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about

about



is a social scientist, bestselling author, board member, and award-winning entrepreneur. Her work and post-doctoral research are focused on how technology interacts with people, organizations, and the planet.

Her 2024 book on artificial intelligence, co-written with Prof. Dr. Miriam Meckel, 
"Alles überall auf einmal: Wie Künstliche Intelligenz unsere Welt verändert und was wir gewinnen können" became an instant national bestseller

For nearly a decade, she has explored the profound impacts of emerging technologies like AI. Her publications cover topics such as automated facial recognition, synthetic voice generation, quantum computing, as well as her PhD research on her original framework of Code Capital. With her deep expertise, Léa serves on the Board of Directors of Weleda AG and as an AI Advisory Board Member of Ringier AG, one of Switzerland's leading media companies. Additionally, she lectures at the University of St. Gallen, teaching courses like "Social and Economic Impacts of Artificial Intelligence" and "AI Governance: Perspectives from Asia". 

In 2018, she co-founded and is now the Executive Chairwoman of ada Learning, a startup that accelerates workforce transformation by up-skilling 
employees to drive innovation from within. Since its inception, ada has equipped thousands of employees from DAX companies, SMEs, governments, and civil society organizations with the necessary transformation skills. 

Previously, she served as the Chief Innovation Officer of WirtschaftsWoche, Germany's leading business magazine, where she also covered the future of work and socio-technological change. Prior to joining Handelsblatt Media Group, she worked with social justice NGOs in Bosnia-Hercegovina, Rwanda, and the Democratic Republic of Congo. She was selected as a Forbes 30 Under 30 leader, one of Medium Magazine’s Top 30 Under 30 journalists, a BCG Thought Leader, and an Atlantik Brücke Young Leader. In 2011, she was awarded the Henry Richardson Labouisse Prize
 for independent research
 
Léa holds degrees from Princeton University (A.B.), the Harvard Kennedy School of Government (MPP), and the University of St. Gallen (PhD). 
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Léa Steinacker

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speaking

speaking

Léa regularly shares her expertise in keynotes and lectures, enabling the audience to think deeply about the impact of transformative technologies. With a focus on the implications of artificial intelligence, she provides wide-ranging insights on the field's foundations, ethics, and business implications. She covers, among other topics, how AI may transform the workforce and reshape jobs, how it can impact productivity and decision-making, how organization can unlock AI's potential through up-skilling, and what practices are critical in governing AI to ensure that it is deployed in a responsible and equitable manner. ?

For speaking inquiries, please use the contact form below. 

books

books
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research

research

ACADEMIC PAPERS 

Ma, C., Poehlmann, G., Meckel, M., Steinacker, L. (2024). Narratives from Commercial, Business-Focused, Industrial Reports and Articles on Quantum Technology. Proceedings of the 84th Annual Meeting of the Academy of Management, Chicago.

Rietsche, R., Dremel, C., Bosch, S., Steinacker, L., Meckel, M., & Leimeister, J. M. (2022).
Quantum computing. Electronic Markets, 1-12. 

Kostka, G., Steinacker, L., & Meckel, M. (2022).
Under big brother's watchful eye: Cross-country attitudes toward facial recognition technology. Government Information Quarterly, 101761.

Suter, V., Meckel, M., Shahrezaye, M., & Steinacker, L. (2022, January).
AI Suffrage: A four-country survey on the acceptance of an automated voting system. In Proceedings of the 55th Hawaii International Conference on System Sciences.

Kostka,G.; Steinacker, L.; Meckel, M. (2021). 
Between Security and Convenience: Facial recognition technology in the eyes of citizens in China, Germany, the United Kingdom, and the United StatesPublic Understanding of Science.

Steinacker, L; Meckel, M.; Kostka, G. & Borth, D. (2020). 
Facial Recognition: A cross-national Survey on Public Acceptance, Privacy, and Discrimination. Proceedings of the 37th International Conference on Machine Learning Law and ML Workshop, Vienna.
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