AI, Climate, and Transparency: Operationalizing and Improving the AI Act
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914947761438720 |
|---|---|
| author | Alder, Nicolas Ebert, Kai Herbrich, Ralf Hacker, Philipp |
| author_facet | Alder, Nicolas Ebert, Kai Herbrich, Ralf Hacker, Philipp |
| contents | This paper critically examines the AI Act's provisions on climate-related transparency, highlighting significant gaps and challenges in its implementation. We identify key shortcomings, including the exclusion of energy consumption during AI inference, the lack of coverage for indirect greenhouse gas emissions from AI applications, and the lack of standard reporting methodology. The paper proposes a novel interpretation to bring inference-related energy use back within the Act's scope and advocates for public access to climate-related disclosures to foster market accountability and public scrutiny. Cumulative server level energy reporting is recommended as the most suitable method. We also suggests broader policy changes, including sustainability risk assessments and renewable energy targets, to better address AI's environmental impact. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07471 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI, Climate, and Transparency: Operationalizing and Improving the AI Act Alder, Nicolas Ebert, Kai Herbrich, Ralf Hacker, Philipp Computers and Society Artificial Intelligence This paper critically examines the AI Act's provisions on climate-related transparency, highlighting significant gaps and challenges in its implementation. We identify key shortcomings, including the exclusion of energy consumption during AI inference, the lack of coverage for indirect greenhouse gas emissions from AI applications, and the lack of standard reporting methodology. The paper proposes a novel interpretation to bring inference-related energy use back within the Act's scope and advocates for public access to climate-related disclosures to foster market accountability and public scrutiny. Cumulative server level energy reporting is recommended as the most suitable method. We also suggests broader policy changes, including sustainability risk assessments and renewable energy targets, to better address AI's environmental impact. |
| title | AI, Climate, and Transparency: Operationalizing and Improving the AI Act |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2409.07471 |