Minimizing social bias strengthens societal bonds, promoting shared
understanding and better decision-making. We revisit the definition of bias by
discovering new bias types (e.g., societal status) in dynamic environments and
describe them relative to context, such as culture, region, time, and personal
background. Our framework includes eight hypotheses about bias and a minimizing
bias strategy for each assumption as well as five methods as proposed solutions
in LLM. The realization of the framework is yet to be completed.

在动态环境中重新审视偏见的定义，发现新的偏见类型（如社会地位），并描述它们与文化、地区、时间和个人背景等上下文的关系。我们的框架包括关于偏见的八个假设以及每个假设的减少偏见策略，以及作为解决方案的五种方法。该框架的实现尚未完成。

关于完整的偏见注释

A Note on Bias to Complete

Research in multi-agent teaming has increased substantially over recent
years, with knowledge-based systems to support teaming processes typically
focused on delivering functional (communicative) solutions for a team to act
meaningfully in response to direction. Enabling humans to effectively interact
and team with a swarm of autonomous cognitive agents is an open research
challenge in Human-Swarm Teaming research, partially due to the focus on
developing the enabling architectures to support these systems. Typically,
bi-directional transparency and shared semantic understanding between agents
has not prioritised a designed mechanism in Human-Swarm Teaming, potentially
limiting how a human and a swarm team can share understanding and
information\textemdash data through concepts and contexts\textemdash to achieve
a goal. To address this, we provide a formal knowledge representation design
that enables the swarm Artificial Intelligence to reason about its environment
and system, ultimately achieving a shared goal. We propose the Ontology for
Generalised Multi-Agent Teaming, Onto4MAT, to enable more effective teaming
between humans and teams through the biologically-inspired approach of
shepherding.

本篇研究提出了 Onto4MAT，这是一种形式化的知识表示设计，通过牧羊的生物学方法，促进了人类和团队之间更有效的协作。