A summary of the European AI Act

 

 

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The advent of generative AI marks a profound "cognitive revolution", transforming how we interact with technology and unlocking new possibilities across various domains. As a game-changing technology, generative AI, exemplified by models like GPT and DALL-E, has captured public attention by demonstrating capabilities in generating text, images, and even complex problem-solving that mimic human creativity. This surge in public awareness was fueled by several high-profile developments, including the release of ChatGPT, which showcased AI's potential in everyday applications and sparked widespread discussions about its implications.

Generative AI differs from traditional AI in its ability to create novel content rather than simply analyzing or categorizing existing data. While traditional AI focuses on recognizing patterns and making decisions based on predefined rules or data, generative AI uses complex algorithms, such as deep learning and neural networks, to produce new, original outputs, such as text, images, or music, that were not explicitly programmed.

The impact of generative AI is expected to be vast, influencing fields such as education, healthcare, entertainment, and content creation. It will drive innovation in these sectors, but also raises important questions about ethics, ownership, and the potential for misuse. Given its transformative power, it is crucial to establish a robust governance framework to guide the responsible development and deployment of generative AI technologies, ensuring they benefit society while minimizing risks.

 Governing the development and deployment of AI through regulatory institutions like the EU is essential to ensuring that these powerful technologies are aligned with public interest, ethical standards, and human rights. Regulatory oversight helps mitigate risks such as bias, misuse, and privacy violations, which can have far-reaching consequences in an AI-driven society. By setting clear guidelines and standards, institutions like the EU can foster innovation while ensuring that AI systems are transparent, accountable, and safe. This governance is crucial not only for protecting citizens but also for maintaining global competitiveness and leadership in the responsible development of AI technologies.

The European AI Act, which officially came into force on August 1, 2024, marks a significant milestone in the global regulation of artificial intelligence. As the first comprehensive AI regulation worldwide, this Act aims to establish a harmonized legal framework across the European Union, addressing the potential risks associated with AI while fostering innovation.

Motivation and Objectives

The AI Act was motivated by the need to manage the dual challenges of promoting AI's economic benefits while safeguarding fundamental rights, health, and safety. The regulation adopts a risk-based approach, classifying AI systems into categories such as minimal risk, high risk, and unacceptable risk. This classification determines the level of regulatory scrutiny and compliance required.

Key Features

1. Risk-Based Regulation: AI systems are categorized based on the potential risk they pose. High-risk systems, such as those used in critical infrastructure or law enforcement, must meet strict requirements, including transparency, human oversight, and rigorous testing. Unacceptable risk AI systems, such as those enabling social scoring by governments, are outright banned.

2. Transparency and Accountability: The Act requires that AI systems provide clear information to users, particularly when the AI interacts directly with individuals, ensuring users are aware they are engaging with AI and can understand the decision-making process.

3. Support for Innovation: To balance regulation with innovation, the Act includes provisions for regulatory sandboxes, where developers can test AI technologies under the supervision of regulatory bodies. This fosters innovation while ensuring compliance with EU standards.

4. Global Impact: The AI Act's extraterritorial application ensures that any AI system used within the EU, regardless of where it was developed, must comply with EU regulations. This positions the EU as a global leader in AI governance, influencing international standards and practices.

The Act's phased implementation, with key obligations for high-risk AI systems becoming enforceable by 2026, provides time for businesses and governments to adapt. This legislation is seen as a crucial step in ensuring that AI development is both innovative and aligned with fundamental European values​.

For further information, please consult the following sources:

European Commission, Goodwin.

We will now provide a summary of the main parts of the document (“AI Act”) issued by the European institutions.

1. Title and Preamble

Title:

• The document is titled the "Artificial Intelligence Act," officially labeled as "Regulation (EU) 2024/... of the European Parliament and of the Council laying down harmonized rules on artificial intelligence (AI) and amending certain Union Legislative Acts."

Preamble:

• The preamble provides the legal basis and the motivation for the regulation. It states that the purpose of the Act is to establish a uniform legal framework within the EU for the development, deployment, and use of AI systems. The regulation aims to promote AI uptake while ensuring that AI systems are aligned with EU values, such as fundamental rights, democracy, the rule of law, and environmental protection.
• It emphasizes the importance of AI systems being human-centric, trustworthy, and safe, and it underlines the need to prevent fragmentation of the internal market due to divergent national regulations.
• The preamble also highlights the balance the regulation seeks to achieve between fostering innovation and protecting public interests like health, safety, and fundamental rights.

2. Recitals

The recitals provide detailed reasoning and context behind the specific provisions of the AI Act. Here is a summary of the key points covered in the recitals:

• Objective of the Regulation: The regulation aims to improve the functioning of the internal market by establishing harmonized rules for AI systems in the EU, promoting innovation while ensuring the protection of health, safety, and fundamental rights.

• Scope and Application: The regulation is designed to apply across different sectors and industries, given the broad applicability of AI systems. It also takes into account the rapid evolution of AI technologies, ensuring that the legal framework remains relevant and adaptable.

• Human-Centric AI: The regulation emphasizes that AI systems should be developed and used in a way that enhances human well-being, with a focus on human dignity, autonomy, and fundamental rights.

• Risk-Based Approach: A core principle of the regulation is the risk-based approach, where AI systems are classified based on the level of risk they pose to public interests and fundamental rights. This approach dictates the level of regulatory scrutiny and obligations imposed on AI providers and users.

• Prohibited AI Practices: The recitals identify certain AI practices that are deemed unacceptable, such as those that manipulate human behavior, exploit vulnerabilities, or involve social scoring by public authorities.

• High-Risk AI Systems: The recitals outline the rationale for categorizing certain AI systems as high-risk, particularly those that could significantly impact health, safety, or fundamental rights. These systems are subject to stricter requirements to ensure their safe and ethical use.

• Transparency and Accountability: The regulation requires transparency in AI systems, ensuring that users and affected individuals are aware of when they are interacting with AI and understanding the decision-making processes involved.

