Google’s ML-fairness-gym lets researchers study the long-term effects of AI’s decisions
Google researchers developed ML-fairness-gym, a set of components for evaluating algorithmic fairness in simulated social environments.
ML-fairness-gym — is designed to be used to research the long-term effects of automated systems by simulating decision-making using OpenAI’s Gym framework. framework help ML practitioners bring simulation-based analysis to their ML systems.
GOOGLE’S NEW ML FAIRNESS GYM HAS A CLEAR MISSION — TRACK DOWN BIAS & PROMOTE FAIRNESS IN AI
How it Works:
AI-controlled agents interact with digital environments in a loop, and at each step, an agent chooses an action that affects the environment’s state. The environment then reveals an observation that the agent uses to inform its next actions so that the environment models the system and dynamics of a problem and the observations serve as data.
Features: Several environments that simulate the repercussions of different automated decisions are available, including one for college admissions, lending, attention allocation, and infectious disease.
ML-fairness-gym can be used to explore phenomena like censoring in the observation mechanism, errors from the learning algorithm, and interactions between the decision policy and the environment.
