Trust in Autonomous Labs

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 to the context of rapid progress of algorithm efficiency, since it enabled 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 focus on the examination of how trust is being constructed among scientists working in convergence-driven research environments of chemical and materials sciences research, as bioengineering, nanomedicine, drug discovery and precision oncology.

The study

(1) documents empirical evidence of discourses, practices and ethical principles guiding the knowledge production on autonomous labs 

(2) explore pathways adopted by experts to build societal trust in autonomous labs between researchers from multiple fields, technology developers, regulators, policy-makers and society.

The TAL Project analyses implications of autonomous labs in

If you are a scientist working with autonomous experimentation systems and is interested to collaborate as partner in our study, please send an e-mail to Renan da Silva at renan.leonel@hest.ethz.ch or Cristian Capotescu at cfc2149@columbia.edu.