Empowering and Enhancing Workers Through Building A Community-Centered Gig Economy
The gig economy is characterized by short-term contract work performed by independent workers who are paid in return for the "gigs" they perform. Example gig platforms include Uber, Lyft, Postmates, Instacart, UpWork, and TaskRabbit. Gig economy platforms bring about more job opportunities, lower barriers to entry, and improve worker flexibility. However, growing evidence suggests that worker wellbeing and systematic biases on the gig economy platforms have become significant societal problems. For example most gig workers lack financial stability, have low earning efficiency and lack autonomy, and many have health issues due to long work hours and limited flexibility. Additionally, gig economy platforms perpetuate biases against already vulnerable populations in society. To address these problems, this project aims to build a community-centered, meta-platform to provide decision support and data sharing for gig workers and policymakers, in order to develop a more vibrant, healthy, and equitable gig economy.
The project involves three major research activities. (1) Working with gig workers and local policymakers to understand their concerns, challenges, and considerations related to gig worker wellbeing, as well as the current practices, problems, and biases of existing gig economy platforms. (2) Developing a data-driven and human-centered decision-assistance environment to help gig workers make "smart" decisions in navigating and selecting gigs,and provide a macrolevel perspective for policymakers working to balance their diverse set of objectives and constraints. (3) Deploying and evaluating whether and how the above environment addresses the fundamental problems of worker wellbeing and systematic biases in the gig economy.
-
Performance PeriodOctober 2020 - September 2024
-
Carnegie-Mellon University
-
Award Number1952085
-
Lead PIHaiyi Zhu -
Co-PIZhiwei Wu
-
Co-PIYanhua Li -
Co-PIMin Kyung Lee -
Co-PIDavid Burtch
Project Material
- COVID-GAN: Estimating Human Mobility Responses to COVID-19 Pandemic through Spatio-Temporal Conditional Generative Adversarial Networks
- Is Reinforcement Learning the Choice of Human Learners?: A Case Study of Taxi Drivers
- Cycling-Net: A Deep Learning Approach to Predicting Cyclist Behaviors from Geo-Referenced Egocentric Video Data
- cST-ML: Continuous Spatial-Temporal Meta-Learning for Traffic Dynamics Prediction
- TrajGAIL: Trajectory Generative Adversarial Imitation Learning for Long-Term Decision Analysis
- cGAIL: Conditional Generative Adversarial Imitation Learning—An Application in Taxi Drivers’ Strategy Learning
- Mental health during the COVID-19 pandemic: Impacts of disease, social isolation, and financial stressors
- Spatial-Temporal Augmented Adaptation via Cycle-Consistent Adversarial Network: An Application in Streamflow Prediction
- Self-supervised Pre-training for Robust and Generic Spatial-Temporal Representations
- Distributional Cloning for Stabilized Imitation Learning via ADMM
- CAC: Enabling Customer-Centered Passenger-Seeking for Self-Driving Ride Service with Conservative Actor-Critic
- Geo-Foundation Models: Reality, Gaps and Opportunities
- "Nip it in the Bud": Moderation Strategies in Open Source Software Projects and the Role of Bots
- ST-iFGSM: Enhancing Robustness of Human Mobility Signature Identification Model via Spatial-Temporal Iterative FGSM
- Seeing Seeds Beyond Weeds: Green Teaming Generative AI for Beneficial Uses
- Co-Designing Alternatives for the Future of Gig Worker Well-Being: Navigating Multi-Stakeholder Incentives and Preferences
- Designing Individualized Policy and Technology Interventions to Improve Gig Work Conditions
- STM-GAIL: Spatial-Temporal Meta-GAIL for Learning Diverse Human Driving Strategies
- Domain Disentangled Meta-Learning
- Understanding Frontline Workers’ and Unhoused Individuals’ Perspectives on AI Used in Homeless Services
- Measuring the Stigmatizing Effects of a Highly Publicized Event on Online Mental Health Discourse
- A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms
- Learning to Become a Volunteer Counselor: Lessons from a Peer-to-Peer Mental Health Community
- Matching for Peer Support: Exploring Algorithmic Matching for Online Mental Health Communities
- STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19
- EgoSpeed-net: forecasting speed-control in driver behavior from egocentric video data
- Mest-GAN: Cross-City Urban Traffic Estimation with Meta Spatial-Temporal Generative Adversarial Networks
- STrans-GAN: Spatially-Transferable Generative Adversarial Networks for Urban Traffic Estimation
- Comparing Experts and Novices for AI Data Work: Insights on Allocating Human Intelligence to Design a Conversational Agent
- Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
- Improved Regret for Differentially Private Exploration in Linear MDP,
- Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
- How are ML-Based Online Content Moderation Systems Actually Used? Studying Community Size, Local Activity, and Disparate Treatment
- Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits
- The Model Card Authoring Toolkit: Toward Community-centered, Deliberation-driven AI Design
- “Why Do I Care What’s Similar?” Probing Challenges in AI-Assisted Child Welfare Decision-Making through Worker-AI Interface Design Concepts
- Urban Traffic Dynamics Prediction—A Continuous Spatial-temporal Meta-learning Approach
- COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features
- A Little Too Personal: Effects of Standardization versus Personalization on Job Acquisition, Work Completion, and Revenue for Online Freelancers
- HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data
- Algorithmic Management Reimagined For Workers and By Workers: Centering Worker Well-Being in Gig Work
- Participatory Design of AI Systems: Opportunities and Challenges Across Diverse Users, Relationships, and Application Domains
- Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support
- How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions
- Employment relationships in algorithmic management: A psychological contract perspective
- SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization
- Imitation Learning From Inconcurrent Multi-Agent Interactions
- C3-GAN: Complex-Condition-Controlled Urban Traffic Estimation through Generative Adversarial Networks
- Deep Incremental RNN for Learning Sequential Data: A Lyapunov Stable Dynamical System
- DAC-ML: Domain Adaptable Continuous Meta-Learning for Urban Dynamics Prediction
- ICFinder: A Ubiquitous Approach to Detecting Illegal Hazardous Chemical Facilities with Truck Trajectories
- Learning Decision Making Strategies of Non-experts: A NEXT-GAIL Model for Taxi Drivers
- BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation
- Together But Alone: Atomization and Peer Support among Gig Workers
- DILSA+: Predicting Urban Dispersal Events through Deep Survival Analysis with Enhanced Urban Features
- MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi-task Learning
- Participatory Algorithmic Management: Elicitation Methods for Worker Well-Being Models
- Algorithmic management in a work context
- Join, Stay or Go?: A Closer Look at Members' Life Cycles in Online Health Communities
- SCCS: Smart Cloud Commuting System with Shared Autonomous Vehicles
- Adaptive Reduced Rank Regression
I am the Daniel P. Siewiorek Associate Professor of Human-Computer Interaction and the Director of HCI Undergraduate Programs at Carnegie Mellon University. I received a B.S in Computer Science from Tsinghua University and an M.S. and a Ph.D. in Human-Computer Interaction from Carnegie Mellon University. I have received multiple NSF awards (CRII, Cyber Human System, EAGER on AI and Society, Fairness in AI, and Smart and Connected Communities), several paper awards in venues such as CHI, CSCW, and Human Factors, and an Allen Newell Award for Research Excellence.