Preparing my AI to sue your AI.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6979919
Tort Law at the Frontier of Artificial Intelligence
The frontier of contemporary AI development is dominated by AI systems built on foundation models - highly versatile algorithms, trained in the first instance on broad swathes of data, that can function as tools and agents across a wide variety of commercial, social, military and political domains. For the moment, at least, the process of developing and releasing foundation models is subject to anemic formal regulation and haphazard ex ante governance. Until that changes, it is largely the common law of torts - our society's most ancient and general legal mechanism for governing serious risks of physical injury - that will govern the frontier of AI development.
This Article offers an in-depth conceptual, normative, and doctrinal examination of tort liability for foundation model development and release. It provides a qualified defense of the tort of negligence - the common law's broadest and most flexible cause of action - as the principal doctrinal foundation of the tort system's governance of this novel domain. Legal scholarship on AI liability has been quite hostile to negligence. By contrast, this Article argues that the generality and flexibility of the negligence tort - and its greater sensitivity to the externalized benefits of risky activity - render it well-suited to the polymathic and protean functionality of foundation models. Only the tort of negligence has the breadth and flexibility to address the range of important pathways - including internal deployments, inadequate model weight security, targeted entrustments of non-defective models, and open source releases - by which foundation model developers might cause serious harm.
Analyzing the choice between negligence and competing doctrinal regimes does, however, suggest important ways in which common law courts should incrementally develop the law of negligence, in order to properly reflect the risks and capabilities of foundation models. For example, courts should expand the scope of the duty of care in negligence, in order to provide redress when foundation models cause economic or emotional injury by behaving in ways that are closely analogous to serious human wrongdoing (e.g., certain crimes and intentional torts, such as theft, deceit, and outrage).
But the Article's analysis also suggests certain fundamental pathologies of tort liability as a mechanism of AI governance - pathologies that no amount of doctrinal development will adequately cure. In particular, the specter of tort liability can be expected to disincentivize frontier AI developers from investigating and disclosing many of the novel and poorly understood risks that frontier AI development may pose. That is especially disturbing given that our society is relying quite heavily, for its ability to discover and understand these risks, on frontier AI developers themselves. Thus, tort liability is not only inadequate as a mechanism of frontier AI governance; in certain important respects, it is actively perverse, and its perverse effects must be countered by governance institutions of a different kind. Ultimately, a robust regime of ex ante regulation - under which government institutions or credibly neutral third-party experts are empowered to investigate, evaluate, and mitigate the risks of frontier AI development - is urgently required in frontier AI governance.
Tools & Techniques. Could be useful.
https://journals.uwyo.edu/index.php/jtilt/article/view/10247
Teaching Prompt Engineering as a Core AI Literacy Skill in Undergraduate Education
This learning representation introduces undergraduate students to prompt engineering as a structured, iterative practice rather than an ad hoc interaction with generative AI tools. Students design, test, and refine prompts within a domain of their choosing, documenting each iteration and evaluating outputs for accuracy, relevance, and ethical considerations. The activity emphasizes transparency, reflection, and intentional AI use, positioning prompt engineering as both a technical and metacognitive skill. By engaging students in guided experimentation and revision, the assignment supports AI literacy while reinforcing critical thinking, communication, and documentation skills applicable across academic and professional contexts.
Tools & Techniques. Takes some work, but could be useful.
I built a tiny Claude skill that turns any document into a mind map, and now I can visualize anything
I have reopened the same 60-page PDF multiple times this week, and I still can't tell you what is in the middle of it. Linear reading has never really clicked for me. Somewhere around page twenty, a long report stops being information and starts being wallpaper. So I built a small Claude skill that takes any document and hands me back a navigable mind map. It's the same instinct behind turning plain notes into visual maps. A branching picture sticks in my head when paragraphs just slide off it.
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