Towards Verified Artificial Intelligence
By Sanjit A. Seshia, Dorsa Sadigh. 2016
211 citations recorded by the source corpus.
Verified artificial intelligence (AI) is the goal of designing AI-based systems that that have strong, ideally provable, assurances of correctness with respect to mathematically-specified requirements. This paper considers Verified AI from a formal methods perspective. We describe five challenges for achieving Verified AI, and five corresponding principles for addressing these challenges.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Software
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Tom Everitt
- Shane Legg
- Jan Leike
- Miljan Martic
- Victoria Krakovna
- Laurent Orseau
- Pedro A. Ortega
- Christopher Hahn
In Computational Theory and Mathematics
- Tom Everitt
- Jan Leike
- Shane Legg
- Miljan Martic
- Pedro A. Ortega
- Christopher Hahn
- Javier Alonso-Mora
- Daniela Rus
In Software
- Christopher Hahn
- Bernd Finkbeiner
- Krzysztof Czarnecki
- Markus Norman Rabe
- Rick Salay
- Frederik Schmitt
- Christian Kästner
Citations in the corpus
- AI Safety Gridworlds (2017, background): “, 2017), formal verification (Seshia et al., 2016; Huang et al., 2017b; Katz et al., 2017), scalable oversight and reward learning problems (Amodei et al.”
- Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262 (2018, background): “Some early proposals on doing formal verification of ML models can be found in [68].”
- Trustworthy AI: From Principles to Practices (2021, unknown): “Simulation [42, 50, 54, 103, 192, 205, 323, 324, 359] Formal Verification [164, 298, 335]”
- Software Engineering for AI-Based Systems: A Survey (2021, unknown): “Two papers [86, 183] present an overview of current challenges and practices in V&V of autonomous systems, in particular those based on DL.”
- Planning and Decision-Making for Autonomous Vehicles (2018, background): “(135) identified five challenges: modeling of the complex environment, modeling of the system, formal specification of the desired properties of the system, scalability, and formal quantification of the requirements for training data.”
- Teaching Temporal Logics to Neural Networks (2020, methodology): “Another area in the intersection of formal methods and machine learning is the verification of neural networks [41,40,43,12,18,8].”
- Will Distributed Computing Revolutionize Peace? The Emergence of Battlefield IoT (2018, unknown): “Recent work [37] describes challenges for applying”