Hildebrandt, Mireille. 2018 Algorithmic regulation and the rule of law
37 important questions on Hildebrandt, Mireille. 2018 Algorithmic regulation and the rule of law
What does the essay primarily discuss regarding the intersection of computational systems and governmental processes?
The essay primarily discusses the integration of computational systems into governmental processes.
According to the essay, in what ways can legislation be influenced by algorithmic applications?
Legislation may be written in a way conducive to algorithmic application, and administration may be automated, including administrative decisions.
Which term is used to describe the integration of artificial legal intelligence (ALI) in court judgments?
The term used to describe the integration of artificial legal intelligence (ALI) in court judgments is "algorithmic regulation."
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According to the essay, what potential conflation is raised in the context of legislation becoming self-executing?
The potential conflation raised in the context of legislation becoming self-executing is the merging of legislation and administration.
How might algorithmic decision systems impact the traditional separation between legislation and administration?
Algorithmic decision systems might impact the separation between legislation and administration by making legislation self-executing.
In the context of automated systems, what implications are mentioned for the contestability of decisions?
In the context of automated systems, decisions may be contestable based on the conditions not applying or an incorrect interpretation of legal norms.
What distinguishes code-driven regulation from data-driven regulation?
Code-driven regulation is self-executing, while data-driven regulation provides decisional support based on predictive algorithms.
How is IFTTT (if this then that) logic related to code-driven regulation?
IFTTT logic is related to code-driven regulation as it forms the fundamental logic for all algorithmic decision systems.
According to the essay, what is the significance of the choice of training data in data-driven regulation?
The choice of training data in data-driven regulation is significant for informing the code rather than being based on human translation.
What distinguishes code-driven regulation from older forms of legal artificial intelligence?
Code-driven regulation takes actual decisions affecting legal subjects, distinguishing it from older forms of legal artificial intelligence.
Why does the essay argue that code-driven regulation may not necessarily ensure complete transparency and interpretability?
The essay argues that transparency and interpretability issues are hidden in the formalization preceding the operations of the system.
In code-driven regulation, what role does the human entity play in the decision-making process?
In code-driven regulation, the human entity must comprehend and justify decisions based on legal conditions.
How does data-driven regulation differ from code-driven regulation in terms of decision-making?
Data-driven regulation differs in that it informs code through training on data, and ALI is based on natural language processing.
What challenges are associated with the interpretability of output in artificial legal intelligence (ALI)?
Challenges associated with the interpretability of output in ALI include choices in training data, labelling, and performance metrics.
In what ways does ALI introduce a new type of discretion in algorithmic decision-making?
ALI introduces a new type of discretion in design choices made during algorithm training.
Why does the essay argue for a new legal hermeneutics in the face of major investments in ALI?
A new legal hermeneutics is argued for in response to major investments in ALI, based on understanding the vocabulary and grammar of machine learning.
According to the essay, what is the importance of lawyers understanding the vocabulary and grammar of machine learning?
Lawyers need to understand machine learning's vocabulary and grammar to adapt legal hermeneutics to the challenges posed by ALI.
How does the concept of 'agonistic machine learning' propose to address challenges in the design of ALI?
’Agonistic machine learning' is proposed to bring adversarial contestation into the design of ALI.
How does the essay describe the transformation of text-driven law into code-driven and data-driven regulation?
The essay describes the transformation of text-driven law into code-driven and data-driven regulation, eroding the grammar and alphabet of modern positive law.
According to the essay, what potential consequences may arise as algorithmic regulation takes over from human regulation?
Consequences of algorithmic regulation taking over include potential erosion of the grammar and alphabet of modern positive law.
In what ways might the transformation impact the grammar and alphabet of modern positive law?
The transformation may impact the comprehensibility and contestability of administrative decisions based on code-driven and data-driven regulation.
In what ways do computational systems impact governmental processes, and how might legislation, administration, and adjudication be influenced by algorithmic applications?
