of the 5th AAAI/ACM Conference on AI, Ethics, and Society (AIES)
, 2022
https://doi.org/10.1145/3514094.3534201
Citation
Details
Hardy, Ian and Yetukuri, Jayanth and Liu, Yang
"Adaptive Adversarial Training Does Not Increase Recourse Costs"
, 2023
https://doi.org/10.1145/3600211.3604704
Citation
Details
Jin, Kun and Yin, Tongxin and Chen, Zhongzhu and Sun, Zeyu and Zhang, Xueru and Liu, Yang and Liu, Mingyan
"Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts"
Proceedings of the AAAI Conference on Artificial Intelligence
, v.38
, 2024
https://doi.org/10.1609/aaai.v38i11.29191
Citation
Details
Jin, Kun and Yin, Tongxin and Chen, Zhongzhu and Sun, Zeyu and Zhang, Xueru and Liu, Yang and Liu, Mingyan
"Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts"
, 2024
Citation
Details
Jin, Kun and Zhang, Xueru and Khalili, Mohammad Mahdi and Naghizadeh, Parinaz and Liu, Mingyan
"Incentive Mechanisms for Strategic Classification and Regression Problems"
ACM Conference on Economics and Computation (EC)
, 2022
https://doi.org/10.1145/3490486.3538300
Citation
Details
Kang, Mintong and Li, Linyi and Weber, Maurice and Liu, Yang and Zhang, Ce and Li, Bo
"Certifying Some Distributional Fairness with Subpopulation Decomposition"
Neural Information Processing Systems (NeurIPS)
, 2022
Citation
Details
Yang, Yifan and Liu, Yang and Naghizadeh, Parinaz
"Adaptive Data Debiasing through Bounded Exploration"
Advances in neural information processing systems
, 2022
Citation
Details
X. Zhang, M. Khalili
"Fairness Interventions as (Dis)Incentives for Strategic Manipulation"
International Conference on Machine Learning (ICML)
, v.162
, 2022
Citation
Details
Wu, Songhua and Gong, Mingming and Han, Bo and Liu, Yang and Liu, Tongliang
"Fair Classification with Instance-dependent Label Noise"
First Conference on Causal Learning and Reasoning
, 2022
Citation
Details
Wu, Jimmy and Chen, Yatong and Liu, Yang
"Metric-Fair Classifier Derandomization"
International Conference on Machine Learning (ICML)
, 2022
Citation
Details
Wei, Jiaheng and Narasimhan, Harikrishna and Amid, Ehsan and Chu, Wen-Sheng and Liu, Yang and Kumar, Abhishek
"Distributionally Robust Post-hoc Classifiers under Prior Shifts"
International Conference on Learning Representations (ICLR)
, 2023
Citation
Details
Wang, Xinru and Liang, Chen and Yin, Ming
"The Effects of AI Biases and Explanations on Human Decision Fairness: A Case Study of Bidding in Rental Housing Markets"
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23)
, 2023
https://doi.org/10.24963/ijcai.2023/343
Citation
Details
Wang, Jialu and Wang, Xin and Liu, Yang
"Understanding Instance-Level Impact of Fairness Constraints"
International Conference on Machine Learning (ICML)
, 2022
Citation
Details
Wang, Jialu and Liu, Yang and Wang, Xin
"Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search"
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
, 2021
https://doi.org/10.18653/v1/2021.emnlp-main.151
Citation
Details
Chen, Yatong and Raab, Reilly and Wang, Jialu and Liu, Yang
"Fairness Transferability Subject to Bounded Distribution Shift"
Neural Information Processing Systems (NeurIPS)
, 2022
Citation
Details
Chen, Yatong and Tang, Wei and Ho, Chien-Ju and Liu, Yang
"Performative Prediction with Bandit Feedback: Learning through Reparameterization"
, 2024
Citation
Details
Chen, Yatong and Tang, Zeyu and Zhang, Kun and Liu, Yang
"Model Transferability with Responsive Decision Subjects"
International Conference on Machine Learning
, 2023
Citation
Details
Duan, Xiaoni and Ho, Chien-Ju and Yin, Ming
"The Influences of Task Design on Crowdsourced Judgement: A Case Study of Recidivism Risk Evaluation"
Proceedings of the 2022 ACM Web Conference
, 2022
https://doi.org/10.1145/3485447.3512239
Citation
Details
(Showing: 1 - 10 of 35)
(Showing: 1 - 35 of 35)
Please report errors in award information by writing to: awardsearch@nsf.gov.