Sociology of Law: A Science of Norms and Strategic Normative Thinking

Sociology of Law as a Science of Norms

  • Conceptual Expansion of Sociology of Law (SoL):

    • Hydén argues that Sociology of Law must broaden its scope to become a "science of norms."
    • The concept of norms is expanded in several respects:
      1. Empirical Phenomenon: Norms are an existing, "real" empirical phenomenon.
      2. Analytical Tool: Norms serve as a tool to analyze the driving forces behind human behavior, giving SoL a central role in social science.
      3. Interdisciplinarity: Norms contribute to the need for interdisciplinary development.
      4. Legal Science Adequacy: SoL becomes more adequate as a legal science and gains greater relevance for legal practice.
      5. Interplay: It creates an understanding of the interplay between norms and legal rules.
      6. Social Science Expansion: It expands the scope of the social sciences.
    • Norms provide a common denominator for SoL, serving as its primary object of study, much like the legal system for lawyers, the political system for political scientists, the economy for economists, and the economic system for business administrators.

  • The Parallel to Genomics:

    • Understating changes in law requires mapping movements within a normative force field, combined with interpreting societal development through S-curves.
    • The Gene Analogy: The genes of a human organism are compared to the norms of human society. They are the "smallest components" of society and carry vital information.
    • Ready for Action: Just as genes create a readiness for action in organisms, norms create a readiness for action in society. However, neither are sole deciding factors; environmental and contextual factors determine most actions.
    • The Double Helix of Norms: Norms are likened to DNA. The two strands are held together by cross-links between three bases:
      1. W: Wills and Values.
      2. C / K: Knowledge and Cognition.
      3. SP: Systems and Possibilities.
    • The language and composition of these combinations remain largely unknown to legal and societal research, similar to the early stages of medical research into genes.

  • Norms vs. Legal Rules:

    • Legal rules are viewed as one specific category of norms. They are norms that have been "elevated" to have additional force.
    • Primary vs. Secondary: Norms/legal rules are activated in two stages. First, norms stipulate the action. If a conflict occurs, legal rules are activated. Law is thus secondary to society's underlying norms and only becomes relevant when the underlying norm system fails.
    • Sanction Differentiation:
      • Standard Norms: With the exception of moral/ethical norms and legal rules, sanctions are usually embedded in the norm itself (e.g., economic norms where a violation leads to loss, or technical norms where violation leads to structural failure).
      • Moral Norms: According to Émile Durkheim (19061906), moral norms have "artificial" or "synthetic" sanctions created externally by the societal system.
      • Technical Norms: Sanctions are natural and occur spontaneously from the violation.

  • Norms between Law and Society:

    • The Filter Function: Norms act as a filter between legal rules and society, affecting the outcome of regulations. This includes "intervening norms" produced by the legal system itself.
    • Interplay Questions:
      • What is the source of the legal rule?
      • Do norms and rules agree or collide? Collisions risk the norm "disturbing" the intended content of the rule.
      • Is the rule or the norm operative in specific cases?
    • Flexibility and Rigidity: Legal rules offer consistency and reliability (predictability) but are rigid. Transforming a norm into a legal rule makes it less flexible and may create a "gap" between the law and societal norms. A greater gap increases the cost of enforcing the rule.

Methodological Implications of Norm Science

  • Socio-Legal Analysis vs. Legal Dogma:

    • Legal Dogma: Starts with the rule and draws conclusions about the required action based on the rule's structure.
    • Socio-Legal Analysis: Begins with the action or behavior and reconstructs the normative premises that generated it. It asks: "What were the motives and normative content behind this behavior?"

  • Systemic Sources and Goal-Orientation:

    • Natural/Technical Systems: Norms linked to photosynthesis or thermodynamics are non-negotiable and demand obedience. However, environmental norms are often ignored because their sanctions are in the distant future and lack immediate identification.
    • Economic System: Highly goal-oriented. Economic norms decide what is normatively correct in a market context. Sanctions are activated directly (e.g., a bad investment results in immediate loss).
    • Socio-Cultural System: Based on discursive communication about good/bad or right/wrong. It is not traditionally goal-oriented and is often "weaker" than economic or technical systems. It is mobilized primarily when basic societal values (health, survival) are at risk.

  • Lifeworld vs. System:

    • The conflict between moral (lifeworld) and instrumental (system) motives changes over time.
    • Strength of Norms: Determined by quality and intensity rather than the number of people who embrace them.
    • The Market as Instrument: Consumers can use the economic system to assert socio-cultural norms (e.g., questioning production processes as negative externalities increase).

  • Temporal Worlds (Figure 7.30):

    • Vertical Perspective: People can experience the present from two curves simultaneously: the "upper curve" (industrial society, collective consumption) and the "lower curve" (digital society, individual investments).
    • Horizontal Perspective: People in different parts of the world belong to different temporal stages of development despite sharing the same point in time.

Strategic Normative Thinking (SNT)

  • Definition: A functional approach to legal regulation focusing on the involved parties' motives to avoid disputes and search for consensus-based solutions.