• Governance and Oversight: The recitals stress the need for strong governance mechanisms, including the establishment of supervisory authorities and the European Artificial Intelligence Board, to ensure compliance with the regulation.

• International Cooperation: The regulation also addresses the international dimension, ensuring that AI systems used in the EU, even if developed outside, comply with EU standards. This aims to prevent regulatory evasion and protect EU citizens from harmful AI practices.

   

  3. General Provisions

  The General Provisions section of the AI Act sets the foundational elements of the regulation, including its scope, definitions, and overarching principles. This section is crucial as it establishes the framework within which the entire regulation operates.

  Key Points:

  Scope (Article 1):

• The regulation applies to the development, placement on the market, and use of AI systems within the EU. It covers AI systems regardless of whether they are standalone or embedded within other products.
• The regulation aims to ensure that AI systems are safe, transparent, and align with EU values, particularly concerning fundamental rights and public interests like health and safety.
• Exemptions are provided for AI systems developed and used exclusively for military, defense, or national security purposes, as these areas are outside the regulation's scope.

Definitions (Article 2):

• The regulation provides clear definitions of key terms used throughout the document, such as "AI system," "provider," "user," "high-risk AI system," and "biometric data."
• AI System: Defined as software developed using machine learning, logic-based, or knowledge-based approaches that can, for a given set of human-defined objectives, generate outputs such as predictions, recommendations, or decisions influencing the environments they interact with.
• Provider: Refers to any person or entity that develops, places on the market, or puts into service an AI system.
• User: Any person or entity using an AI system under their authority, except for personal, non-professional use.

Risk Classification (Article 3):

• The regulation adopts a risk-based approach to classify AI systems into different categories: unacceptable risk, high risk, and minimal risk. The level of regulation and obligations imposed depends on the risk classification:
  • Unacceptable Risk: AI systems that pose a clear threat to safety, livelihood, and rights of people are banned outright.
  • High-Risk AI Systems: Systems that could significantly affect individuals' safety or rights are subject to strict requirements, including mandatory conformity assessments, transparency obligations, and human oversight.
  • Minimal Risk AI Systems: These are subject to minimal regulation, primarily focusing on transparency.

General Obligations (Article 4):

• AI providers must ensure that their systems comply with the requirements laid down in the regulation before placing them on the market or putting them into service.
• Providers are responsible for ensuring that their AI systems undergo conformity assessments, are accompanied by the necessary documentation, and meet the required safety, transparency, and robustness standards.
• Providers must also establish and maintain risk management systems throughout the lifecycle of the AI systems to ensure continued compliance with the regulation.

Union-Level Cooperation (Article 5):

• The regulation mandates the establishment of the European Artificial Intelligence Board (EAIB), which will coordinate and support the consistent application of the AI Act across the EU.
• The EAIB will work closely with national authorities to ensure that AI systems deployed within the EU comply with the regulation, fostering a harmonized approach across Member States.

4. Prohibited AI Practices

This section outlines the AI practices that are explicitly banned under the regulation due to their potential to harm individuals or society at large. These prohibitions are essential to ensure that AI technologies do not undermine fundamental rights, democracy, or public safety.

Key Points:

Prohibited AI Practices (Article 6):

• Manipulative AI Systems: AI systems that deploy subliminal techniques beyond an individual's conscious perception to materially distort their behavior in a way that may cause physical or psychological harm are prohibited.
• Exploitation of Vulnerabilities: AI systems that exploit vulnerabilities of specific groups, such as children, disabled individuals, or economically disadvantaged persons, to materially distort their behavior in a way that causes harm, are banned.
• Social Scoring: The use of AI systems by public authorities or private entities to evaluate or classify individuals based on their social behavior, known or inferred personal characteristics, or other subjective factors over a period, which leads to detrimental or unfair treatment, is prohibited.
• Biometric Surveillance: Real-time remote biometric identification systems in publicly accessible spaces for law enforcement purposes are generally prohibited, with narrowly defined exceptions (e.g., for severe public safety threats).

Exceptions and Specific Conditions (Article 7):

• The regulation does provide certain exceptions where the use of otherwise prohibited AI practices may be justified. For instance, the use of real-time biometric identification systems by law enforcement is permitted in cases of significant public interest, such as the prevention of terrorism or the search for missing children, but only under strict conditions and oversight.

   

  5. High-Risk AI Systems

  The section on High-Risk AI Systems is one of the most critical parts of the AI Act. It outlines the specific obligations and requirements for AI systems that are classified as high risk due to their potential impact on people's safety, rights, or significant public interest.

  Key Points:

  Classification of High-Risk AI Systems (Article 8):

• High-risk AI systems are identified based on their intended purpose, the context of their use, and their potential impact on health, safety, and fundamental rights.
• The regulation provides a list of high-risk AI systems in an annex, which includes AI systems used in critical infrastructure (like energy and transport), educational settings, employment, law enforcement, and biometric identification.

Obligations for Providers of High-Risk AI Systems (Article 9):

• Risk Management: Providers must implement a comprehensive risk management system throughout the lifecycle of the AI system. This includes identifying, analyzing, and mitigating risks associated with the AI system before it is placed on the market and continuously during its use.
• Data Governance: High-risk AI systems must be developed using high-quality, relevant, and representative data sets. Providers are required to establish data governance practices that ensure data quality, relevance, and traceability, with a focus on minimizing bias and inaccuracies.
• Technical Documentation: Providers must create and maintain detailed technical documentation that demonstrates the AI system’s compliance with the AI Act. This documentation must include a description of the system’s architecture, data, algorithms, and performance metrics.
• Record-Keeping: Providers must keep logs of the AI system's operations, particularly when the system makes decisions that impact individuals’ rights or safety. These records should be available for audit by regulatory authorities.
• Transparency and Provision of Information: Providers must ensure that their AI systems are transparent, meaning that users can understand how the system operates and how decisions are made. This includes providing clear instructions for use, limitations, and any risks associated with the system.

