Forbidden knowledge in machine learning -- Reflections on the limits of research and publication

Abstract

Certain research strands can yield "forbidden knowledge". This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance with regard to generative video or text synthesis, personality analysis, behavior manipulation, software vulnerability detection and the like. Up to now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline norms that can help to decide whether and when the dissemination of such information should be prevented. It proposes review parameters for the machine learning community to establish an ethical framework on how to deal with forbidden knowledge and dual-use applications.

References in corpus (14)

• Progressive Growing of GANs for Improved Quality, Stability, and Variation
• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
• Release Strategies and the Social Impacts of Language Models
• Emergence of Grounded Compositional Language in Multi-Agent Populations
• In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
• DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
• HeadOn: Real-time Reenactment of Human Portrait Videos
• Defending Against Neural Fake News
• It's Time to Do Something: Mitigating the Negative Impacts of Computing Through a Change to the Peer Review Process
• A Selective Overview of Deep Learning
• Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research
• A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images
• TequilaGAN: How to easily identify GAN samples
• Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learning

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• GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters