Eternal Sunshine of the Mechanical Mind: The Irreconcilability of Machine Learning and the Right to be Forgotten
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913258522279936 |
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| author | Manab, Meem Arafat |
| author_facet | Manab, Meem Arafat |
| contents | As we keep rapidly advancing toward an era where artificial intelligence is a constant and normative experience for most of us, we must also be aware of what this vision and this progress entail. By first approximating neural connections and activities in computer circuits and then creating more and more sophisticated versions of this crude approximation, we are now facing an age to come where modern deep learning-based artificial intelligence systems can rightly be called thinking machines, and they are sometimes even lauded for their emergent behavior and black-box approaches. But as we create more powerful electronic brains, with billions of neural connections and parameters, can we guarantee that these mammoths built of artificial neurons will be able to forget the data that we store in them? If they are at some level like a brain, can the right to be forgotten still be protected while dealing with these AIs? The essential gap between machine learning and the RTBF is explored in this article, with a premonition of far-reaching conclusions if the gap is not bridged or reconciled any time soon. The core argument is that deep learning models, due to their structure and size, cannot be expected to forget or delete a data as it would be expected from a tabular database, and they should be treated more like a mechanical brain, albeit still in development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05592 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Eternal Sunshine of the Mechanical Mind: The Irreconcilability of Machine Learning and the Right to be Forgotten Manab, Meem Arafat General Literature Artificial Intelligence 68P27 K.4.1; K.5.2; I.2.0 As we keep rapidly advancing toward an era where artificial intelligence is a constant and normative experience for most of us, we must also be aware of what this vision and this progress entail. By first approximating neural connections and activities in computer circuits and then creating more and more sophisticated versions of this crude approximation, we are now facing an age to come where modern deep learning-based artificial intelligence systems can rightly be called thinking machines, and they are sometimes even lauded for their emergent behavior and black-box approaches. But as we create more powerful electronic brains, with billions of neural connections and parameters, can we guarantee that these mammoths built of artificial neurons will be able to forget the data that we store in them? If they are at some level like a brain, can the right to be forgotten still be protected while dealing with these AIs? The essential gap between machine learning and the RTBF is explored in this article, with a premonition of far-reaching conclusions if the gap is not bridged or reconciled any time soon. The core argument is that deep learning models, due to their structure and size, cannot be expected to forget or delete a data as it would be expected from a tabular database, and they should be treated more like a mechanical brain, albeit still in development. |
| title | Eternal Sunshine of the Mechanical Mind: The Irreconcilability of Machine Learning and the Right to be Forgotten |
| topic | General Literature Artificial Intelligence 68P27 K.4.1; K.5.2; I.2.0 |
| url | https://arxiv.org/abs/2403.05592 |