arXiv:2506.00175v5 Announce Type: replace Abstract: Modern AI systems are typically developed through multiple stages-pretraining, fine-tuning rounds, and subsequent adaptation or alignment, where each stage builds on the previous ones and updates the model in distinct ways. This raises a critical question of accountability: when a deployed model succeeds or fails, which stage is responsible, and to what extent? We pose the accountability attribution problem for tracing model behavior back to specific stages of the model development process. To address this challenge, we propose a general fram
The accelerating complexity and multi-stage development processes of modern AI necessitate a more rigorous framework for accountability, especially as AI systems are deployed in critical applications.
Establishing clear accountability for AI system behavior is crucial for regulatory compliance, ethical deployment, risk management, and fostering public trust in increasingly autonomous systems.
This research introduces a formal framework for attributing accountability to specific stages of AI model development, shifting the conversation from general responsibility to granular credit/blame assignment.
- · AI ethicists
- · Regulatory bodies
- · AI developers with robust MLOps
- · Users of AI systems
- · AI developers with opaque processes
- · Companies facing AI-related liabilities
- · Black-box AI systems
Individual development stages and teams within AI pipelines will bear more direct responsibility for model outcomes.
This granular accountability will drive the adoption of more transparent and auditable AI development methodologies and tooling.
New insurance products and legal precedents could emerge, specifically tailored to AI accountability and liability across development stages.
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Read at arXiv cs.LG