AIDRIN 2.0: A Framework to Assess Data Readiness for AI
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918069577711616 |
|---|---|
| author | Hiniduma, Kaveen Ryan, Dylan Byna, Suren Bez, Jean Luca Madduri, Ravi |
| author_facet | Hiniduma, Kaveen Ryan, Dylan Byna, Suren Bez, Jean Luca Madduri, Ravi |
| contents | AI Data Readiness Inspector (AIDRIN) is a framework to evaluate and improve data preparedness for AI applications. It addresses critical data readiness dimensions such as data quality, bias, fairness, and privacy. This paper details enhancements to AIDRIN by focusing on user interface improvements and integration with a privacy-preserving federated learning (PPFL) framework. By refining the UI and enabling smooth integration with decentralized AI pipelines, AIDRIN becomes more accessible and practical for users with varying technical expertise. Integrating with an existing PPFL framework ensures that data readiness and privacy are prioritized in federated learning environments. A case study involving a real-world dataset demonstrates AIDRIN's practical value in identifying data readiness issues that impact AI model performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18213 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AIDRIN 2.0: A Framework to Assess Data Readiness for AI Hiniduma, Kaveen Ryan, Dylan Byna, Suren Bez, Jean Luca Madduri, Ravi Computers and Society Artificial Intelligence AI Data Readiness Inspector (AIDRIN) is a framework to evaluate and improve data preparedness for AI applications. It addresses critical data readiness dimensions such as data quality, bias, fairness, and privacy. This paper details enhancements to AIDRIN by focusing on user interface improvements and integration with a privacy-preserving federated learning (PPFL) framework. By refining the UI and enabling smooth integration with decentralized AI pipelines, AIDRIN becomes more accessible and practical for users with varying technical expertise. Integrating with an existing PPFL framework ensures that data readiness and privacy are prioritized in federated learning environments. A case study involving a real-world dataset demonstrates AIDRIN's practical value in identifying data readiness issues that impact AI model performance. |
| title | AIDRIN 2.0: A Framework to Assess Data Readiness for AI |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2505.18213 |