Obligations for Users of High-Risk AI Systems (Article 10):

• Monitoring and Reporting: Users of high-risk AI systems must monitor the operation of these systems and report any incidents or malfunctions that could affect compliance with the AI Act to the provider or relevant authorities.
• Human Oversight: Users must ensure that AI systems are used under human oversight. This includes setting up mechanisms for intervention when the AI system operates unexpectedly or in a way that could lead to harm.
• Use in Accordance with Instructions: Users must operate high-risk AI systems strictly according to the instructions provided by the system's provider, especially regarding safety and performance limitations.

Conformity Assessment (Article 11):

• High-risk AI systems must undergo a conformity assessment before they can be placed on the market or put into service. This assessment can be conducted either by the provider (for some types of systems) or by a third-party conformity assessment body.
• The conformity assessment includes evaluating the system’s compliance with the AI Act’s requirements, particularly in terms of risk management, data governance, and technical documentation.
• Post-Market Surveillance: Providers must establish and implement a post-market surveillance plan to monitor the performance of high-risk AI systems after they have been deployed. This ensures ongoing compliance with the AI Act and addresses any emerging risks or non-compliances.

Registration of High-Risk AI Systems (Article 12):

• Providers of high-risk AI systems must register their systems in an EU-wide database managed by the European Artificial Intelligence Board (EAIB). This database is intended to ensure transparency and facilitate monitoring and enforcement by authorities.
  

 

6. Transparency Requirements

This section focuses on the obligations related to transparency, which is crucial for ensuring that AI systems are understandable and accountable, particularly for users and those affected by AI decisions.

Key Points:

Transparency Obligations (Article 13):

• Disclosure to Users: Providers must ensure that users are informed that they are interacting with an AI system, especially in cases where it may not be obvious. This is particularly relevant for AI systems that generate or manipulate content, like chatbots or deepfakes.
• Explainability: AI systems must be designed and deployed in a way that allows users to understand the rationale behind the decisions made by the AI. This is essential for maintaining trust and accountability, especially in high-risk applications.
• Disclosure of Capabilities and Limitations: Providers must clearly communicate the capabilities and limitations of the AI system, including any conditions under which the system might fail or produce biased results.

Human-Machine Interaction (Article 14):

• AI systems that interact with humans must be designed to clearly indicate when the user is interacting with a machine, not a human. This is intended to prevent deception and ensure that users are fully aware of the nature of their interaction.
• Feedback Mechanisms: Providers must include mechanisms within the AI system that allow users to provide feedback or report issues. This feedback is crucial for continuous improvement and addressing any unintended consequences or errors in the system’s operation.

 

7. Governance and Oversight

The Governance and Oversight section of the AI Act outlines the structures and mechanisms put in place to ensure compliance with the regulation across the European Union. This section is crucial for maintaining the integrity and effectiveness of the AI Act through coordinated supervision, enforcement, and cooperation between various authorities.

Key Points:

European Artificial Intelligence Board (EAIB) (Article 15):

• The AI Act establishes the European Artificial Intelligence Board (EAIB) to oversee the implementation and enforcement of the regulation across the EU.
• Composition: The EAIB will be composed of representatives from each Member State's national supervisory authority, the European Commission, and other relevant EU bodies. It will be chaired by a representative from the European Commission.
• Functions: The EAIB will play a central role in ensuring a consistent application of the AI Act across the EU. Its duties include:
  • Guidance: Providing guidelines, recommendations, and best practices for the implementation of the AI Act.
  • Coordination: Facilitating cooperation between national supervisory authorities and ensuring consistent enforcement of the AI Act across Member States.
  • Advisory Role: Advising the European Commission on matters related to AI, including updates to the list of high-risk AI systems and other regulatory aspects.
  • Monitoring: Overseeing the operation of the EU-wide database of high-risk AI systems and ensuring that the registration and reporting requirements are met.

National Supervisory Authorities (Article 16):

• Each Member State is required to designate one or more national supervisory authorities responsible for enforcing the AI Act within their jurisdiction.
• Powers: These authorities are granted wide-ranging powers to investigate AI systems, conduct audits, require the disclosure of documentation, and impose penalties for non-compliance.
• Responsibilities: National authorities must monitor AI systems' compliance with the regulation, particularly in relation to high-risk AI systems. They are also responsible for ensuring that providers and users adhere to their obligations under the AI Act.
• Cooperation: National supervisory authorities are required to cooperate closely with the EAIB and other national authorities to ensure uniform enforcement of the regulation across the EU.

Market Surveillance and Enforcement (Article 17):

• The regulation empowers national market surveillance authorities to take necessary actions to ensure that AI systems placed on the market or put into service comply with the AI Act.
• Enforcement Actions: Authorities can:
  • Conduct Inspections: Inspect premises, products, and documentation related to AI systems.
  • Issue Corrective Measures: Require providers to bring non-compliant AI systems into conformity with the regulation, withdraw them from the market, or recall them.
  • Penalties: Impose administrative fines and other penalties on providers or users who fail to comply with the AI Act.
• Complaint Mechanism: Individuals or organizations can lodge complaints with national supervisory authorities if they believe that an AI system violates the AI Act. Authorities are required to investigate such complaints and take appropriate action.

Reporting Obligations (Article 18):

• Providers of high-risk AI systems must report any serious incidents or malfunctions to the relevant national supervisory authority. These reports help authorities monitor the ongoing compliance of AI systems and address any emerging risks.
• Annual Reports: National authorities must submit annual reports to the EAIB on the use, risks, and incidents related to AI systems within their jurisdiction. This information helps the EAIB in its coordination and monitoring role.

Regulatory Sandboxes (Article 19):

• The AI Act encourages Member States to establish regulatory sandboxes for AI systems, which are controlled environments where AI providers can test innovative solutions under the supervision of regulatory authorities.
• Purpose: Sandboxes allow providers to experiment with new AI technologies in a way that ensures compliance with the AI Act, while also fostering innovation and allowing regulators to gain insights into emerging technologies.
• Conditions: Participation in regulatory sandboxes is voluntary and subject to specific conditions, including requirements related to risk management, transparency, and human oversight.

