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March 20, 2021


Aaron Brian Silverman

Irving, I find this interesting, especially in the area of trust, as you mention.

Could I suggest one more criteria which you have written about in the past, which is diversity? Recommendation engines and algorithms need to be developed by a diverse group of developers and data scientists, and the data collected needs to be from a diverse population to help ensure the recommendations will be valuable to all people.

James Drogan

Diversity implies a broad view. This seems to me act against the intent of recommendation engines.

The more precise the aim the greater is the likelihood the target will be hit.



The hyper-personalisation means that the recommendation engines will continue to recommend more of the same kind. This is the recipe for us to go into a bubble of sorts in all the areas where we follow the recommendation engines. It feels like we are able to discover new stuff due to the recommendations but in fact, these are not random but along an axis that we are already familiar with.

This is a problem because we are social animals and we live in a society with other humans. We need to be able to gather and discover new stuff to expand our thinking and the inputs (what we consume) play a significant role in the outputs (what we create). So, I believe that as a consumer, we need to consciously build in diversity in our inputs in order for us to be able to create better and make it easier and more interesting to live together.

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