Masked Unfairness: Hiding Causality within Zero ATE
Masked Unfairness: Hiding Causality within Zero ATE explores A fairness-aware optimization tool that identifies and mitigates hidden causal unfairness in decision-making processes, even when average treatment effects appear neutral.. Commercial viability score: 6/10 in Fairness AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisFair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual ExplanationsViability: 5.0
- Builds On ThisEvaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in HealthcareViability: 4.0
- Builds On ThisIntersectional Fairness via Mixed-Integer OptimizationViability: 5.0
- Builds On ThisAnterior's Approach to Fairness Evaluation of Automated Prior Authorization SystemViability: 4.0
- Builds On ThisFair Data Pre-Processing with Imperfect Attribute Space