 

8. International Aspects

This section addresses the international dimension of the AI Act, particularly how it applies to AI systems developed or operated outside the EU but used within its borders. The AI Act has a global reach, ensuring that AI systems affecting EU citizens comply with EU standards, even if they originate elsewhere.

Key Points:

Extraterritorial Application (Article 20):

• The AI Act applies to providers and users of AI systems that are established outside the EU but place AI systems on the EU market or use them within the EU. This provision ensures that AI systems developed abroad are subject to the same rules as those developed within the EU if they have an impact on EU citizens.
• Responsibility of EU-Based Entities: EU-based entities that import or distribute AI systems developed outside the EU are responsible for ensuring that these systems comply with the AI Act.

International Cooperation (Article 21):

• The regulation encourages international cooperation on AI standards and governance, promoting the EU’s approach to trustworthy AI on a global scale.
• Bilateral and Multilateral Agreements: The European Commission is empowered to negotiate agreements with third countries and international organizations to facilitate the exchange of information, cooperation on enforcement, and alignment of AI standards.

Transfer of AI Systems (Article 22):

• The AI Act regulates the transfer of AI systems to third countries, ensuring that such transfers do not compromise the protection of fundamental rights as outlined in the regulation.
• Conditions for Transfer: AI systems can only be transferred to third countries if the receiving entity ensures equivalent levels of protection for fundamental rights and complies with the requirements of the AI Act.

 

9. Annexes

The Annexes of the AI Act provide detailed supplementary information that is critical for the practical implementation and enforcement of the regulation. These sections often include specific lists, technical standards, and procedural guidelines that help clarify and operationalize the broader provisions laid out in the main body of the regulation.

Key Points:

Annex I: List of High-Risk AI Systems

• Classification: This annex provides a detailed list of AI systems that are classified as high-risk under the regulation. These are systems that have a significant impact on individuals' safety, rights, or well-being and thus require strict compliance with the AI Act.
• Examples:
  • Critical Infrastructure: AI systems used in managing critical infrastructure, such as energy or transport networks, where failures could have severe consequences.
  • Educational and Vocational Training: AI systems used to evaluate students or applicants, which could determine access to education or employment.
  • Employment, Workers Management, and Access to Self-Employment: AI systems that make decisions about hiring, performance evaluation, promotion, and termination of employment.
  • Law Enforcement: AI systems used in predictive policing, criminal risk assessments, or surveillance.
  • Biometric Identification and Categorization: AI systems used for biometric identification (e.g., facial recognition) in public spaces.
  • Access to and Use of Essential Private and Public Services: AI systems that determine access to credit, public benefits, or emergency services.

Annex II: Requirements for High-Risk AI Systems

• Technical Documentation: Detailed requirements for the technical documentation that providers must prepare for high-risk AI systems. This includes descriptions of the system's architecture, algorithms, data management processes, and risk management strategies.
• Risk Management: Specific guidelines for implementing a risk management framework throughout the AI system’s lifecycle, including the identification, analysis, and mitigation of potential risks.
• Data Governance: Standards for ensuring data quality, relevance, and representativeness, particularly to avoid bias and ensure the fairness and accuracy of AI systems.
• Transparency and Information Provision: Requirements for making AI systems transparent to users, including clear instructions on the system's capabilities, limitations, and conditions of use.
• Human Oversight: Guidelines for implementing human oversight mechanisms to ensure that AI systems can be monitored and intervened with when necessary, preventing harmful outcomes.

Annex III: Conformity Assessment Procedures

• Self-Assessment: For certain high-risk AI systems, providers are allowed to conduct internal conformity assessments to verify compliance with the AI Act’s requirements.
• Third-Party Assessment: For other high-risk AI systems, an external conformity assessment by a notified body is mandatory. This annex outlines the procedures for such assessments, including the roles and responsibilities of the notified bodies.
• Post-Market Surveillance: Detailed procedures for the ongoing monitoring and surveillance of AI systems after they have been placed on the market. This includes guidelines for reporting incidents and updating the AI system in response to new risks or regulatory changes.

Annex IV: Standards and Specifications

• Harmonized Standards: This annex lists the harmonized European standards that AI systems should comply with to meet the requirements of the AI Act. These standards are developed by recognized European standardization organizations and cover various aspects of AI system development, such as safety, transparency, and data governance.
• Technical Specifications: In the absence of harmonized standards, this annex provides technical specifications that can be used as a reference for compliance. These may include guidelines on algorithm design, data handling, and system security.

Annex V: Registration of High-Risk AI Systems

• EU-Wide Database: This annex details the process for registering high-risk AI systems in the EU-wide database managed by the European Artificial Intelligence Board (EAIB). It includes the information that must be provided during registration, such as the system’s purpose, risk classification, and conformity assessment results.
• Reporting Obligations: Guidelines on the reporting obligations for providers and users of high-risk AI systems, including how to report serious incidents or breaches of compliance.

Annex VI: List of Prohibited Practices

• Detailed Description: This annex provides a comprehensive list of AI practices that are banned under the AI Act. Each prohibited practice is described in detail, including the rationale for its prohibition and the specific risks it poses to individuals or society.
• Examples:
  • AI systems that manipulate human behavior in ways that are harmful or deceptive.
  • AI systems that exploit vulnerabilities of specific groups (e.g., children, disabled persons).
  • AI systems used for social scoring by public authorities or private entities, leading to discriminatory outcomes.

 We remark that, The AI Act represents a significant regulatory framework aimed at ensuring that AI systems developed, marketed, or used within the EU are safe, transparent, and aligned with fundamental European values, including the protection of human rights and the promotion of trustworthy AI.

