{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":20,"n_cols":5,"rows_kept":20,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[11,2,11,3],[13,2,13,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9911034477903321"]
[null,null,"Autore",null,"Windholz Natascha"]
[null,null,"Titolo",null,"The AI Act Handbook"]
[null,null,"Pubbl/distr/stampa",null,"München : , : Carl Hanser Verlag, , 2025"]
[{"$nulls":4},"©2025"]
[null,null,"ISBN",null,"9781569903247"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (0 pages)"]
[null,null,"Disciplina",null,"776.028563"]
[null,null,"Soggetti",null,"AI art"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Table of Contents -- Foreword -- 1 What is AI and How Do Data Science and Data Analytics Differ? -- Gabriele Bolek-Fugl -- 1.1 The Cornerstones of AI -- 1.1.1 Data -- 1.1.2 Algorithms -- 1.1.3 Computing Power -- 1.1.4 Storage -- 1.1.5 Measurement and Model Optimization -- 1.1.6 Interfaces for Interaction -- 1.1.7 Security and Data Protection -- 1.2 Data Science and Data Analytics -- 1.3 Development of AI in SMEs -- 2 Geopolitics of Artificial Intelligence -- Veronica Cretu -- 2.1 Emerging Landscape of AI Regulations -- 2.2 The Race for AI Regulation - the Big Three -- 3 AI Act: Rights and Obligations -- Gabriele Bolek-Fugl, Veronica Cretu, Julia Fuith, Merve Taner, Natascha Windholz, Carina Zehetmaier -- 3.1 Introduction to the AI Act -- 3.1.1 Definition of AI systems -- 3.1.2 Roles of Natural or Legal Persons -- 3.1.3 Market Launch Phases -- 3.1.4 Terms for the Use of AI Systems -- 3.1.5 Data-related Designations -- 3.1.6 AI Literacy -- 3.2 AI Literacy for Providers -- 3.2.1 Introduction -- 3.2.2 Definition of AI Literacy -- 3.2.3 AI Literacy and the Provisions of the AI Act -- 3.2.4 Proposal for a Maturity Framework for AI providers -- 3.3 Risk-based Approach -- 3.3.1 Prohibited AI Systems -- 3.3.2 High-risk AI Systems -- 3.3.2.1 Classification of AI as a High-risk AI System -- 3.3.2.2 Annex III -- 3.3.2.3 Requirements for High-risk AI Systems -- 3.4 Fundamental Rights Impact Assessment -- 3.4.1 AI Act and Fundamental Rights -- 3.4.1.1 Implementation of the Fundamental Rights Impact Assessment -- 3.4.1.2 Impact Assessment as Part of AI Governance -- 3.4.1.3 Existing Tools for Fundamental Rights Impact Assessments -- 3.5 Harmonized Standards, Conformity Assessment, Certificates and Registration -- 3.5.1 Harmonized Standards and CE "]
[{"$nulls":4},"Marking -- 3.5.2 Conformity Assessment Procedure -- 3.5.3 Exemptions from the Conformity Assessment Procedure -- 3.5.4 EU Declaration of Conformity -- 3.5.5 Registration -- 3.6 Transparency Obligations in the AI Act -- 3.6.1 Guidelines for the Implementation of Transparency Obligations for Data and Data Management -- 3.6.2 Guidelines for the Implementation of the Transparency Provisions Provided for in Art. 13 AI Act -- 3.6.3 Guidelines on the Implementation of Transparency Obligations for Providers and Suppliers of Certain AI Systems and GPAI Models -- 3.7 General-purpose Artificial Intelligence (GPAI) -- 3.7.1 ChatGPT: the Start of an \"AI revolution\"? - Implications for the Legislative Process -- 3.7.2 Inclusion of GPAI in the AI Act -- 3.7.3 AI Models and AI Systems for General Use -- 3.7.3.1 Classification Rules for GPAI Models -- 3.7.3.2 Commitments -- 3.7.4 GPAI Models with Systemic Risk -- 3.7.4.1 Classification Rules for General-Purpose AI Models with Systemic Risk according to Art. 51 AI Act -- 3.7.4.2 Obligations for GPAI Models with Systemic Risk under Article 55 -- 3.7.5 GPAI Models and High-risk Systems -- 3.7.6 Implementation Period and Penalties -- 3.8 AI Sandboxes -- 3.8.1 Setup and Functionality -- 3.8.2 Further Processing of Personal Data -- 3.8.3 Tests Outside of AI Sandboxes -- 3.8.4 Consent for Tests Outside Sandboxes -- 3.8.5 Facilitation for SMEs -- 3.9 Authorities -- 3.9.1 Notifying Authority -- 3.9.2 Conformity Assessment Bodies and Notified Bodies -- 3.10 Governance in the AI Act -- 3.10.1 AI Office -- 3.10.2 AI Board -- 3.10.2.1 Composition -- 3.10.2.2 Tasks of the AI Board -- 3.10.3 Advisory Forum -- 3.10.4 Scientific Panel -- 3.10.5 National Authorities -- 3.10.6 EU Database for High-risk AI Systems -- 3.10.7 Post-market Monitoring -- 3.10.8 Sharing Information on Serious Incidents -- 3.10.9 Law Enforcement -- 3.10.10 Confidentiality of Procedures -- 3.10.11 Procedures at National Level for dealing with AI Systems presenting a Risk -- 3.10.12 Procedures for AI Systems Classified as Non-high-risk AI by the Provider -- 3.10.13 Compliant AI Systems which present a Risk -- 3.10.14 Formal Non-conformity -- 3.10.15 Legal Remedy -- 3.10.15.1 Right to a Explanation of Decision-making -- 3.10.15.2 Legal Remedies for GPAI -- 3.11 Penalties and Sanctions -- 3.12 SMEs and Start-ups in the AI Act -- 3.12.1 Facilitations and Exemptions for SMEs and Start-ups -- 3.12.2 Checklist: Launching a New AI System in Accordance with the AI Act -- 4 Data Protection -- Gabriele Bolek-Fugl -- 4.1 General Requirements of the GDPR -- 4.1.1 The Principles for Processing Personal Data -- 4.1.2 Lawfulness of Processing -- 4.1.3 Obligation to Provide Information where Personal Data is Collected -- 4.1.4 Rights of the data subjects -- 4.2 Privacy by Design -- 4.2.1 Implementation -- 4.2.2 Responsibility for Processing in Compliance with the Law -- 4.3 Requirements for Testing Data -- 4.4 Automated Decision Making -- 4.5 Guidance and Recommendations on GDPR and AI from Data Protection Authorities -- 4.5.1 Publications of the European Data Protection Board (Excerpt) -- 4.5.2 DSK Recommendations -- 4.5.3 The State Commissioner for Data Protection and Freedom of Information Baden-Wurttemberg -- 4.5.4 Hamburg Commissioner for Data Protection on LLMs -- 4.5.5 FAQ of the Austrian Data Protection Authority -- 4.6 ChatGPT and the Data Protection Complaint from noyb -- 5 Intellectual Property -- Alexandra Ciarnau -- 5.1 Protection of AI and its Components -- 5.1.1 Copyrights and Ancillary Copyrights -- 5.1.1.1 General Information -- 5.1.1.2 Individually Developed AI Systems -- 5.1.1.3 Individually Developed AI Models -- 5.1.1.4 Input and Training Data Pool -- 5.1.1.5 User Documentation and User Manual -- 5.1.1.6 Rights and Claims of the Author -- 5.1.1.7 Granting of Rights -- 5.1.1.8 Open Source Software "]
[{"$nulls":4},"-- 5.1.1.9 Patent and Utility Model Protection -- 5.1.2 Trade Secret Protection -- 5.2 Legal IP Compliance when Using AI -- 5.2.1 AI Input -- 5.2.1.1 IP-protected Input Data -- 5.2.1.2 AI Act Requirements for AI Systems -- 5.2.2 AI Output -- 5.3 Checklist -- 5.4 Reference Table Legislation -- 6 AI and IT Contract Law -- Alexandra Ciarnau, Merve Taner -- 6.1 Licensing of Standard Software -- 6.2 Software Development -- 6.3 Software Maintenance -- 6.4 Open Source Software -- 6.4.1 Open Source AI - Paving the Way for the Future? -- 6.4.2 Definition of Open Source and Legal Basis -- 6.4.3 Legal Problem Areas in Connection with Open Source According to Existing Legal Bases -- 6.4.4 Open Source Software Strategy of the European Commission -- 6.4.5 Exceptions for Open Source in the AI Act -- 6.5 Hardware Purchase and Maintenance -- 6.6 General Information on Liability -- 6.7 Reference Table Legislation -- 7 Private Sector -- Kristina Altrichter, Gabriele Bolek-Fugl, Karin Bruckmuller, Alexandra Ciarnau, Julia Eisner, Isabella Hinterleitner, Manuela Machner, Renate Rechinger, Carina Zehetmaier, Klaudia Zotzmann-Koch -- 7.1 AI - from Prejudice to Discrimination -- 7.1.1 Right to Equality and Non-discrimination -- 7.1.2 How Prejudices Find their Way into AI -- 7.1.2.1 How the AI Act Addresses Discrimination -- 7.1.2.2 Can We Fix Bias in AI? -- 7.2 AI in the Financial Sector -- 7.2.1 Exceptions to the Scope of Application -- 7.2.2 