The Defend Trade Secrets Act Whistleblower Immunity Provision: A Legislative History

The Defend Trade Secrets Act of 2016 ( DTSA ) was the product of a multi-year effort to federalize trade secret protection. In the final stages of drafting the DTSA, Senators Grassley and Leahy introduced an important new element: immunity for whistleblowers who share confidential information in the course of reporting suspected illegal activity to law enforecement or when filing a lawsuit, provided they do so under seal. The meaning and scope of this provision are of vital importance to enforcing health, safety, civil rights, financial market, consumer, and environmental protections and deterring fraud against the government, shareholders, and the public. This article explains how the whistleblower immunity provision was formulated and offers insights into its proper interpretation

Active Inverse Reward Design

Designers of AI agents often iterate on the reward function in a trial-and-error process until they get the desired behavior, but this only guarantees good behavior in the training environment. We propose structuring this process as a series of queries asking the user to compare between different reward functions. Thus we can actively select queries for maximum informativeness about the true reward. In contrast to approaches asking the designer for optimal behavior, this allows us to gather additional information by eliciting preferences between suboptimal behaviors. After each query, we need to update the posterior over the true reward function from observing the proxy reward function chosen by the designer. The recently proposed Inverse Reward Design (IRD) enables this. Our approach substantially outperforms IRD in test environments. In particular, it can query the designer about interpretable, linear reward functions and still infer non-linear ones

The Assistive Multi-Armed Bandit

Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they want. In this work, we introduce the assistive multi-armed bandit, where a robot assists a human playing a bandit task to maximize cumulative reward. In this problem, the human does not know the reward function but can learn it through the rewards received from arm pulls; the robot only observes which arms the human pulls but not the reward associated with each pull. We offer sufficient and necessary conditions for successfully assisting the human in this framework. Surprisingly, better human performance in isolation does not necessarily lead to better performance when assisted by the robot: a human policy can do better by effectively communicating its observed rewards to the robot. We conduct proof-of-concept experiments that support these results. We see this work as contributing towards a theory behind algorithms for human-robot interaction.Comment: Accepted to HRI 201

Misconstruing Whistleblower Immunity Under the Defend Trade Secrets Act

In crafting the Defend Trade Secrets Act of 2016 (DTSA), Congress went beyond the federalization of state trade secret protection to tackle a broader social justice problem: the misuse of nondisclosure agreements (NDAs) to discourage reporting of illegal activity in a variety of areas. The past few decades have witnessed devastating government contracting abuses, regulatory violations, and deceptive financial schemes that have hurt the public and cost taxpayers and investors billions of dollars. Congress recognized that immunizing whistleblowers from the cost and risk of trade secret liability for providing information to the Government could spur law enforcement. But could this goal be accomplished without jeopardizing legitimate trade secret protection