 

AI Act: A Citizen's Perspective

The European AI Act represents a significant step towards safeguarding the rights, freedoms, and safety of citizens in the face of rapidly advancing artificial intelligence technologies. From a citizen's point of view, the AI Act provides several important protections and assurances:

1. Protection of Fundamental Rights

• Human-Centric AI: The AI Act is grounded in the principle that AI systems must serve people, not the other way around. This ensures that AI technologies are designed and used in ways that respect human dignity, autonomy, and the fundamental rights enshrined in the European Union’s legal framework.
• Prohibition of Harmful AI Practices: The Act explicitly bans AI systems that can manipulate or exploit individuals in harmful ways. For example, AI systems that use subliminal techniques to influence behavior without a person’s conscious awareness, or those that exploit vulnerable groups such as children, are strictly prohibited. This ensures that citizens are protected from technologies that could otherwise harm their physical or psychological well-being.

2. Transparency and Awareness

• Right to Know: Citizens are given the right to know when they are interacting with an AI system. Whether it’s through online platforms, customer service, or automated decision-making tools, the AI Act mandates that these systems must clearly disclose their nature as AI. This transparency empowers individuals to make informed decisions about their interactions and engagements with AI technologies.
• Explainability of AI Decisions: In scenarios where AI systems make decisions that impact individuals—such as determining eligibility for services, loans, or even employment—citizens are entitled to understand the reasoning behind these decisions. The AI Act requires that these systems be designed to provide clear, understandable explanations, thereby reducing the risk of opaque or biased decision-making.

3. Safety and Accountability

• High-Risk AI Systems: For AI systems deemed high-risk—such as those used in healthcare, law enforcement, or critical infrastructure—the AI Act imposes stringent safety and accountability measures. Citizens can feel assured that these systems are subject to rigorous testing, ongoing monitoring, and strict oversight to ensure they operate safely and fairly.
• Recourse and Redress: If an AI system causes harm or operates in a way that infringes on a person’s rights, the AI Act ensures that citizens have clear avenues for recourse. Individuals can report incidents, lodge complaints, and seek redress through national supervisory authorities, which are empowered to take corrective actions and impose penalties on non-compliant entities.

4. Privacy and Data Protection

• Safeguarding Personal Data: AI systems often rely on vast amounts of data, including personal information. The AI Act reinforces existing EU data protection laws by ensuring that AI systems processing personal data do so in a way that respects privacy rights. Citizens can expect that their data will be handled with care, security, and integrity, minimizing risks such as unauthorized access or misuse.
• Biometric Data Protections: Given the sensitivity of biometric data, the AI Act places specific restrictions on AI systems that use such data for identification or categorization purposes. This includes stringent controls on the use of facial recognition technologies in public spaces, limiting their application to exceptional cases of significant public interest, such as preventing terrorism.

5. Empowerment Through AI Literacy

• AI Literacy Initiatives: The AI Act encourages the development of AI literacy programs, ensuring that citizens are equipped with the knowledge to understand, interact with, and critically assess AI systems. These initiatives aim to empower individuals by providing them with the tools to navigate the AI-driven aspects of modern life, from understanding AI in consumer products to recognizing their rights in digital environments.

6. Public Consultation and Engagement

• Involvement in AI Governance: The AI Act promotes the idea that citizens should have a voice in how AI technologies are governed. Through public consultations and participatory mechanisms, individuals can contribute to shaping the policies and standards that will influence the development and deployment of AI systems. This ensures that the governance of AI is not solely in the hands of technologists and policymakers but reflects the broader societal values and concerns.

This comprehensive framework established by the AI Act is designed to protect and empower citizens as they navigate an increasingly AI-driven world. It ensures that while AI technologies continue to advance, they do so in ways that are safe, transparent, and aligned with the values of European society.

 

AI Act: A Government and Institutional Perspective

From the viewpoint of governments and institutions, the European AI Act serves as a robust regulatory framework designed to manage the development, deployment, and oversight of artificial intelligence across the European Union. The Act provides a structured approach to ensure that AI technologies align with public policy objectives, safeguard citizens' rights, and promote innovation within a controlled and ethical environment.

1. Regulatory Framework and Compliance

• Harmonization Across Member States: One of the primary objectives of the AI Act is to establish a uniform regulatory environment across the EU. This harmonization prevents fragmentation within the internal market, ensuring that AI systems can be developed, marketed, and used under consistent rules, regardless of the Member State. For governments, this facilitates easier cross-border cooperation and reduces the complexity of enforcing AI regulations.
• Conformity Assessments: Governments are tasked with ensuring that high-risk AI systems undergo rigorous conformity assessments. These assessments, either conducted internally by providers or through third-party bodies, ensure that AI systems comply with the strict safety, transparency, and ethical standards mandated by the AI Act. This system helps maintain public trust in AI technologies and ensures that only compliant and safe AI systems enter the market.

2. Institutional Oversight and Enforcement

• National Supervisory Authorities: Each Member State is required to establish or designate one or more national supervisory authorities responsible for enforcing the AI Act. These authorities are empowered to monitor AI systems, conduct investigations, and impose penalties for non-compliance. The role of these authorities is crucial in maintaining the integrity of AI deployments and ensuring that providers and users adhere to the regulations.
• European Artificial Intelligence Board (EAIB): The AI Act establishes the EAIB as a central body to coordinate the enforcement of the Act across the EU. The EAIB ensures consistency in the application of the regulation, provides guidance to national authorities, and facilitates cooperation between Member States. For governments, the EAIB serves as a vital resource and partner in managing the challenges associated with AI governance.
• Market Surveillance: Governments are responsible for market surveillance to ensure that AI systems placed on the market are safe and compliant. This includes the authority to conduct inspections, mandate corrective actions, and, if necessary, remove non-compliant AI systems from the market. Effective market surveillance protects citizens and reinforces the credibility of the regulatory framework.

3. Promotion of Innovation and Ethical AI

• Regulatory Sandboxes: To foster innovation while ensuring compliance, the AI Act encourages the creation of regulatory sandboxes. These controlled environments allow AI providers to test and develop new technologies under the supervision of regulatory authorities. For governments, these sandboxes are instrumental in balancing the need for technological advancement with the obligation to protect public interests. They also provide insights into emerging technologies and help refine regulatory approaches.
• Support for SMEs and Startups: Recognizing the role of small and medium-sized enterprises (SMEs) and startups in AI innovation, the AI Act includes provisions that offer these entities specific support. This includes tailored guidance on compliance and easier access to regulatory sandboxes. Governments are encouraged to provide additional resources and support to these enterprises, ensuring that they can compete in the AI market while adhering to the highest standards of safety and ethics.

