Ensure sensor inputs for multiple combined sensors – don’t lead to bias or anomalies, while creating transparency for users/non-users.

• Minimizing data & model issues from adding further sensors and combining signals.

•  minimize protocols of passive sensor collection (e.g. using event-based sensors instead of active-capture).

• Activation assurance for device-specific input.

• Calibration benchmark transparency.

• User notification of sensor-capture status

• Sensor-variable synchronicity (e.g. variable spectrum capture, event-based visual sequencing, etc.)

1. For regulators / policy makers 

   1. System explainability is important to ensure compliance, and guard against discriminatory systems. Sensors calibration will only become more important as tech like driver less vehicles come on to the market.

      1. Mandating performance benchmarking

         1. Propublica reporting on racially-discriminatory algorithms  

      2. Data retention for incident response

   2. Oversight of data representatives

   3. Further reading

      1. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86-92 https://arxiv.org/abs/1803.09010

      2. City of Toronto. Digital Infrastructure Strategic Framework City Of Toronto March 2022 https://www.toronto.ca/wp-content/uploads/2022/03/9728-DISFAcc2.pdf