  • Ambition: To avoid litigation and find normative solutions that benefit both parties, as conflicts are expensive.

  • Relation to Legal Positivism: SNT complements legal positivism and is a natural component of legal dogmatic practice.

  • Application in the S-Curve:

    • Norms are usually created during the "establishment" phase of an S-curve through self-regulation.
    • Productivity Paradox: Coined by Robert Solow in the mid-1980s1980s; knowledge/technology adoption doesn't immediately show in productivity statistics because it takes time to develop skills and organization.
    • Game Rules Paradox: New "game rules" take time to develop even after a new society/tech emerges due to mental inertia and the resistance of old powerbrokers.

  • Self-Regulation Details:

    • Defined as systemic measures by companies or groups to solve problems relative to customers.
    • Includes: Recommendations, co-regulation (jointly formulated with authorities), and self-imposed measures.
    • Permissive Norms: Described by Nils Kristian Sundby as norms that permit rather than command/prohibit (e.g., contracts).

Sociology of Law in the Digital Era

  • Gene Technology and the Lag in Law:

    • There is a "knee-jerk reflex" to create new legislation for new technology, resulting in a lag where new tech is valued using terms from the old society.
    • Swedish Environmental Code (Chapter 13): Regulates Genetically Modified Organisms (GMOs).
    • Section 12: Approval for GMO release is only granted if the activity is "ethically justifiable," as determined by the Swedish Board of Agriculture and the Swedish Gene Technology Advisory Board.
    • The Responsibility Paradox: By permitting GMO use, the state assumes responsibility and grants criminal immunity to companies (Chapter 29, Section 1, Clause 4). From an SNT perspective, the industry might have been safer without state intervention, as companies would then have borne full civil and criminal liability, forcing higher caution.

  • Code is Law and Algorithms are Norms:

    • Lawrence Lessig's Four Forces:
      1. Law: Constraints through punishment.
      2. Societal Norms: Constraints through stigma.
      3. The Market: Constraints through price.
      4. Architecture (including Code): Constraints through physical or software-based burdens.
    • Software as Regulation: Code writers are the architects of societal construction. If "code is law," then "algorithms are the norms."

  • Algo Norms: The Second Order of Normativity:

    • First Order: Technical instructions (the algorithm as computational procedure).
    • Second Order: The societal consequences springing from the technical instructions. Hydén calls these "Algo Norms."
    • Difference from Social Norms: Social norms typically move from Will/Values (11) $\rightarrow$ Knowledge (22) $\rightarrow$ Systems (33). Algo norms move from Knowledge (22) $\rightarrow$ Systems (33) $\rightarrow$ Will/Values (11).
    • Kranzberg's First Law of Technology: Technology is neither good nor bad; nor is it neutral.

  • Sociological Concerns with Algo Norms:

    • Democratic Deficit: Norms are decided by technicians and algorithms rather than democratic political decision-making.
    • Manipulation and Filter Bubbles: Algorithms guess user preferences, encapsulating them in "filter bubbles" (Eli Pariser, 20112011) which isolate individuals from disagreeing viewpoints.

Regulatory and Methodological Challenges

  • The Productivity and Impact of AI:

    • McKinsey Global Institute states the AI revolution is happening 10×10\times faster and at 300×300\times the scale of the industrial revolution (3,000×3,000\times the impact).
    • Linear thinking prevents us from seeing the exponential progress according to Ray Kurzweil.

  • Methodological Reversal:

    • To understand algo norms, research must start from the outcome (actions/big data patterns) and work backward to reconstruct underlying motives and algorithms. This is the inverse of the legal sphere, which starts with the norm to predict the action.

  • Proactive vs. Reactive Regulation:

    • AI has a unique capacity for self-reproduction and autonomous development without human involvement.
    • The Dilemma: A proactive strategy is desirable but difficult due to a lack of experience with new tech. A reactive strategy (trial and error) might be too late once the AI has evolved.

  • Case Studies in Regulation:

    • Red Flag Laws: In 18621862 Sweden and the 19th19th century UK, men with red flags had to walk in front of steam-powered vehicles. Modern AI regulation faces similar regressive experiences as it integrates into society.
    • Japan's Tokku: Special zones for robotics empirical testing and development (e.g., Fukuoka Prefecture).
    • COMPAS: An algorithm used by US courts to assess recidivism risk via scales using behavioral big data.
    • Common Law vs. Statutory Law: Common law (case law) confronts new phenomena earlier because judges must decide even without precedents, whereas statutory systems (Continental Europe) wait for political consensus.

Questions & Discussion

  • Question: To what extent do you think AI would make better/worse and more/less unbiased decisions than humans?
  • Response: A 20192019 survey by YouGov (Nordic countries) found that around half of respondents believe AI makes decisions just as good as or better than humans. Support is strongest in banking and government but lowest in human-centric roles like nurses, doctors, and lawyers.
  • Argument on Regulation: The state should be a "safety belt" while the market acts as an "enforcer."