Trustworthy AI
The promise of AI is huge. AI systems have already achieved good enough performance to be in our streets and in our homes. However, they can be brittle and unfair. For society to reap the benefits of AI systems, society needs to be able to trust them. Inspired by decades of progress in trustworthy computing, we suggest what trustworthy properties would be desired of AI systems. By enumerating a set of new research questions, we explore one approach--formal verification--for ensuring trust in AI. Trustworthy AI ups the ante on both trustworthy computing and formal methods.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
How Fake News Affect Trust in the Output of a Machine Learning System for News Curation
People are increasingly consuming news curated by machine learning (ML) systems. Motivated by studies on algorithmic bias, this paper explores which recommendations of an algorithmic news curation system users trust and …
BIG-bench Machine LearningEvolution of trust in structured populations
The trust game, derived from an economics experiment, has recently attracted interest in the field of evolutionary dynamics. In a recent version of the evolutionary trust game, players adopt one of three strategies: inve…
Trustworthy Representation Learning Across Domains
As AI systems have obtained significant performance to be deployed widely in our daily live and human society, people both enjoy the benefits brought by these technologies and suffer many social issues induced by these s…
FairnessRepresentation LearningA Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability
Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push the capabilities of AI to the network edge for real-time, efficient and secure intelligent decision-making and computation. H…
Decision MakingEdge-computingTowards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment
Deep Neural Networks have become the dominant solution for Autonomous Driving perception, but their opacity conflicts with emerging Trustworthy AI guidelines and complicates safety assurance, debugging, and human oversig…
Scene UnderstandingAutonomous Driving