Computational AI in Medicine | 2023-today

Autonomous experimentation systems – also known as Autonomous Labs, Self-driving Labs, or Materials Acceleration Platforms – are engineered platforms capable of running a high amount of scientific experiments autonomously, assisted by computational tools designed and programmed to do it with a high level of precision, accuracy, and resilience. Autonomous Labs are associated with the rapid progress of algorithm efficiency because they enable computation exploration of chemical space to design new materials. The Trust in Autonomous Labs (TAL) Project aims to explore the implications of autonomous labs in knowledge and society. We will examine how trust is constructed among scientists working in chemical and material science research, bioengineering, bottom-up synthetic biology, and precision oncology. TAL produces empirical evidence of (1) discourses, practices, and ethical principles guiding the knowledge production in autonomous labs and (2) explores pathways experts adopt to build broader societal trust in autonomous labs between researchers from multiple fields, technology developers, regulators, policy-makers, and society.