Nudging and Societal Mechanisms Quiz

Recap - Nudging

• Nudging encourages individuals to make self-beneficial decisions through subtle policy shifts rather than coercion.
• Examples of nudging include default options, social proof heuristics, reminders, feedback mechanisms, entertainment elements, and disclosure.
• Numerous "nudge units" exist, often driven by specific agendas from governmental and non-governmental organizations.

Recap – Societal-scale Mechanisms

• The Chinese Social Credit System raises concerns over economic and societal implications versus potential dystopian outcomes.
• It evaluates individuals and organizations based on economic factors and moral values, with experimental implementation strategies.
• The system is a case study for testing broader applications of nudging globally.

What is Artificial Intelligence?

• AI was coined by John McCarthy in 1956, lacking a universally accepted definition.
• Defined as the science and engineering of creating intelligent machines, particularly computer programs.
• AI encompasses understanding human or other forms of intelligence and does not limit itself to biologically observable methods.

Overview on the Process

• The ordinary legislative procedure for AI regulation includes public debate, consultations, and advocacy starting from April 2021.
• Key milestones involve electing government members and consulting the public, with a timeline extending from June 14, 2023, to December 9, 2023.
• Informal trilogues between responsible Members of the European Parliament and the Council aim at reaching political agreement.

Example: Changes Suggested to the AI Act Proposal

• Various European Parliament committees propose amendments to the AI Act, summarized in a negotiating mandate.
• This document facilitates discussions between the Parliament and the Council to achieve a political consensus.
• Academic and NGO feedback plays a significant role in shaping the proposed amendments.

Example: Differing Positions on Facial Analysis AI

• Facial Analysis AI is categorized as high-risk under the AI Act, which regulates both classification and identification.
• European Commission supports transparency for emotion recognition systems, while the European Parliament advocates for prohibitions on using sensitive characteristics in such systems for law enforcement and educational contexts.
• Council of the EU proposes additional transparency obligations regarding exposure to emotion recognition systems.

Conclusion of the Trilogue – Political Agreement Reached

• A political agreement covers prohibited AI practices, high-risk AI systems, transparency obligations, general-purpose AI models, and innovation measures.
• Governance and monitoring include EU databases for high-risk AI systems and post-market surveillance provisions.
• Compliance with codes of conduct and penalties for violations are also outlined.

Risk-based Approach to AI Regulation

• Prohibited AI Practices represent unacceptable risk, including social scoring and dangerous toys, leading to outright bans.
• High-risk AI systems require stringent regulations for classification, obligations, and notifications to authorities.
• Limited and minimal risk systems necessitate transparency obligations for users, with minimal risk cases needing optional guidelines and conduct codes.

Risk-based Approach – High-risk AI Systems

• High-risk applications encompass biometric identification systems, critical infrastructure management, and others that could significantly impact citizen rights and safety.
• Certain uses, like emotion recognition technologies, may be subject to stricter scrutiny and potential prohibition.

Test your knowledge on the concept of nudging and its impact on decision-making. Explore how subtle policy shifts can guide individuals towards better choices without force. Additionally, delve into societal-scale mechanisms like the Chinese Social Credit System and the implications they carry.