Regulation is increasingly cited as the most important and pressing concern in machine learning. However, it is currently unknown how to implement this, and perhaps more importantly, how it would effect model performance alongside human collaboration if actually realized. In this paper, we attempt to answer these questions by building a regulatable large-language model (LLM), and then quantifying how the additional constraints involved affect (1) model performance, alongside (2) human collaboration. Our empirical results reveal that it is possible to force an LLM to use human-defined features in a transparent way, but a "regulation performance trade-off" previously not considered reveals itself in the form of a 7.34% classification performance drop. Surprisingly however, we show that despite this, such systems actually improve human task performance speed and appropriate confidence in a realistic deployment setting compared to no AI assistance, thus paving a way for fair, regulatable AI, which benefits users.

Who is responsible when AI harms someone? A California jury may soon have to decide. In December 2019, a person driving a Tesla with an artificial intelligence driving system killed two people in Gardena in an accident. The Tesla driver faces several years in prison. In light of this and other incidents, both the National Highway Transportation Safety Administration (NHTSA) and National Transportation Safety Board are investigating Tesla crashes, and NHTSA has recently broadened its probe to explore how drivers interact with Tesla systems.

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To see the'Sheet book' and'Table of associated records' go the bottom, Pag. 2, Pag. 3. ( For free online version, on the image). Who might face civil liability if autonomous vehicles (AVs) are hacked to steal data or inflict mayhem, injuries, and damage? How will the civil justice and insurance systems adjust to handle such claims? RAND researchers addressed these questions to help those in the automotive, technology, legal, and insurance industries prepare for the shifting roles and responsibilities that the era of AVs may bring. Using four scenarios (a ransomware attack, a hacked vehicle damaging government property, hacks on a connected roadway that cause damage, and theft of information through hacking of AVs), the authors explored the civil legal theories that may come into play when real-world damages result from AVs being hacked.

Self-driving-cars are notoriously difficult to test for safety. Hotz writes in an email, "It's not my code, I did not release it"--Comma.ai Inc. "released and maintains it." Most legal experts that spoke with IEEE Spectrum--and Hotz himself--believe that if you use the company's code and something goes wrong, then it isn't liable for damages. But Consumer Watchdog advocate John Simpson doesn't believe this is fair.