Law Commons^™

How Can I Tell If My Algorithm Was Reasonable?, Karni A. Chagal-Feferkorn

Michigan Technology Law Review

Self-learning algorithms are gradually dominating more and more aspects of our lives. They do so by performing tasks and reaching decisions that were once reserved exclusively for human beings. And not only that—in certain contexts, their decision-making performance is shown to be superior to that of humans. However, as superior as they may be, self-learning algorithms (also referred to as artificial intelligence (AI) systems, “smart robots,” or “autonomous machines”) can still cause damage.

When determining the liability of a human tortfeasor causing damage, the applicable legal framework is generally that of negligence. To be found negligent, the tortfeasor must have …

From Automation To Autonomy: Legal And Ethical Responsibility Gaps In Artificial Intelligence Innovation, David Nersessian, Ruben Mancha

Michigan Technology Law Review

The increasing prominence of artificial intelligence (AI) systems in daily life and the evolving capacity of these systems to process data and act without human input raise important legal and ethical concerns. This article identifies three primary AI actors in the value chain (innovators, providers, and users) and three primary types of AI (automation, augmentation, and autonomy). It then considers responsibility in AI innovation from two perspectives: (i) strict liability claims arising out of the development, commercialization, and use of products with built-in AI capabilities (designated herein as “AI artifacts”); and (ii) an original research study on the ethical practices …