Page History

...

• Role of AI in de-identification - demand for data, opportunities, threats
  • Google has a de-id tool
  • Amazon Comprehension
  • Identifying images at risk–which images are likely to contain burned in information than others?
  • Problem with scalability in terms of building the ruleset. Better to identify selectively.
  • Barcodes, pacemaker serial numbers, implanted devices
  • There is the potential of identifying objects but not the raw data.
  • Action: Describe the steps involved in imaging and the evolution of data in different levels of processing
    Case-based data
  • Is raw data in our purview?
  • Raw data is often in proprietary format and can lack a header.
  • Post-processed data like 3D reconstructions
  • What is the harm of reidentification? High-resolution 3D image of the face
  • Penetration testers that applies to de-ID
  • How to evaluate the success of de-facing?
  • Newman, L. H. (2016). AI Can Recognize Your Face Even If You’re Pixelated. Wired. https://www.wired.com/2016/09/machine-learning-can-identify-pixelated-faces-researchers-show/
  • When is it okay to release information that you know is identifiable? Example of boy in NYT.
  • Sometimes reidentification does not provide any new data.
  • What do you now know that you didn't know before?
  • Expectations of doing better deidentification and the threats of better reidentification. What can we do now and what in the future with AI?
  • Do you expect that one day a machine will replace your manual deidentification process? Can a robot replace human review?
  • Can you accept the risk of AI/machines/code? Get to the level of risk that is tolerable.
  • Main topic for the next call: the need for human QC.
  • When will you stop using humans or a targeted subset?
  • What would increase your comfort level to help you stop using human QC.