Important to keep in mind that #AI is made by humans. Our flaws and biases get encoded and amplified. We need the right diversity of people at the table to design and implement #ArtificialIntelliegence and #ML. #humancentereddesign for all of humanity. #design
https://twitter.com/marcusborba/status/1357116081916309511
Who determines definitions, labels of #data cut offs? Too often we get all nerdy on which variables & factors in the model. But are we even using the right data? #tech #technology types might consider reading Maya Angelou & Toni Morrison and others to think outside the box
#AI
#AI
Example: many people are interested in #SDoH #socialdeterminantsofhealth & #data for it.
Who has existing large #databases?
Banks
Banks are traditionally risk averse, block/exclude those who are labeled high risk
So can their data improve health equity?
reuters.com/article/us-usa…
Who has existing large #databases?
Banks
Banks are traditionally risk averse, block/exclude those who are labeled high risk
So can their data improve health equity?
reuters.com/article/us-usa…
Who created the #database & for what reason? Who chose domains & fields, defined them. What are the priorities, values, concept of risk, risk aversion, & ultimate goals of decision makers, therefore of the databases created? Do they align with your desired outcome/objective? #AI
In your #AI #algorithm #design & application, are you assessing for unintended consequences or who is excluded. Is there a measure of #quality or #safety? Are you so evangelist that you are not enough of a skeptic or self critic? Does the algorithm transfer well across settings?
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