Bericht

Using market design to improve red teaming of generative AI models

With the final approval of the EU's Artificial Intelligence Act (AI Act), it is now clear that general-purpose AI (GPAI) models with systemic risk will need to undergo adversarial testing. This provision is a response to the emergence of "generative AI" models, which are currently the most notable form of GPAI models gen- erating rich-form content such as text, images, and video. Adversarial testing involves repeatedly interact- ing with a model to try to lead it to exhibit unwanted behaviour. However, the specific implementation of such testing for GPAI models with systemic risk has not been clearly spelled out in the AI Act. Instead, the legislation only refers to codes of practice and harmonised standards which are soon to be developed. In this policy brief, which is based on research funded by the Baden-Württemberg Foundation, we propose that these codes and standards should reflect that an effective adversarial testing regime requires testing by independent third parties, a well-defined goal, clear roles with proper incentive and coordination schemes for all parties involved, and standardised reporting of the results. The market design approach is helpful for developing, testing and improving the underlying rules and the institutional setup of such adversarial testing regimes. We outline the design space for an extensive form of adversarial testing, called red team- ing, of generative AI models. This is intended to stimulate the discussion in preparation for the codes of practice, harmonised standards and potential additional provisions by governing bodies.

Language
Englisch

Bibliographic citation
Series: ZEW policy brief ; No. 06/2024

Classification
Wirtschaft

Event
Geistige Schöpfung
(who)
Rehse, Dominik
Valet, Sebastian
Walter, Johannes
Event
Veröffentlichung
(who)
ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung
(where)
Mannheim
(when)
2024

Last update
10.03.2025, 11:43 AM CET

Data provider

This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.

Object type

  • Bericht

Associated

  • Rehse, Dominik
  • Valet, Sebastian
  • Walter, Johannes
  • ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung

Time of origin

  • 2024

Other Objects (12)