OccuReward: LLM-Guided Occupant-Centric Reward Shaping for Demographic Equity in Grid-Interactive Buildings
arXiv:2605.28168v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated promising capability in generating reward functions for deep reinforcement learning (DRL)-based building energy management. However, their potential to exhibit or exacerbate disparities in occupant comfort across heterogeneous demographic populations remains unexplored. We present OccuReward, a framework investigating how LLM-mediated reward design affects demographic equity. Our contribution is three-fold: the introduction of the Comfort Equity Index (CEI) as a novel feedback signal; a methodology f
The increasing integration of LLMs into critical infrastructure management and the growing awareness of demographic disparities in technology prompt this examination of equitable AI application in energy systems.
A strategic reader should care because this research addresses fundamental questions about AI's potential to exacerbate or mitigate inequality in resource allocation, directly impacting social stability and regulatory frameworks.
The explicit focus on 'demographic equity' and the introduction of a 'Comfort Equity Index' in LLM-guided building management shifts the conversation beyond pure efficiency to include social fairness in smart systems design.
- · AI ethicists
- · Smart building developers focused on social impact
- · Underrepresented demographic groups
- · Urban planners
- · Developers solely focused on energy cost optimization
- · Systems that perpetuate 'cold start' problems for minorities
AI-driven building management systems will begin incorporating explicit equity metrics into their optimization functions.
Public policy and building codes may start requiring AI systems to demonstrate fairness and equity in resource distribution.
This could lead to a broader philosophical debate about how AI defines and delivers 'comfort' or 'utility' across diverse human populations, challenging purely economic or energy-centric views.
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Read at arXiv cs.AI