Computational systems influence governmental processes by 'infusing' legislation, administration, and adjudication. Legislation may be written to facilitate algorithmic application, administration may be automated, and courts may utilize artificial legal intelligence (ALI). These developments raise questions about the rule of law, such as the conflation of legislation and administration, the potential conflation of legal judgment with prediction, and the challenges posed by automated systems.
Discuss the implications of algorithmic regulation on the text-driven nature of modern, positive law, and how does it affect the force of law?
Algorithmic regulation, including code-driven and data-driven approaches, transforms modern, positive law from being text-driven. This evolution challenges the force of law, defined in terms of legal effect and performative speech acts, distinct from behavioristic regulatory paradigms. The essay traces the transformation from text-driven law to code-driven and data-driven regulation, highlighting potential erosion of the grammar and alphabet of modern positive law.
Why does the essay propose the need for a new hermeneutics in the face of major investments in ALI and smart regulation, and what is the significance of 'agonistic machine learning' in this context?
The essay argues for a new hermeneutics to address major investments in ALI and smart regulation, emphasizing the importance of understanding the vocabulary and grammar of machine learning. 'Agonistic machine learning' is presented as an example, advocating for adversarial contestation in the design of ALI to ensure robust and accountable decision-making.
Differentiate between code-driven and data-driven algorithmic regulation, providing examples of each and explaining their impact on decision-making.
Code-driven regulation is self-executing, relying on deterministic IFTTT logic, and is illustrated by systems making decisions affecting legal subjects, such as 'smart regulation' in taxation. Data-driven regulation involves decisional support based on predictive algorithms informed by machine learning, with examples like artificial legal intelligence (ALI). The essay emphasizes the challenges and nuances associated with human intervention in decision-making under these approaches.
Discuss the significance of IFTTT in code-driven regulation and how it influences the transparency and interpretability of decision systems.
IFTTT, or 'if this then that,' forms the fundamental logic of code-driven regulation. While this logic is deterministic and predictable, the essay highlights that transparency and interpretability issues are hidden in the formalization preceding system operations. The essay suggests that understanding the translation of legal norms into computer code is crucial for justifying decisions.
Analyze the contestability of decisions in code-driven regulation under the rule of law, considering the basis for contestation and the role of legal norms.
Code-driven regulation's decisions must be comprehensible and justifiable under the rule of law. Contestability arises on the basis of legal conditions not applying or an incorrect interpretation of relevant legal norms. Legal norms expressed in human language introduce ambiguity, making contestation a safeguard against over-inclusive or under-inclusive legal norms.
Critical Analysis of Regulation Perspectives: Discuss the two distinct meanings of the term "regulation" outlined in the text. How do these meanings represent different levels of analysis, and what implications do they have for understanding the relationship between law and regulation?
Critical Analysis of Regulation Perspectives: The term "regulation" encompasses two distinct meanings, external and internal, influencing behavior and policy rules by regulators. Examine each perspective, noting the external's focus on behavior and information and the internal's concern with legal principles and constitutional protection. Conclude that understanding both perspectives is crucial for lawyers, emphasizing the need for a nuanced comprehension of law and regulation.
Legislating vs. Regulating: Elaborate on the distinction between legislating and regulating, emphasizing the internal and external perspectives on law. How does this distinction impact the binding force of policy rules and the role of regulatory bodies?
Legislating vs. Regulating: Highlight the difference between legislating and regulating. Elaborate on the distinction, emphasizing the legislative authority of Parliament or Congress and the regulatory role of administrative bodies. Discuss the impact of this separation on legal governance, emphasizing the importance of rule-bound discretion in administration.
Cybernetic vs. Legal Regulation: Explore the concepts of "cybernetic regulation" and "legal regulation." How does the cybernetic perspective treat law as a subset, and what does it miss in terms of domain specificity and the force of law? Analyze the internal perspective of legal regulation and its connection to the rule of law.
