Situated Ground Truths: Enhancing Bias-Aware AI by Situating Data Labels with SituAnnotate

TL;DR

This paper presents SituAnnotate, an ontology for context-aware data annotation that aims to reduce bias in AI training by embedding situational and cultural context into labels.

Contribution

It introduces a novel ontology, SituAnnotate, for structured, context-rich data annotation to address bias and improve AI interpretability and cultural adaptability.

Findings

Enables context-aware data annotation for bias reduction.

Aligns with Dolce Ultralight ontology for consistency.

Supports training AI with explicit situational context.

Abstract

In the contemporary world of AI and data-driven applications, supervised machines often derive their understanding, which they mimic and reproduce, through annotations--typically conveyed in the form of words or labels. However, such annotations are often divorced from or lack contextual information, and as such hold the potential to inadvertently introduce biases when subsequently used for training. This paper introduces SituAnnotate, a novel ontology explicitly crafted for 'situated grounding,' aiming to anchor the ground truth data employed in training AI systems within the contextual and culturally-bound situations from which those ground truths emerge. SituAnnotate offers an ontology-based approach to structured and context-aware data annotation, addressing potential bias issues associated with isolated annotations. Its representational power encompasses situational context, including annotator details, timing, location, remuneration schemes, annotation roles, and more, ensuring semantic richness. Aligned with the foundational Dolce Ultralight ontology, it provides a robust and consistent framework for knowledge representation. As a method to create, query, and compare label-based datasets, SituAnnotate empowers downstream AI systems to undergo training with explicit consideration of context and cultural bias, laying the groundwork for enhanced system interpretability and adaptability, and enabling AI models to align with a multitude of cultural contexts and viewpoints.