Summary of Situated Ground Truths: Enhancing Bias-aware Ai by Situating Data Labels with Situannotate, By Delfina Sol Martinez Pandiani and Valentina Presutti
Situated Ground Truths: Enhancing Bias-Aware AI by Situating Data Labels with SituAnnotate
by Delfina Sol Martinez Pandiani, Valentina Presutti
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 This research paper introduces SituAnnotate, a novel ontology designed to address potential biases in supervised machine learning by grounding annotations within contextual and culturally-bound situations. The proposed approach enables structured and context-aware data annotation, representing situational context including annotator details, timing, location, and more. Built upon the Dolce Ultralight ontology, SituAnnotate provides a robust framework for knowledge representation, enabling AI systems to undergo training with explicit consideration of context and cultural bias. This research has implications for enhancing system interpretability, adaptability, and aligning AI models with various cultural contexts and viewpoints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to annotate data that helps prevent biases in AI training. It’s like labeling pictures or videos with words or descriptions, but this new approach also considers the context of who did the labeling, where they were, when they did it, and more. This means AI systems can learn from data in a way that is fairer and more inclusive. |
Keywords
» Artificial intelligence » Grounding » Machine learning » Supervised
