Publications

Is a Good Description Worth a Thousand Pictures? Reducing Multimodal Alignment to Text-Based, Unimodal Alignment
Amin Memarian
Touraj Laleh
Irina Rish
Ardavan S. Nobandegani
Generative AI systems (ChatGPT, Llama, etc.) are increasingly adopted across a range of high-stake domains, including healthcare and crimina… (see more)l justice system. This rapid adoption indeed raises moral and ethical concerns. The emerging field of AI alignment aims to make AI systems that respect human values. In this work, we focus on evaluating the ethics of multimodal AI systems involving both text and images --- a relatively under-explored area, as most alignment work is currently focused on language models. Specifically, here we investigate whether the multimodal alignment problem (i.e., the problem of aligning a multimodal system) could be effectively reduced to the (text-based) unimodal alignment problem, wherein a language model would make a moral judgment purely based on a description of an image. Focusing on GPT-4 and LLaVA as two prominent examples of multimodal systems, here we demonstrate, rather surprisingly, that this reduction can be achieved with a relatively small loss in moral judgment performance in the case of LLaVa, and virtually no loss in the case of GPT-4.
2024-06-16
ICML.cc/2024/Workshop/MFHAIA (poster)
openreview.net
openreview.net
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David M. Krueger
Lennart Heim
Markus Anderljung
AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (see more)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.
2024-06-16
ArXiv (preprint)
doi.org
arxiv.org
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David M. Krueger
Lennart Heim
Markus Anderljung
2024-06-16
ArXiv (preprint)
doi.org
arxiv.org
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David M. Krueger
Lennart Heim
Markus Anderljung
AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (see more)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.
2024-06-16
ArXiv (preprint)
doi.org
arxiv.org
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David M. Krueger
Lennart Heim
Markus Anderljung
2024-06-16
ArXiv (preprint)
doi.org
arxiv.org
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David M. Krueger
Lennart Heim
Markus Anderljung
2024-06-16
ArXiv (preprint)
doi.org
arxiv.org
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David M. Krueger
Lennart Heim
Markus Anderljung
AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (see more)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.
2024-06-16
ArXiv (preprint)
doi.org
arxiv.org
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David M. Krueger
Lennart Heim
Markus Anderljung
2024-06-16
ArXiv (preprint)
doi.org
arxiv.org
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David M. Krueger
Lennart Heim
Markus Anderljung
AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (see more)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.
2024-06-16
ArXiv (preprint)
doi.org
arxiv.org
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David M. Krueger
Lennart Heim
Markus Anderljung
AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (see more)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.
2024-06-16
ArXiv (preprint)
doi.org
arxiv.org
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David M. Krueger
Lennart Heim
Markus Anderljung
2024-06-16
ArXiv (preprint)
doi.org
arxiv.org
Improving Molecular Modeling with Geometric GNNs: an Empirical Study
Ali Ramlaoui
Theo Saulus
Basile Terver
Victor Schmidt
David Rolnick
Fragkiskos D. Malliaros
Alexandre AGM Duval
2024-06-16
ICML.cc/2024/Workshop/ML4LMS (poster)
doi.org
openreview.net