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Algorithms tell you how to vote. Algorithms can revoke your driver’s license and terminate your disability benefits. Algorithms predict crimes. Algorithms ensured you didn’t hear about #FreddieGray on Twitter. Algorithms are everywhere, and, to hear critics, they are trouble. What’s the problem? Critics allege that algorithms are opaque, automatic, emotionless, and impersonal, and that they separate decision-makers from the consequences of their actions. Algorithms cannot appreciate the context of structural discrimination, are trained on flawed datasets, and are ruining lives everywhere. There needs to be algorithmic accountability. Otherwise, who is to blame when a computational process suddenly deprives someone of his or her rights and livelihood?
But at heart, criticism of algorithmic decision-making makes an age-old argument about impersonal, automatic corporate and government bureaucracy. The machine like bureaucracy has simply become the machine. Instead of a quest for accountability, much of the rhetoric and discourse about algorithms amounts to a surrender—an unwillingness to fight the ideas and bureaucratic logic driving the algorithms that critics find so creepy and problematic. Algorithmic transparency and accountability can only be achieved if critics understand that transparency (no modifier is needed) is the issue. If the problem is that a bureaucratic system is impersonal, unaccountable, creepy, and has a flawed or biased decision criteria, then why fetishize and render mysterious the mere mechanical instrument of the system’s will ?
(Score: 3, Interesting) by krishnoid on Thursday May 21 2015, @06:56PM
"The computer/network is down, so:
An algorithm could be considered simply as a computer implementation of human/business processes, in which case they're both mechanisms of the same underlying issue -- which may be that front-line employees don't have the authority/flexibility/scoping/instruction to go off-script and operate against a division-wide understanding of a central goal.
If they could, they could use the processes and algorithms as advisories and guidelines towards that goal, and work with customers and other organizations in that light. Otherwise, meat or silicon implementing the rules wouldn't seem to make that much of a difference. They do seem to work well together when working against the customer, however.