Strategic normative thinking

  • SNT is introduced as a legal innovation

    • uses norm science in a way to handle or analyze real world normative problems

  • Self regulations

    • systemic measures given by companies, industries, or the whole buisness community to investigate prevent or solve problems in relation to consumers or customers

    • self regulation

      • self interest and general interest collide

  • SNT as a normative design to support self regulation

    • when new behaviors emerge from society that law has yet to catch up SNT helps design

      • normative structures that guide behavior (social norms?)

      • configurations that take into account different motivations attitudes etc

  • Gene tech - law lag and state acceptance of risk

    • chapter uses swedish code regulation as an example of law lagging behind technological innovation

  • due to unknown consequences regulation now requires aprovals before introducing new products that include gmo

  • the normativity of tech: digital disrupt

    • gigital tech and AI are described as disruptive and conflict with the ways of doing things

    • problems are often cognitive before normative

    • old rules become obstacles to new forms of work lifestyle and trade

    • lagging mentality which involves looking through the lens of the old society and old problems subjected onto them

    • tech development generates new normative conditions faster than traditional law making can respond

  • code is law

    • law constrains via sanctions

    • societal norms contrain via stigma

    • markets contrain via prices

    • architechure contrains via what is technically possible or impossible

    • code includes features selected by programmers that permit block or channel behaviors in a sense code is regulation just like physical architecture

    • Algorithms are instructions on what to do with what and what order

    • together with interfaces

  • Methodological and regulatory implications

    • to study algo norms you start from outcomes anfd patterns of action then infer the underlying motives and the effect of those outcomes

  • Regulatory implications

    • legal regulation requires knowledge about what is being regulated but with AI we often do not fully know the consequences

    • normative patterns become empirical questions