The new wave of 'foundation models'-general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)-represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection mechanism for the content it generates, as a condition of its public release. The detection mechanism should be made publicly available in a tool that allows users to query, for an arbitrary item of content, whether the item was generated (wholly or partly) by the model. In this paper, we argue that this requirement is technically feasible and would play an important role in reducing certain risks from new AI models in many domains. We also outline a number of options for the tool's design, and summarize a number of points where further input from policymakers and researchers would be required.

Generative AI models should include detection mechanisms as a condition for public release

Pedreschi, D;
2023-01-01

Abstract

The new wave of 'foundation models'-general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)-represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection mechanism for the content it generates, as a condition of its public release. The detection mechanism should be made publicly available in a tool that allows users to query, for an arbitrary item of content, whether the item was generated (wholly or partly) by the model. In this paper, we argue that this requirement is technically feasible and would play an important role in reducing certain risks from new AI models in many domains. We also outline a number of options for the tool's design, and summarize a number of points where further input from policymakers and researchers would be required.
2023
Knott, A; Pedreschi, D; Chatila, R; Chakraborti, T; Leavy, S; Baeza-Yates, R; Eyers, D; Trotman, A; Teal, Pd; Biecek, P; Russell, S; Bengio, Y
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1215409
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