Three different Workshops for AI ethics education and implementation kickoff. Includes a certificate and exercises.
Management Perspective of Ethics in Artificial Intelligence published in Springer AI and Ethics Journal (ISSN: 2730-5961)
Trustworthy AI Implementation (TAII) generates a meta perspective on the systemic dependencies of ethics for the company ecosystem, supporting the Trustworthy Artificial Intelligence approach of the European Commission.
It generates orienteering for the AI ethics kickoff without requiring a deep background in philosophy and considers perspectives of social impact outside the software and data engineering setting.
Iterative dependency cycle of Corporate Values, Business Model, Stakeholder, Risk, Sustainable Development Goals and Human Rights.
Three different online | offline packages. Request the Workshop Program for AI ethics education and company kickoff, including a certificate and exercises. Course content: What is AI ethics? Values and norms. Ethical frameworks. Common good & well-being. Utilitarianism. Accountability. Transparency. Human rights. Data privacy. Safety & robustness. Bias. Fairness & non-discrimination. Ethics-washing. Guidelines & principles. From principles to doing.
One course is available for free after registration. The 4 or 6 hrs workshops AI Ethics Compact/Kickoff include handouts and generate detailed knowledge about AI ethics. The AI Ethics Kickoff workshop guides from theory into doing the first steps for companies or institutions.
Trustworthy AI generates new opportunities to define the value creation and how to create, deliver and capture sustainable value for the business.
The TAII Framework generates a meta perspective on the systemic dependencies of ethics for the company ecosystem.
Innovation and implementation of AI technologies and services within the organization's core business model to strengthen the market position for the future.
The assessment of the seven key requirements reflects and possibly adapts the design and development process of AI systems. Therefore, it generates a dispute with social implications and responsibilities to contribute and shape a good society.
Some machine learning techniques, although very successful from the accuracy point of view, are very opaque in terms of understanding how they make decisions. Non-trustworthy black-box AI systems refer to scenarios, where it is not possible to trace back to the reason for certain decisions.
Prepare and adapt the AI design and development process to simplify the implementation of governmental regulation and certification. Communicate the taken social responsibility and shape a role model within your industry.
European Commission: Proposal for a Regulation on a European approach for Artificial Intelligence European Commission: White Paper on Artificial Intelligence: a European approach to excellence and trust European Commission: Excellence and trust in artificial intelligence European Commission: Ethics guidelines for trustworthy AI European Commission: The European Artificial Intelligence landscape European Commission: The European Digital Strategy European Commission: AI Watch European Data Protection Supervisor: Artificial Intelligence Council of Europe: Artificial Intelligence HUMAINT: Human behaviour and machine intelligence UNICEF: Policy Guidance on AI for Children United Nations : Sustainable Development Goals United Nations: Universal Declaration of Human Rights G20 Insights: A Step to Implementing the G20 Principles on Artificial Intelligence AI4EU Consortium: European Artificial Intelligence On-Demand Platform and Ecosystem DIGHUM: Vienna Manifesto on Digital Humanism Future of Life Institute: Benefits & risks of artificial intelligence HUMANE AI: Human-centered artificial intelligence CIFAR: Artificial Intelligence Policy Initiatives IEEE SA: Raising the Standards in Artificial Intelligence Systems (AIS) UNESCO: Artificial intelligence with human values for sustainable development OECD: Principles on AI OECD: AI Dashboard