Summary of Dynamic Environment Responsive Online Meta-learning with Fairness Awareness, by Chen Zhao et al.
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness
by Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed fair-aware online learning framework tackles the challenges of continuous lifelong learning, where learners aim to acquire new tasks while ensuring statistical parity among protected sub-populations. Building upon current approaches, which heavily rely on the i.i.d assumption and static regret analysis, this paper introduces a unique regret measure (FairSAR) that incorporates long-term fairness constraints into a strongly adapted loss regret framework. Furthermore, an adaptive fairness-aware online meta-learning algorithm (FairSAOML) is presented to manage bias control and model accuracy in dynamic environments. Theoretical analysis yields sub-linear upper bounds for both loss regret and cumulative violation of fairness constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new approach to learning that makes sure people from different groups have an equal chance of getting the right answer when they learn something new. This is important because it helps make sure algorithms are fair and don’t treat certain groups differently. The approach uses a special type of analysis called “FairSAR” that takes into account fairness constraints over time. It also has a algorithm called “FairSAOML” that adjusts to changing environments by balancing bias control and model accuracy. |
Keywords
* Artificial intelligence * Meta learning * Online learning
