Love this!!! To inspect the concept of neutrality as a software like process driven institution is a very illuminating approach.
I would add that there is a very relevant discussion in the AI sphere which is profoundly connected to the points you are making: the existence of bias in AI models.
To identify and measure bias is in a sense to identify and measure lack of neutrality, so it follows that, to define bias, one must first be very rigorous on the definition of neutrality.
This can seem simple for some of the more pedestrian AI tasks, but can become increasingly sophisticated as we introduce AI as an essential piece in workflows and institutions.
AI algorithms can be heavily biased, datasets can be biased and even data structures can be biased.
I feel this is a topic which you can further explore in the future. Thank you for this!
Love this!!! To inspect the concept of neutrality as a software like process driven institution is a very illuminating approach.
I would add that there is a very relevant discussion in the AI sphere which is profoundly connected to the points you are making: the existence of bias in AI models.
To identify and measure bias is in a sense to identify and measure lack of neutrality, so it follows that, to define bias, one must first be very rigorous on the definition of neutrality.
This can seem simple for some of the more pedestrian AI tasks, but can become increasingly sophisticated as we introduce AI as an essential piece in workflows and institutions.
AI algorithms can be heavily biased, datasets can be biased and even data structures can be biased.
I feel this is a topic which you can further explore in the future. Thank you for this!