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1.2: Intermediate

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    Copyright and intellectual property

    Closely related to truth and integrity are ideas of intellectual property and the legal concerns of copyright. Copyright issues have been particularly prevalent in AI image generation. AI image generators like Stable Diffusion, DALL-E 2, and Midjourney are trained on images “scraped” from the internet. These images are then broken down and run through the AI algorithms, so that they can later be reconstructed.

    This has resulted in artists’ “styles” being used in AI image generation without their permission. Many artists believe that this infringes their intellectual property rights, and is an ethical issue. An often-used counterargument is that all art is based on other artists’ work, and therefore the machine is simply replicating those processes. Class action lawsuits have already been filed against some AI image generators on behalf of artists.

    Large language models like ChatGPT also incorporate huge amounts of other writers’ work. Where writing is publicly available – such as out of copyright books or CC journalism and articles – it can be incorporated into the dataset. Even when writing is protected by copyright it can become part of the datasets. Prompting a language model to write something in the style of another author could be viewed in the same way as an image generator adopting another artist’s style.

    There are also question marks over who owns the copyright to materials produced by AIs such as image generators and language models.

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    Subject examples

    A painting of a city at nightDescription automatically generated with medium confidence

    I call this oneMelbourne skyline in the style of picasso and van gogh –ar 3:2 –q 2 –v 4but is it art? And is it legal? Via Midjourney.

    Privacy and security

    Privacy is a major concern in the development and use of AI systems. As these technologies become more sophisticated and integrated into our lives, there are increasing concerns about the collection and use of personal data, data breaches, and the lack of transparency in AI decision-making.

    One of the most prominent examples of these issues can be found in the use of facial recognition technology. This technology, which is used in a variety of applications such as security, surveillance, and marketing, has been criticised for its potential to violate individuals’ privacy and civil rights. For example, facial recognition systems have been known to have higher error rates for people with darker skin tones, and have been used to target and monitor marginalised communities as discussed earlier in “bias”.

    Another example of privacy concerns with AI systems is targeted advertising. AI-powered algorithms are used to analyse data on individuals’ online activities in order to deliver targeted ads. Whilst this may seem harmless, it raises concerns about data privacy, data breaches, and the use of personal data for commercial gain.

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    dramatic feature article head image collage of surveillance technologies. red, white, and black. in the style of an editorial header image. Techno. CCTV. Privacy and data breaches. Digital collage. --ar 3:2 --q 2 --v 4

    Every click and like goes towards powering AI surveillance. Image via Midjourney. Prompt in alt text.

    Data collection and “datafication”

    The phrase “data is the new oil” crops up everywhere when you start researching AI. As I wrote about earlier in “bias”, Artificial Intelligence is powered by huge amounts of data. The oil analogy suggests both data as fuel, but also the costly, dangerous, and extractive process of data-mining. In the constant quest for more and more data, the companies that develop AI systems sometimes revert to unethical practices.

    Datafication” is a term used for turning all parts of our life into a data point to be fed into an AI algorithm. As per the privacy discussion above, this should raise some serious concerns. From location data to health data, shopping habits, likes, clicks, and views, almost every interaction we have with technology is fed into an algorithm somewhere.

    As we become commodities, we open ourselves up to exploitation. One major ethical concern with “datafication” is that fact that the users become the products, and that the free-labour of the users is used to generate capital for the platform owners.

    “Big Data” also contributes to many of the issues we have described so far, including bias and discrimination. Any data collected by the devices we wear and use or the platforms we subscribe to ultimately becomes part of the algorithm’s “world view”. Unfortunately, because not everyone in the world has access to these technologies, that worldview is by definition missing some very important data.

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    1.2: Intermediate is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by LibreTexts.

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