R0029/2026-03-27/Q001/SRC02
IBM Research — AI Attribution Toolkit
Source
| Field |
Value |
| Title |
A new tool for crediting AI's contributions |
| Publisher |
IBM Research Blog |
| Author(s) |
IBM Research (Jessica He, Justin Weisz, Stephanie Houde) |
| Date |
May 2025 |
| URL |
https://research.ibm.com/blog/AI-attribution-toolkit |
| Type |
Industry research tool / blog post |
Summary
| Dimension |
Rating |
| Reliability |
Medium-High |
| Relevance |
High |
| Bias: Missing data |
Low risk |
| Bias: Measurement |
N/A |
| Bias: Selective reporting |
Some concerns |
| Bias: Randomization |
N/A — not an RCT |
| Bias: Protocol deviation |
N/A — not an RCT |
| Bias: COI/Funding |
Some concerns |
Rationale
| Dimension |
Rationale |
| Reliability |
Blog post from IBM Research describing a publicly available toolkit (aiattribution.github.io). Backed by peer-reviewed research (CHI 2025). Not itself peer-reviewed as a blog post. |
| Relevance |
Directly demonstrates a working attribution framework — one of the few that has been built and released |
| Bias flags |
Selective reporting: IBM may emphasize the toolkit's strengths over limitations. COI: IBM has commercial interest in positioning itself as a leader in responsible AI |
| Evidence ID |
Summary |
| SRC02-E01 |
IBM AI Attribution Toolkit structure and self-described experimental status |