Behavioral Use Licensing for Responsible AI
arXiv:2011.03116 · doi:10.1145/3531146.3533143
Abstract
With the growing reliance on artificial intelligence (AI) for many different applications, the sharing of code, data, and models is important to ensure the replicability and democratization of scientific knowledge. Many high-profile academic publishing venues expect code and models to be submitted and released with papers. Furthermore, developers often want to release these assets to encourage development of technology that leverages their frameworks and services. A number of organizations have expressed concerns about the inappropriate or irresponsible use of AI and have proposed ethical guidelines around the application of such systems. While such guidelines can help set norms and shape policy, they are not easily enforceable. In this paper, we advocate the use of licensing to enable legally enforceable behavioral use conditions on software and code and provide several case studies that demonstrate the feasibility of behavioral use licensing. We envision how licensing may be implemented in accordance with existing responsible AI guidelines.
arXiv commentPaper published at ACM FAccT 2022
References in corpus (13)
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- Artificial Intelligence: the global landscape of ethics guidelines
- Language Models are Few-Shot Learners
- Model Cards for Model Reporting
- Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
- Tacotron: Towards End-to-End Speech Synthesis
- Adversarial Video Generation on Complex Datasets
- Multi-level Attention Model for Weakly Supervised Audio Classification
- LiFT: A Scalable Framework for Measuring Fairness in ML Applications
- It's Time to Do Something: Mitigating the Negative Impacts of Computing Through a Change to the Peer Review Process
- Towards Standardization of Data Licenses: The Montreal Data License
- Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies
Cited by in corpus (8)
- Auditing large language models: a three-layered approach
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- Stronger Together: on the Articulation of Ethical Charters, Legal Tools, and Technical Documentation in ML
- The "Who", "What", and "How" of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Tools
- Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp
- ML-Asset Management: Curation, Discovery, and Utilization
- The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware)