AI is only as useful as the data used to train it. Training AI with dubious data leads to questionable decisions paid intel
highlighting the growing ethical risks in surveillance. The latter manifest themselves both in the form of unchecked capacity to monitor individuals beyond their ability to reasonably consent and in the controversial practice of assessing affect through algorithms.that more than 20 competing concepts of fairness relating to AI and machine learning had been published in the past decade.
And it’s a big one—it’s been a topic of serious consideration by the largest technology industry bodies for decades.plainly: “Extraordinary care should be taken to identify and mitigate potential risks in machine learning systems. A system for which future risks cannot be reliably predicted requires frequent reassessment of risk as the system evolves in use, or it should not be deployed.”
Start with the reality that bias itself can be a loaded term and may make people reflexively defensive, protesting that they are not personally biased or prejudiced against a particular group. But algorithms can reflect the unconscious assumptions and blind spots of their creators, thereby amplifying a lack of experience or a narrow worldview.For example, some facial recognition algorithms were trained on repositories of celebrity photographs.
A charge of bias is not a charge of malice. AI researchers and business users alike need to be ready to push past the urge to qualify or defend their efforts, and instead look at the facts of the matter.Industry is stepping up to the challenge, with interdisciplinary ethics panels, workshops and expert groups at every reputable machine learning event and ethical watchdogs in every conscientious AI laboratory.
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