Am I allowed to subvert machine learning for fun and profit?

Posted on in Presentations

When your machine learning (ML) system is attacked, what legal remedies can you seek? Has your terms of service even been updated to account for such attacks? If you’re an attacker, what risks are you assuming? This panel’s goal will be to convey the definitional challenges that attacks on ML systems pose in cybercrime, copyright and product liability law, and their impact on organizations and society.

Pre-Requisites: The panel is aimed at policy and legal professionals with little to no understanding of Machine learning. All the pre-reqs are self contained. At the beginning of each section, we go through the attack - and then dissect it.

Nicholas Carlini


Research Scientist, Google Brain

Betsy Cooper


Director, Aspen Tech Policy Hub, Aspen Institute

Cristin Goodwin


Assistant General Counsel, Microsoft

Ram Shankar Siva Kumar


Data Cowboy, Microsoft; Harvard

Policy & Government Hackers & Threats Machine Learning & Artificial Intelligence

risk & vulnerability assessment hackers & threats government regulations governance risk & compliance artificial intelligence & machine learning



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