Applying the TAII Framework on Tesla Botpublished at the Montreal AI Ethics Institute MAIEI in February 2022
Management Perspective of Ethics in Artificial Intelligence published in Springer AI and Ethics Journal (ISSN: 2730-5961)
Organisations and companies need practical tools and guidelines to kick-off the implementation of Trustworthy Artificial Intelligence (TAI) Systems. The scientific research based TAII Framework takes a holistic approach to identify the systemic relationships of ethics for the company ecosystem and considers corporate values, business models, and common good aspects like the 17 Sustainable Development Goals and the Universal Declaration of Human Rights. The TAII Framework Canvas creates guidance to initiate the implementation of AI ethics in organisations without requiring a deep background in philosophy and considers the social impacts outside of a software and data engineering setting. Orgnizations and Universities do trust the TAII Framework. Contact us for a free online introduction meeting.
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.
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.
New Business, IT- & Digitalisierungs-Guide: KI-Ethik-Workshops, February 2021
Artificial Intelligence EU Conference: AI Research Hub, March 2021
AI4EU Cafe: How to overcome the barrier to kickoff TAII, May 2021
PERFORM European Digital Retail Summit: TAII Framework, June 2021
DigitalCity.Wien: Digitale Montagsrunde, July 2021
Innovation Value Institute: Digital Retail Webinar, September 2021
AI Guild: Trustworthy AI, October 2021
ETAPAS: Disruptive Technologies, November 2021
European Commission: AI Alliance, December 2021