Always interesting.

https://www.rand.org/content/dam/rand/pubs/research_reports/RRA2200/RRA2249-1/RAND_RRA2249-1.pdf

Finding a Broadly Practical Approach for Regulating the Use of Facial Recognition by Law Enforcement

Communities across the United States are grappling with the implications of law enforcement organizations’ and other government agencies’ use of facial recognition (FR) technology. Although the purported benefits of FR as stated are clear, they have yet to be measured and weighed against the existing risks, which are also substantial. Given the variety of ways in which FR can be used by law enforcement, the full benefit-to-risk trade-off is difficult to account for, leading to some municipalities that ban the use of FR by law enforcement and others that have no clear regulations. This report provides an overview of what is known about FR use in law enforcement and provides a road map of sorts to help policymakers sort through the various risks and benefits relative to different types of FR use.

We categorize the various identified risks associated with FR technology and its use by law enforcement, including accuracy, bias, the scope of the search (i.e., surveillance versus investigation), data-sharing and storage practices, privacy issues, human and civil rights, officer misuse, law enforcement reactions to the FR results (e.g., street stops), public acceptance, and unintended consequences. The concerns are discussed in detail in Chapter 3, but they are summarized here.

A thoughtful AI?

https://www.tandfonline.com/doi/full/10.1080/15027570.2023.2180184

The Need for a Commander

… One article in this double issue of the Journal of Military Ethics asks about what an AI (artificial intelligence) commander would look like. The underlying question is whether we are more or less inevitably moving towards a situation where AI-driven systems will come to make strategic decisions and hence be the place where the buck stops.

I don’t get it…

https://www.science.org/doi/abs/10.1126/science.add2202

Leveraging IP for AI governance

The rapidly evolving and expanding use of artificial intelligence (AI) in all aspects of daily life is outpacing regulatory and policy efforts to guide its ethical use (1). Governmental inaction can be explained in part by the challenges that AI poses to traditional regulatory approaches (1). We propose the adaptation of existing legal frameworks and mechanisms to create a new and nuanced system of enforcement of ethics in AI models and training datasets. Our model leverages two radically different approaches to manage intellectual property (IP) rights. The first is copyleft licensing, which is traditionally used to enable widespread sharing of created content, including open-source software. The second is the “patent troll” model, which is often derided for suppressing technological development. Although diametric in isolation, these combined models enable the creation of a “troll for good” capable of enforcing the ethical use of AI training datasets and models.

I think I think so too.

https://indexlaw.org/index.php/rdb/article/view/7547

DEEP LEARNING AND THE RIGHT TO EXPLANATION: TECHNOLOGICAL CHALLENGES TO LEGALITY AND DUE PROCESS OF LAW

This article studies the right to explainability, which is extremely important in times of fast technological evolution and use of deep learning for the most varied decision-making procedures based on personal data. Its main hypothesis is that the right to explanation is totally linked to the due process of Law and legality, being a safeguard for those who need to contest automatic decisions taken by algorithms, whether in judicial contexts, in general Public Administration contexts, or even in private entrepreneurial contexts.. Through hypothetical-deductive procedure method, qualitative and transdisciplinary approach, and bibliographic review technique, it was concluded that opacity, characteristic of the most complex systems of deep learning, can impair access to justice, due process legal and contradictory. In addition, it is important to develop strategies to overcome opacity through the work of experts, mainly (but not only). Finally, Brazilian LGPD provides for the right to explanation, but the lack of clarity in its text demands that the Judiciary and researchers also make efforts to better build its regulation.

Tools worth testing?

https://www.makeuseof.com/accurate-ai-text-detectors/

The 8 Most Accurate AI Text Detectors You Can Try

As language models like GPT continue to improve, it is becoming increasingly difficult to differentiate between AI-generated and human-written text. But, in some cases, like academics, it’s necessary to ensure that the text isn't written by AI.

This is where AI text detectors come into play. Though none of the tools currently available detect with complete certainty (and neither do they claim to do so), a few of these tools do provide pretty accurate results. So, here, we list down the eight most accurate AI text detectors you can try.