4. International Cooperation and Global Leadership

• Extraterritorial Application: The AI Act has a global reach, applying not only to AI systems developed within the EU but also to those that are marketed or used within the EU, regardless of their origin. For governments, this extraterritorial application ensures that AI systems affecting EU citizens are subject to EU standards, thereby preventing regulatory arbitrage and protecting citizens from potentially harmful technologies developed abroad.
• Global Standards and Diplomacy: The AI Act positions the EU as a global leader in AI governance. Governments and institutions are encouraged to engage in international dialogues and negotiations to promote the EU’s approach to AI regulation globally. This not only helps in setting international AI standards but also ensures that European values, such as human rights and ethical AI use, are reflected in global AI governance frameworks.

5. Data Governance and Privacy Protection

• Alignment with GDPR: The AI Act reinforces existing data protection laws, particularly the General Data Protection Regulation (GDPR). Governments and institutions must ensure that AI systems handling personal data do so in compliance with GDPR. This includes overseeing the use of biometric data and ensuring that AI systems respect individuals’ privacy rights.
• Biometric Data Regulation: Specific provisions within the AI Act regulate the use of biometric data, especially in high-risk scenarios such as biometric identification in public spaces. Governments are responsible for enforcing these provisions, ensuring that the use of such technologies is strictly controlled and limited to scenarios that serve significant public interests.

6. Public Engagement and AI Literacy

• Public Consultation: The AI Act promotes the involvement of citizens in AI governance through public consultations. Governments are encouraged to facilitate these consultations, ensuring that the development and deployment of AI systems are informed by public opinion and societal values. This helps in building public trust and ensuring that AI policies are responsive to the concerns of citizens.
• AI Literacy Programs: Governments are also encouraged to implement AI literacy programs, helping citizens understand and engage with AI technologies. These programs aim to equip individuals with the knowledge to navigate an AI-driven world, making informed decisions and understanding their rights in the digital age.

Conclusion

From a government and institutional perspective, the European AI Act provides a comprehensive framework for managing the opportunities and challenges presented by AI technologies. It empowers national authorities, fosters innovation, and ensures that AI systems are developed and deployed in ways that align with European values and public policy objectives. By implementing the AI Act effectively, governments can protect citizens, promote ethical AI use, and position the EU as a global leader in AI governance.

 

 

 Image source: Goodwin

 

 For a deeper understanding of the European AI Act:

High-level summary of the AI Act

What is the Artificial Intelligence Act of the European Union (EU AI Act)?

Analyzing the European Union AI Act: What Works, What Needs Improvement

AI and Product Safety Standards Under the EU AI Act

 

Exploring the Evolution of Text Classification in Healthcare Discussions

 

Source paper: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7433297

In the ever-evolving landscape of Natural Language Processing (NLP), one of the most exciting developments has been the rise of Transformer models. These models have revolutionized the field much like Convolutional Neural Networks did for computer vision. Our recent study, "From Bag-of-Words to Transformers: A Comparative Study for Text Classification in Healthcare Discussions in Social Media", delves into this paradigm shift, focusing on how different text representation techniques can classify healthcare-related social media posts.

The crux of our research lies in comparing traditional methods like Bag-of-Words (BoW) with state-of-the-art models such as BERT, Mistral, and GPT-4. We aimed to tackle the inherent challenges posed by short, noisy texts in Italian, a language often underrepresented in NLP research.

We employed two primary datasets: leaflets of medical products to train word embedding models and Facebook posts from groups discussing various medical topics. This dual-dataset approach allowed us to test the effectiveness of different text representation techniques thoroughly.

Our findings revealed a clear winner in the form of the Mistral embedding model, which achieved a staggering balanced accuracy of 99.4%. This model's superior performance underscores its powerful semantic capabilities, even in a niche application involving the Italian language. Another standout performer was BERT, particularly when fine-tuned, reaching a balanced accuracy of 92%. These results highlight the significant leap in performance that modern Transformer models offer over traditional methods.

Interestingly, while the classic BERT architecture proved highly effective, our exploration of hybrid models combining BERT with Support Vector Machines (SVM) yielded mixed results. This indicates that while BERT’s contextual embeddings are robust, integrating them with other classifiers requires careful parameter optimization to unlock their full potential.

In contrast, traditional methods like BoW and TF-IDF struggled with the classification task, reaffirming the need for more sophisticated models in handling complex language data. However, word embedding techniques, particularly when paired with rigorous pre-processing, still present a viable alternative, especially in computationally constrained environments.

A particularly exciting aspect of our study was examining GPT-4's in-context learning capabilities. GPT-4 demonstrated impressive adaptability, providing not only high classification accuracy but also nuanced understanding and semantic explanations for its decisions. This ability to perform in-context learning opens new avenues for developing more intuitive and explainable AI systems.

Our research has significant implications for healthcare professionals and researchers. By leveraging advanced NLP models, we can gain deeper insights into patient experiences and public health trends, potentially transforming how we monitor and combat misinformation in medical discussions online.

In conclusion, our study reaffirms the transformative power of modern LLMs in text classification tasks, particularly in challenging contexts like healthcare-related social media posts. As we continue to explore and refine these models, we move closer to harnessing their full potential in real-world applications, ultimately enhancing our ability to process and understand the vast amounts of textual data generated every day.

 

The term "Computational Intelligence" in Japanese: 計算知能 (Keisan Chinou)

Recently, our team had an enriching experience at WCCI 2024 in Yokohama, Japan, where we delved into the fascinating world of Computational Intelligence. In honor of our time there, I want to share a deeper look at the Japanese term for "Computational Intelligence": 計算知能 (Keisan Chinou).

The phrase was written during the gala dinner, mimicking the Japanese ritual that sees writing as a profound meditative moment.

