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The National Health Service (NHS) wrote down several principles in a document Code of conduct for data-driven health and care technology (updated 18 July 2019). I am concerned with principle 7.

Show what type of algorithm is being developed or deployed, the ethical examination of how the data is used, how its performance will be validated and how it will be integrated into health and care provision

Demonstrate the learning methodology of the algorithm being built. Aim to show in a clear and transparent way how outcomes are validated.

But how exactly can outcomes be shown in a clear and transparent way how outcomes are validated?

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The current version of the document Code of conduct for data-driven health and care technology provides more details about all principles, including principle number 7, which I will quote here.

Consider how the introduction of AI will change relationships in health and care provision, and the implications of these changes for responsibility and liability. Use current best practice on how to explain algorithms to those taking actions based on their outputs.

When building an algorithm, be it a stand-alone product or integrated within a system, show it clearly and be transparent of the learning methodology (if any) that the algorithm is using. Undertake ethical examination of data use specific to this use-case. Achieving transparency of algorithms that have a higher potential for harm or unintended decision-making, can ensure the rights of the data subject as set out in the Data Protection Act 2018 are met, to build trust in users and enable better adoption and uptake.

Work collaboratively with partners, specify the context for the algorithm, specify potential alternative contexts and be transparent on whether the model is based on active, supervised or unsupervised learning. Show in a clear and transparent specification:

  • the functionality of the algorithm
  • the strengths and limitations of the algorithm (as far as they are known)
  • its learning methodology
  • whether it is ready for deployment or still in training
  • how the decision has been made on the acceptable use of the algorithm in the context it is being used (for example, is there a committee, evidence or equivalent that has contributed to this decision?)
  • the potential resource implications

This specification and transparency in development will build trust in incorporating machine-led decision-making into clinical care.

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