3. Data Ethics
Consent
Consent is no longer a one-off decision but an on-going issue to be carefully monitored. The mechanisms for withdrawing consent should be as easy as those for giving consent.
Data sharing
Strong ethical practices preclude sharing data without the consent but the effective use of data demands that it should be shared. Addressing the issues and mitigating risk associated with sharing data is vital.
Algorithmic bias
Data can often contain hidden biases, produced by systematic and repeatable errors in a computer system. These can create unreliable or unfair outcomes.
Download and use this tool:
https://theodi.org/article/data-ethics-canvas/
Bias in AI
Three of the latest gender-recognition AIs, from IBM Microsoft and Chinese company Megvii, could correctly identify a person’s gender from a photograph 99 per cent of the time, but only for white men. For dark-skinned women, accuracy dropped to just 35 per cent.
That increases the risk of false identification of women and minorities. Source: New Scientist
Bias in implementation
A company built a smartphone app that monitors for potholes in the road by passively collecting accelerometer data.
The first cities that deployed this technology to prioritize road maintenance saw wealthy communities receive the most attention.
They did not have the worst roads, they were thepeople with the most smartphones.
Source: New Scientist