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.
https://www.makeuseof.com/tiny-claude-skill-that-turns-any-document-into-mind-map-visualize-anything/
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.