The documentation practice for machine-learned (ML) models often falls short of established practices for traditional software, which impedes model accountability and inadvertently abets inappropriate or misuse of models. Recently, model cards, a proposal for model documentation, have attracted notable attention, but their impact on the actual practice is unclear. In this work, we systematically study the model documentation in the field and investigate how to encourage more responsible and accountable documentation practice. Our analysis of publicly available model cards reveals a substantial gap between the proposal and the practice. We then design a tool named DocML aiming to (1) nudge the data scientists to comply with the model cards proposal during the model development, especially the sections related to ethics, and (2) assess and manage the documentation quality. A lab study reveals the benefit of our tool towards long-term documentation quality and accountability.

Molecular imaging is a novel tool that has allowed noninvasive diagnostic imaging to transition from gross anatomical description to identification of specific tissue epitopes and observation of biological processes at the cellular level. Until recently, this technique was confined to the field of nuclear imaging; however, advances in nanotechnology have extended this research to include magnetic resonance (MR) imaging, positron emission tomography (PET), single photon emission computed tomography (SPECT), and ultrasound (US), among others. The application of nanotechnology to MR, SPECT, and US molecular imaging has generated several candidate contrast agents. We discuss the application of one multimodality platform, a targeted perfluorocarbon nanoparticle. Our results show that it is useful for noninvasive detection with all three imaging modalities and may additionally be used for local drug delivery.