Explaining each kanji forming  "計算知能"

1. 計 (Kei) - Plan, Measure

   • This kanji is often associated with planning, measuring, or computing. It represents the careful consideration and calculation required in computational tasks. The left part of the character, 言, relates to speech or words, indicating systematic communication or planning. The right part, 十, is the number ten, symbolizing completeness or thoroughness in planning.

2. 算 (San) - Calculate, Reckon

   • This kanji signifies calculation and arithmetic. It’s an essential component in mathematical operations. The character consists of 竹 (bamboo) at the top, suggesting the counting rods used in ancient China, and 目 (eye) at the bottom, indicating observation or scrutiny.

3. 知 (Chi) - Knowledge, Wisdom

   • Representing knowledge or wisdom, this kanji emphasizes the intelligence aspect. It is composed of 矢 (arrow) on the left, symbolizing directness or precision, and 口 (mouth) on the right, denoting speech or communication, implying the conveyance of knowledge.

4. 能 (Nou) - Ability, Capability

   • This kanji represents ability or capability, crucial to the concept of intelligence. It combines 月 (moon) on the left, which can signify time or cycles, and 匕 (spoon) on the right, representing the ability to nourish or support, metaphorically relating to capabilities and talents.

When combined, 計算知能 (Keisan Chinou) beautifully encapsulates the essence of Computational Intelligence:

• 計 (Kei) and 算 (San) together emphasize the methodical and precise nature of computation.
• 知 (Chi) and 能 (Nou) highlight the knowledge and capabilities that form the foundation of intelligence.

Stay tuned for more insights and experiences from our journey in the world of Computational Intelligence!

The CIPARLABS team at the World Congress on Computational Intellgience 2024 in Yokohama, Japan

The CIPARLABS team has just returned from WCCI 2024 — the world's largest conference on Computational Intelligence, Artificial Neural Networks, and Fuzzy Systems — held in Yokohama, Japan, from June 29 to July 5.

It was an exciting group experience that combined moments of learning and reflection with convivial moments where everyone could express themselves beyond academic performance.

The conference was organized at a large convention center, the Pacific Conference Center in Yokohama. The organization was lacking both in the paper review process and in logistics. Despite paying a high participation fee, no lunch was provided. The coffee break was meager and of insufficient duration for networking needs, especially in the hall where the poster session was held. Some special sessions were also organized in a haphazard manner. We expected greater participation from sponsors and companies. Apart from the poor logistical and organizational aspects, numerous interesting works were presented. The poster session featured some truly noteworthy works.

 

Our PhD students Sabereh Taghdisi Rastkar and Danial Zendehdel presented two papers at IJCNN (the sub-conference on Artificial Neural Networks) on optimization and prediction problems in the field of smart energy systems, specifically within Renewable Energy Communities. Our CIPARLABS research group is focusing its efforts on this topic. In particular, we are working on the design of an optimized Energy Management System with Computational Intelligence algorithms, complemented by energy variable prediction algorithms based on Deep Learning techniques. The design is conceived to fully comply with the technical requirements of the major European countries developing Renewable Energy Communities according to the European Green Deal and NextGenerationEU funds.

The gala dinner was plentiful, and we had the opportunity to experience Japanese culture both from a culinary perspective and in customs, as is usual at conferences of this kind. The ceremony was thrilling, featuring actors in traditional Japanese costumes writing the words "Computational Intelligence" on a large canvas using Japanese ideograms. It is well known that in Japan, writing is a ritual in itself, a personal and artistic performance worth appreciating.

Japan proved to be a crossroads of cultures as distant as the Western and Eastern ones. We visited Tokyo, a commercial city hosting Buddhist and Shinto temples. Tokyo showcases the contradictions of a country that has experienced numerous influences from China, Korea, and other Asian countries, as well as from the United States. The Japanese are distinguished by their kindness and the rituals related to gratitude, so different from us Westerners. The Japanese are both very serious and very self-ironic, a reflection of the animated characters and manga whose main figures are featured everywhere.

In Yokohama, we had the chance to appreciate the port and waterfront, one of the largest China Towns in Japan, and the nightlife area, one of the oldest in the city.

In Tokyo, among many places, we saw crowded districts like Shibuya and visited the old quarter full of small venues for nightlife. We also visited the Sensō-ji (金龍山浅草寺, Kinryū-zan Sensō-ji), a Buddhist temple complex located in the Asakusa district.

 

With the limited time available, we also visited Karakura, another area rich in Japanese gardens and Shinto and Buddhist temples.

 

 

  

  

In conclusion, going to Japan and participating in such an event was well worth it. We hope to repeat such experiences in the future.

See you at IJCNN 2025, which will be held in our home country, Rome, at the end of June 2025.

Summary of "Human versus Machine Intelligence: Complexity and Quantitative Evaluation" by Enrico De Santis et al.

 

Introduction

The paper addresses the increasing presence of machine-generated texts in various domains, contrasting them with human-generated texts. It introduces methods to quantitatively evaluate and compare the complexity and characteristics of these texts.

1. Background and Motivation

The section explores the motivation behind comparing human and machine intelligence through text analysis. It highlights the importance of understanding the intricacies of machine-generated texts, especially with advancements in NLP technologies such as GPT-2.

2. Complexity Measures

This section outlines various complexity measures used to evaluate texts. These measures include:

• Hurst Exponent (H): Indicates the long-term memory of the text.
• Recurrence Rate (RR): Measures the frequency of repetitive patterns.
• Determinism (DET): Captures the predictability of the text.
• Entropy (ENTR): Reflects the randomness.
• Laminarity (LAM): Measures the tendency of the text to form laminar patterns.
• Trapping Time (TT): Indicates the duration of repetitive patterns.
• Zipf’s Law Parameters: Analyzes the frequency distribution of words.

3. Data Collection

The corpus comprises 212 texts divided into three categories: English literature (ENG), machine-generated texts by GPT-2 (GPT-2), and programming codes (LINUX). Each text is represented by a feature vector derived from the complexity measures.