Cybernetic vs. Legal Regulation: Define cybernetic and legal regulation perspectives.Analyze cybernetic regulation as external and legal regulation as internal, highlighting the domain specificity of law. Emphasize the need for a balanced internal perspective to understand law's unique operations compared to cybernetic regulation.
Legal Effect and Speech Act Theory: Explain the concept of legal effect and its basis in speech act theory. How does legal effect differ from mere enforcement or social consensus? Discuss the role of legal effect in creating institutional facts and its significance in modern positive law.
Legal Effect and Speech Act Theory: Define legal effect and its link to speech act theory. Discuss how legal effect, as per speech act theory, creates institutional facts and is not based on causality. Emphasize the performative nature of legal text, which establishes rules and principles constituting modern positive law.
Rule of Law and Legal Certainty: Examine the role of the rule of law in the context of legal regulation. How does the rule of law contribute to legal certainty and the coordination, prohibition, and enablement of actions? Discuss the constraints imposed by the legality principle on government actions.
Rule of Law and Legal Certainty: Introduce the rule of law's role in legal regulation. Discuss how the rule of law contributes to legal certainty, ensuring government actions align with legality principles. Stress the importance of legal certainty in democratic systems and the constraints it places on government actions.
Algorithmic Regulation and Agonistic Machine Learning: Critically analyze the concept of algorithmic regulation, distinguishing between code-driven and data-driven approaches. Explore the challenges and considerations in integrating data-driven regulation under the rule of law. What is the significance of adopting an agonistic approach to machine learning in the legal context?
Algorithmic Regulation and Agonistic Machine Learning: Define algorithmic regulation and distinguish code-driven from data-driven approaches. Analyze challenges in integrating data-driven regulation into the rule of law, advocating for an agonistic approach. Emphasize the need for an adversarial design process, ensuring robust machine learning aligned with legal principles.
Ethical Considerations in Data-Driven Legal Tech: Evaluate the ethical implications of data-driven legal tech, considering issues such as bias, interpretability, and transparency. How can an agonistic design process contribute to addressing these ethical concerns and ensuring robust decision-making?
Ethical Considerations in Data-Driven Legal Tech: Introduce ethical considerations in data-driven legal tech. Discuss bias, interpretability, and transparency issues, emphasizing the ethical need for guidelines and an agonistic design process. Stress the importance of ethical considerations in ensuring fair and just legal tech applications.
Exploratory vs. Confirmatory Machine Learning: Discuss the distinctions between exploratory and confirmatory machine learning. How can these approaches be applied in the context of algorithmic legal interpretation (ALI)? Explore the importance of transparency and pre-registration in research designs related to machine learning in law.
Exploratory vs. Confirmatory Machine Learning: Define exploratory and confirmatory machine learning. Discuss their applications in algorithmic legal interpretation, emphasizing transparency and pre-registration. Highlight the role of transparent exploratory and confirmatory machine learning in supporting legal decisions.
Agonistic Approach and Rule of Law: Investigate the intersection between the agonistic approach and the rule of law. How does inviting dissent and constructive resistance align with the principles of democracy and the rule of law? Discuss the implications of an adversarial design process for decision-making in legal contexts.
Agonistic Approach and Rule of Law: Introduce the agonistic approach and its relevance to the rule of law. Analyze how inviting dissent aligns with democratic principles and the rule of law. Emphasize the importance of an adversarial design process in decision-making, promoting democratic values.
Future Challenges in Algorithmic Regulation: Anticipate and discuss potential challenges and considerations in the future development and application of algorithmic regulation. How can the legal community adapt to ensure that algorithmic systems align with legal principles and uphold the rule of law?
Future Challenges in Algorithmic Regulation: Discuss the growing role of algorithmic regulation. Anticipate challenges such as accountability, transparency, and legal adaptation. Emphasize the need for ongoing legal scrutiny and adaptation to future challenges, ensuring algorithmic regulation aligns with legal principles.
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