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Black boxes

Last updated April 13, 2022

See also: paperclip optimizer

Scientific and technical work is made invisible by its own success. When a machine runs efficiently, one need only to focus on its inputs and outputs and not on its internal complexity. Thus, paradoxically, the more science and technology succeed, the more opaque and obscure they become

In the context of systems, actors are anyone, human or nonhuman, who engages in intentional action shaped by internalized expectations of how it will be perceived

If we start to disect the black box and understand that it

Then this is called “opening the black box” or “infrastructural inversion” for larger scale infrastructures

Jim Johnson: building and rebuilding walls everytime you use it is a waste, thats why we have doors as infrastructure that saves a lot of this repetitive work.

# Computational Reliabilism (CR)

Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI by Juan Manuel Durán, Karin Rolanda Jongsma

On trust in black box algorithmic decision making systems

Black boxes are algorithms that humans cannot survey: they are epistemically opaque systems that no human or group of humans can closely examine in order to determine its inner states. Physicians have a hard time offering accounts of how the algorithm came to its recommendation or diagnosis

Terms:

Claim: transparency will not provide solutions to opacity, and therefore having more transparent algorithms is not a guarantee for better explanations, predictions, and overall justification of our trust in the results of an algorithm

Computational reliabilism (CR)

Responsibility gaps

Counterpoints raised:


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