Ahmed Adnan

Ahmed Adnan, Mushfiqur Rahman, Antu Saha et al.

We advocate for AI/ML issue resolution frameworks tailored to maintenance workflows and the nature of modern AI/ML systems. Existing issue resolution frameworks largely emerged for traditional software maintenance practices and do not explicitly account for characteristics common in AI/ML systems, such as stochastic behavior, experimentation-driven workflows, and heterogeneous artifacts beyond source code. To identify the unique characteristics of issue resolution in AI/ML systems and motivate the need for tailored frameworks, we conducted a qualitative study of issue resolution workflows documented in 100 issue reports and pull requests across four widely used AI/ML systems: TensorFlow, scikit-learn, MLflow, and AutoGPT. Our findings suggest that issue resolution in AI/ML systems involves: recurring AI/ML-related activities that span multiple resolution stages; iterative experimentation and adaptive verification; and coordinated changes across artifacts such as datasets, prompts, and model configurations. We also observed challenges related to reproducibility, nondeterministic behavior, and artifact coordination. Building on these findings, we present a vision for AI/ML issue resolution frameworks and discuss research directions and tooling support needed to realize this vision.

Tanusree Sharma, Anish Krishnagiri, Lili Dudas et al.

As generative voice models are rapidly advancing in both capabilities and public utilization, the unconsented collection, reuse, and synthesis of voice data are introducing new classes of privacy, security and governance risk that are poorly captured by existing, largely uniform threat models. To fill the gap, we present V.O.I.C.E, a taxonomy of voice generation risk grounded in a multi-source threat modeling effort with 569 incidents from major AI incident database, FTC and Internet Crime Complaint Center (IC3); 1067 direct incident reports from U.S. based participants across diverse groups (including voice actors, internet personalities, political personnel, and general public); and 2,221 Reddit discussions. Grounded in real-world data, our taxonomy explicitly models how risk emerges, interact with contextual factors such as degree of exposure, social visibility, and the availability of legal protections for various affected groups.