If we continue to allow AI systems to use human generated content...
https://scholarlycommons.law.wlu.edu/crsj/vol30/iss2/4/
Slavery.AI
The artificial intelligence market is swarming. Supercharged start-ups, global tech giants, and increasingly algorithmic governments target diverse use cases with new and stunningly innovative AI applications coming online every day. Where people are the computational subjects of those algorithmic machinations, however, there is no law, present or effective, to protect them against great and propagating harms. Consequently, people become data production units, the commoditized of the Data Industrial Complex and unfree, unpaid inputs to AI production.
This Article shares a new and provocative vision. It theorizes that unregulated AI systems and uses are giving rise to an emergent form of modern slavery: Slavery.AI. The Article examines the three structural systems of power that were responsible for historical chattel slavery and are at work today in Slavery.AI. Against these interdigitating power structures, the evolution of two legal concepts have brought forth, respectively, people-as-data-as-property and, ultimately, as inputs to AI production, and modern slavery in all its hideous permutations. At the confluence of these power systems and trends, Slavery.AI is emerging, as defined, theorized, and exemplified here. The Article crafts a crucible in which to test its theory of Slavery.AI against the universal characteristics of systems of slavery and demonstrates how those characteristics sounding in property and in the abuse of power through cooptations of the rule of law are firmly entrenched or on their way to being so. This illustrated proof of concept holds. It also reveals that there may be yet be opportunities for responsible leaders to save freedom and to emancipate people from Slavery.AI.
From sketch to AI “photo.”
Forensic Sketch to Real Image
In the realm of forensic investigations, the reliance on hand-drawn sketches or verbal descriptions to identify suspects or victims is well-established. However, these traditional methods often present challenges due to their inherent subjectivity and potential inaccuracies. Recognizing the critical importance of enhancing the accuracy and efficacy of forensic identification processes, our project delves into cutting-edge techniques, particularly leveraging Deep Convolutional Generative Adversarial Networks (DCGANs). Situated at the nexus of image processing, artificial intelligence, and machine learning, these advanced algorithms offer promising avenues for transforming forensic sketches into remarkably realistic images.
Our project's primary objective is to develop a robust Forensic Sketch to Real Image Conversion System capable of generating highly authentic images from both hand-drawn and computer-generated forensic sketches. By harnessing the power of DCGANs, we aim to bridge the gap between the abstract representations provided by traditional forensic sketches and the detailed, lifelike images necessary for effective identification and investigation. This innovative system holds immense potential to revolutionize forensic practices, offering law enforcement agencies invaluable tools to aid in criminal investigations, locate missing persons, and address a myriad of forensic challenges.
Beyond its immediate applications in law enforcement and forensic science, the Forensic Sketch to Real Image Conversion System represents a significant stride towards advancing the intersection of technology and justice. By providing law enforcement agencies with the means to generate highly accurate depictions of suspects or victims from rudimentary sketches, our project seeks to bolster investigative capabilities while ensuring fairness and accuracy in criminal proceedings. Moreover, the potential societal impact extends beyond law enforcement, offering hope and closure to families of missing persons and victims of crime through improved identification methods
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