Prohibited AI Systems -- 7.2.3 High-risk AI Systems -- 7.2.3.1 Classification -- 7.2.3.2 Refutation of the High-risk Property -- 7.2.3.3 Interactions between Financial Regulations and the AI Act -- 7.2.4 General Purpose AI Systems/Models -- 7.2.5 Certain AI Systems -- 7.2.6 Authority Competencies -- 7.3 AI in the Insurance Industry -- 7.3.1 Dynamic Underwriting and Risk Assessment in Health Insurance -- 7.4 AI and Whistleblowing -- 7.4.1 Whistleblower for the AI Category -- 7.4.2 Areas of Application of AI in the Implementation of the Whistleblowing Directive -- 7.4.2.1 Challenges in the Whistleblowing Process -- 7.4.2.2 Procedure of the Whistleblowing Use Case -- 7.5 Use of AI in Future and Existing Employment Relationships -- 7.5.1 Writing Job Ads with AI -- 7.5.2 AI Support for Applicant Selection by Means of Video Analysis [4] -- 7.6 AI in Education -- 7.6.1 Roles in the AI Act -- 7.6.2 AI Literacy (Art. 4 AI Act) -- 7.6.3 AI Systems with \"Limited\" Risk (Art.. 505 0 50 AI Act) in Education -- 7.6.4 High-risk AI Systems in Education -- 7.6.5 Prohibited AI Systems in Education -- 7.7 AI in Healthcare."]
[{"$nulls":4},"7.7.1 Example: AI Diagnosis of Skin Diseases -- 7.7.1.1 High-risk AI Classification within the Meaning of the AI Act -- 7.7.1.2 Requirements and Obligations of the Hospital Deployer According to the AI Act -- 7.8 AI in Advertising -- 7.8.1 Legal Requirements for AI in Advertising -- 7.8.1.1 Prohibited AI Systems -- 7.8.1.2 Overlaps with Other Laws -- 7.8.1.3 Data Trading -- 7.8.1.4 Personalization -- 7.8.2 Energy Consumption and Sustainability -- 7.8.3 Best Practice: Generative AI in Creation -- 7.9 Tourism -- 7.9.1 Use case: Operational efficiency -- 7.9.2 Use Case: Guest Experience -- 7.9.3 Use Case: Smart Companies -- 7.10 AI in Autonomous Driving -- 7.10.1 Austrian & International Legislation -- 7.10.2 Development of Autonomous Driving Functions -- 7.10.3 The AI Act and Autonomous Driving -- 8 Public Sector -- Kristina Altrichter, Karin Bruckmuller, Veronica Cretu, Theresa Tisch, Natascha Windholz -- 8.1 \"Public Decision Making\" and AI -- 8.1.1 Use Cases in Annex III AI Act -- 8.1.2 Example: Allocation of Social Benefits -- 8.1.3 Example: Allocation of Kindergarten Spots -- 8.2 AI in Criminal Prosecution -- 8.2.1 Use of Biometric Real-time Remote Identification Systems -- 8.2.2 Implementation Obligations of the Member States -- 8.3 AI in Elections and Democratic Processes -- 8.3.1 Emerging Discussions about the Impact of AI on Democracy and "]
[{"$nulls":4},"Electoral Processes -- 8.3.2 How Should AI be Defined in the Context of Elections? -- 8.3.3 Exploiting Opportunities and Minimizing Risks through the Use of AI -- 8.3.4 AI and Election Integrity: a Hypothetical Analysis of the Cambridge Analytica Scandal in the Context of the AI Act -- 8.4 AI in the NIS Sector -- 8.4.1 Introduction NIS and NIS 2 -- 8.4.1.1 NIS2 -- 8.4.2 Importance of NIS2 for the Supply Chain -- 8.4.3 Use of AI in NIS Companies -- 8.4.3.1 Annex I AI Act -- 8.4.3.2 Annex III AI Act -- 9 Ethics -- Gabriele Bolek-Fugl, Valerie Hafez, Sabine Singer -- 9.1 Ethical Guidelines for Trustworthy AI -- 9.1.1 What is it About? -- 9.1.2 Ethical Principles of the Guidelines -- 9.1.3 Core Requirements -- 9.1.4 Methods for Implementing the Core Requirements -- 9.1.5 Tools for Implementation -- 9.2 Relevant AI Guidelines & Policies -- 9.2.1 OECD Council Recommendation on Artificial Intelligence -- 9.2.2 The Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence -- 9.2.3 Compliance Tools for Many Occasions -- 9.2.4 Artificial Intelligence Risk Management Framework -- 9.2.5 Further Formative Ethical Guidelines -- 9.3 EU and Global Bodies, Boards and Committees -- 9.4 From Digital Humanism to a Value-based AI System -- 9.4.1 Value-based Engineering -- 9.4.2 Advantages and Strategic Importance of Value-based Engineering -- 9.4.3 Conclusion -- 