4. Methodology

The authors employ a Support Vector Machine (SVM) for classification tasks to discriminate between the three text categories. The feature vectors are normalized, and a genetic algorithm optimizes the SVM's hyperparameters.

 

5. Experimental Results

The results indicate that the complexity measures effectively differentiate between human and machine-generated texts. The SVM achieves high accuracy, demonstrating the distinct characteristics of the three categories. The dendrogram analysis reveals the closeness of novels with GPT-2 texts compared to programming codes.

6. Discussion

The discussion emphasizes the relevance of the complexity measures in characterizing different types of texts. It highlights the potential of these measures to serve as indicators of text originality and authorship. The authors suggest that further research could explore the application of these measures in various domains, including plagiarism detection and content authenticity verification.

7. Conclusion

The paper concludes by affirming the utility of complexity measures in distinguishing human and machine intelligence. It underscores the need for continued exploration of these metrics to enhance our understanding of machine-generated texts and their implications.

Final Resume and Main Considerations

The authors conclude that complexity measures offer a robust framework for distinguishing between human and machine-generated texts. The study demonstrates that features such as entropy, determinism, and Zipf's law parameters are effective in capturing the inherent differences in text structure and complexity. The use of SVM for classification further validates the distinctiveness of these features.

The main considerations from the paper are:

• Quantitative Evaluation: Complexity measures provide a quantitative approach to evaluating and comparing texts, bridging the gap between qualitative assessments and statistical analysis.
• Machine Intelligence Understanding: The study enhances our understanding of how machine-generated texts differ from human texts, contributing to the broader field of AI and machine learning.
• Future Research: There is significant potential for applying these measures in various practical applications, including detecting machine-generated content, verifying content authenticity, and studying the evolution of machine intelligence.

The authors advocate for continued research into complexity measures and their applications, emphasizing their relevance in the ever-evolving landscape of artificial intelligence and natural language processing.

 

Source paper: https://www.computer.org/csdl/journal/tp/2024/07/10413606/1TY3NewpqGQ

 

Summary of "ATMAN: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation" by Björn Deiseroth et al.

 

Abstract

The paper introduces ATMAN, a method to provide explanations for predictions made by generative transformer models with minimal additional computational cost. Unlike existing methods that rely on backpropagation and require substantial GPU memory, ATMAN uses a perturbation method that manipulates attention mechanisms, producing relevance maps efficiently.

1. Explainability through Attention Maps

• 1.1 Generalization in Transformers

  • Transformers have become central in NLP and Computer Vision due to their ability to generalize across tasks.
  • Explainability is crucial to understanding these models, which are becoming increasingly complex and resource-intensive to train and deploy.

• 1.2 Perturbation vs. Gradient-Based Methods

  • Existing explainability methods rely heavily on backpropagation, leading to high memory overheads.
  • Perturbation methods, though more memory-efficient, have not been widely adopted for transformers due to computational impracticality.

• 1.3 Introducing ATMAN

  • ATMAN bridges relevance propagation and perturbations by manipulating attention mechanisms, reducing the computational burden.
  • This method applies a token-based search using cosine similarity in the embedding space to produce relevance maps.

2. Related Work

• Explainability in CV and NLP

  • Explainable AI (XAI) methods aim to elucidate AI decision-making processes.
  • In computer vision, explanations are often mapped to pixel relevance, while in NLP, explanations can be more abstract.

• Explainability in Transformers

  • Most methods focus on attention mechanisms due to the transformers' architecture.
  • Rollout methods and gradient aggregation have been used but face challenges in scalability and relevance.

• Multimodal Transformers

  • Multimodal transformers, which process both text and images, present unique challenges for XAI methods.
  • The authors highlight the importance of explainability in these models for tasks like Visual Question Answering (VQA).

3. ATMAN: Attention Manipulation

• 3.1 Influence Functions

  • ATMAN formulates the explainability problem using influence functions to estimate the effect of perturbations.
  • The method shifts the perturbation space from the raw input to the embedded token space, allowing for more efficient computations.

• 3.2 Single Token Attention Manipulation

  • Perturbations are applied by manipulating attention scores, amplifying or suppressing the influence of specific tokens.
  • This method is illustrated with examples showing how different manipulations can steer model predictions.

• 3.3 Correlated Token Attention Manipulation

  • For inputs with redundant information, single token manipulation might fail.
  • ATMAN uses cosine similarity to suppress correlated tokens, ensuring more comprehensive perturbations.

4. Empirical Evaluation

• 4.1 Language Reasoning

  • ATMAN is evaluated on the SQuAD dataset using GPT-J, showing superior performance in mean average precision and recall compared to other methods.
  • Paragraph chunking is introduced to reduce computational costs and produce more human-readable explanations.

• 4.2 Visual Reasoning

  • Evaluated on the OpenImages dataset, ATMAN outperforms other XAI methods in visual reasoning tasks.
  • The scalability of ATMAN is demonstrated with large models like MAGMA-13B and 30B, showing robust performance across different architectures.

• 4.3 Efficiency and Scalability

  • ATMAN achieves competitive performance with minimal memory overhead.
  • The method scales efficiently, making it suitable for large-scale transformer models, as demonstrated in experiments with varying model sizes and input sequence lengths.

5. Conclusion

• ATMAN is presented as a novel, memory-efficient XAI method for generative transformer models.
• The method outperforms gradient-based approaches and is applicable to both encoder and decoder architectures.
• Future work includes exploring the scalability of explanatory capabilities and the impact on society.

Final Resume and Main Considerations

The paper by Deiseroth et al. introduces ATMAN, a memory-efficient method for explaining predictions of generative transformer models. By manipulating attention mechanisms, ATMAN provides relevance maps without the high memory overhead associated with gradient-based methods. The method is evaluated on both textual and visual tasks, showing superior performance and scalability. The authors emphasize the importance of explainability in large-scale models and suggest that ATMAN can pave the way for further studies on the relationship between model size and explanatory power. They highlight the need for continued research into how these explanations can improve model performance and understanding.