Obscured data theft It copies the design of most constructions in the Western, industrialized world without consent and strives to mimic the most average one of those.
To become as “good” as they are in their current form, many generative AI systems have been trained on vast amounts of data that were not intended for this purpose, and whose owners and makers were not asked for consent. The mere publishing of an article or image on the web does not imply it can be used by anyone for anything. Generative AI tools appear to be getting away with ignoring copyright.
At the same time, there are many intricacies that law- and policymakers need to understand to do a good job of reasoning around infringements on rights or privileges. Images are for example not duplicated and stored, but rather used to feed a computational model, which is an argument often used by defenders to explain how generative AI is only “inspired” by the works of others and not “copying”. There are several ongoing legal cases challenging this view.
Bias & injustice By design, the hammer will most often just hit the thumb of Black, Brown, and underserved people.
One inherent property of AI is its ability to act as an accelerator of other harms. By being trained on large amounts of data (often unsupervised) – that inevitably contain biases, abandoned values, and prejudiced commentary – these will be reproduced in any output. It is likely that this will even happen unnoticeably (especially when not actively monitored) since many biases are subtle and embedded in common language. And at other times it will happen quite clearly, with bots spewing toxic, misogynistic, and racist content.
Because of systemic issues and the fact that bias becomes embedded in these tools, these biases will have dire consequences for people who are already disempowered. Scoring systems are often used by automated decision-making tools and these scores can for example affect job opportunities, welfare/housing eligibility, and judicial outcomes.
No, this does not mean that the hammer has a mind of its own. It means that the hammer is built in a way that has inherent biases which will tend to disfavor people who are already underserved and disenfranchised. AI tools will for example fail to reduce bias in recruitment and contribute to racist and sexist performance reviews.
It’s relevant to compare this topic with a touchless soap dispenser that won’t react to dark skin. It’s not the fault of the user that it does not react, it’s the fault of the manufacturer and how it was built. It doesn’t “act that way” because it has a mind, but because the manufacturing and design process was unmindful. You can’t tell the user to educate themself about using it to make it work better. See also Twitter’s image cropping algorithm, Google’s “inability” to find gorillas, webcams unable to recognize dark-skinned faces, and I’m sure there are many more examples that have been shared with me over the years.
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