Artificial Intelligence and Law

Abstract

We are surrounded by machines. From simple ones-AC motors and transformers-through radio receivers, TV sets, smartphones and personal computers, to sophisticated AI systems, such as self-driving cars, autonomous weapons and IBM's Watson. The advances in technology have reshaped the world we inhabit, including our social environment. When iPhone is the girl's best friend, our communication and decision-making is aided by complex algorithms, and various tasks so far reserved for human beings are carried out by robots, the contemporary societies are not what they used to be. Moreover, the technology is advancing at such a rapid pace that many ideas, such as companion and sex robots, which used to be a fodder for science fiction are fast becoming a reality. This is a profound challenge for any legal system. The law is there to regulate the actions of individuals so that they contribute to the functioning of large societies. It means that legal institutions should be designed in such a way as to embrace any changes and developments that reshape our communal practices. For this reason, technological progress has been a focus of lawyers' debates since the first industrial revolution. The great discoveries of the nineteenth and twentieth centuries-car, airplane, radio, TV, computer, the Internet-have not only influenced the existing legal institutions, but have also led to the establishment of entirely new branches of law. Arguably, however, they did not revamp the very foundations of their contemporary legal systems, but served as a means for regulating interactions between human beings. Technology has been considered only as a tool used by & Bipin Indurkhya

Genetics-driven Risk Predictions with Differentiable Mendelian Randomization

“…Optimization of the ELBO was achieved through gradient descent using the Adam optimizer, incorporating the reparameterization trick to enable backpropagation through the expectation term of the ELBO. This approach aligns with standard practices in variational inference methods that leverage gradient descent [39–41]. The learning rate for all experiments was fixed at 0.01, and we consistently applied gradient clipping with a norm bound of one while training for 1000 epochs.…”