10 Governance in the Company -- Gabriele Bolek-Fugl, Karin Bruckmuller, Veronica Cretu, Valerie Hafez, Klaudia Zotzmann-Koch -- 10.1 Practical Example: Assessment of a Use Case in Accordance with the AI Act -- 10.1.1 Description of the Use Case: AI-supported Fire Detection and Alarm System -- 10.1.2 How do You Start? -- 10.1.3 Conclusion -- 10.2 Risk Management, Human Supervision and Useful Tools -- 10.2.1 Embedding Governance in the Life Cycle of an AI System -- 10.2.2 Recognizing and Addressing Risks -- 10.2.2.1 Approaches to Risks, Incidents, Accidents and Affected Parties -- 10.2.2.2 Measuring Risks -- 10.2.2.3 Responsibility in the Event of Incidents and Accidents -- 10.2.2.4 Perceiving and controlling the unknown -- 10.2.3 Human Supervision -- 10.2.3.1 Break Down Supervision -- 10.2.3.2 Develop and Maintain Supervisory Skills -- 10.2.3.3 Making Supervision Context-sensitive -- 10.2.3.4 Involving External Parties in Supervision -- 10.2.3.5 Human Supervision: Pros and Cons -- 10.2.4 Conclusion -- 10.3 Data and Knowledge Management -- 10.3.1 Pilars of the Data Governance Framework -- 10.4 Audit of Artificial Intelligence -- 10.4.1 Fundamentals of the Audit -- 10.4.2 Audit Team -- 10.4.3 Difference Between Risk Management and Audit -- 10.4.4 Helpful Audit Checklists -- 10.4.5 Example of a Simple AI Audit Checklist -- 10.5 Code of Conduct -- 10.5.1 Example of a Code of Conduct for the Use of Artificial Intelligence in the Organization -- 10.5.2 Further Considerations on the AI Code of Conduct -- 10.6 AI and Sustainability -- 10.6.1 ESG - Environmental, Social and Corporate Governance -- 10.6.2 Diversity, Inclusion, Justice -- 10.6.3 Benefits for the Environment -- 10.6.4 High-risk AI Systems -- 10.6.5 Supply Chains -- 10.6.6 Conclusion -- 11 The Authors -- Index."]
[null,null,null,"Sommario/riassunto","THE AI ACT HANDBOOK //- Detailed overview of the AI Act- Impact of the AI Act on various areas (including fi nance, employment law, advertising and administration)- Related areas of law (data protection, IP and IT law)- Practical overview of AI governance, risk and compliance in companies- Information on standards, norms and certificationsBy experts for practitioners with this handbook, you can prepare yourself for the requirements of the European AI Act in a practical and compliant manner. Get comprehensive information on the effects on the various application fields of artificial intelligence in the private and public sectors. After a brief introduction to the history and technology "]
[{"$nulls":4},"of AI, you will receive a detailed subsumption of the content of the AI Act based on the various risk categories. Subsequently, areas of law closely related to the use of AI, in particular data protection, IP and IT law, will be dealt with in detail. By providing case studies, the book shares insights about the impact of the AI Act on various areas such as autonomous driving, work, critical infrastructure, medicine, insurance, etc. The correlation with the areas of law relevant to these areas will also be considered. A practical overview of the topic of AI governance, risk and compliance (GRC) in companies, tips on the application of guidelines and governance frameworks, implementation ideas for trustworthy AI as well as standards, norms and certifications complement the book.The TEAM OF AUTHORS consists of lawyers specializing in IT and data protection law and the use of AI. It includes, among others, one of Austria's representative in the AI Act negotiations at EU Council level and the founder of the Austrian association Women in AI.FROM THE CONTENTS //- What Is AI and How Do Data Science and Data Analytics Differ?- Geopolitics of Artificial Intelligence- AI Act: Rights and Obligations- Data Protection- Intellectual Property- AI and IT Contract Law- Private Sector- Public Sector- Ethics- Governance in the Company."]