Michal Kosinski Profile

  • Associate Professor in Organizational Behavior at Stanford University Graduate School of Business.

  • A leading expert in the field of psychology, technology and big data.

  • He has been featured in prominent news outlets such as The New York Times or The Wall Street Journal and in documentaries alongside Elon Musk and Ray Kurzweil.
  • Michal is a Professor in Organizational Behavior at Stanford University Graduate School of Business. He has given talks to most demanding audiences, including the German, Russian and Mexican parliaments, audiences at Chatham House, Swiss Economic Forum, Saint Petersburg International Economic Forum, European Parliament, and C-level leaders of dozens of the world’s largest companies, such as Boeing, Microsoft, P&G, InfoSys, Raiffeisen, Sberbank, China Merchants Bank, or McKinsey.

    Michal studies humans in a digital environment using cutting-edge computational methods, AI and Big Data. He has published over 80 peer-reviewed papers in leading journals including Proceedings of the National Academy of Sciences, Machine Learning, and Psychological Science that have been cited over 11,000 times. His research inspired a cover of The Economist, a 2014 theatre play “Privacy”, multiple TED talks, a video game, and was discussed in thousands of books, press articles, podcasts, and documentaries.

    Michal was behind the first press article warning against Cambridge Analytica, a story covered in Steven Levy’s book “Facebook: The Inside Story”.

    Michal holds a doctorate in psychology from the University of Cambridge and master’s degrees in psychometrics and in social psychology. He was the Deputy Director of the University of Cambridge Psychometrics Centre, a researcher at Microsoft Research, and a post-doctoral scholar at Stanford’s Computer Science Department.

    Michal Kosinski Speaking Videos

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    Michal Kosinski's Speech Descriptions

    The End of Privacy.

    Predicting personality from human face.

    Open Data, Machine Learning and AI.

    Social influence on social media.

    Navigating the AI Revolution: Impacts on Organizations, Societies, and Individuals

    Artificial Intelligence (AI), particularly Large Language Models (LLMs), has made significant strides in recent years, transforming the way we live and work. This talk delves into the wide-ranging implications of AI for modern leaders, organizations, societies, and individuals. By understanding the capabilities and limitations of these advanced models, leaders can harness their potential for decision-making, creativity, collaboration, and problem-solving. We will address critical questions surrounding the ethical, economic, and social aspects of LLMs and AI, exploring their impact on the workforce, privacy, accountability, and the future of human-AI interactions. Join us for an engaging and thought-provoking discussion on navigating the AI revolution and shaping a better future for all stakeholders.

    Harnessing the Power of Artificial Intelligence to Improve Decision-Making

    Artificial intelligence (AI) is increasingly used to make decisions affecting individuals, societies, and organizations. Modern leaders need to understand the opportunities and challenges related to AI's growing importance. In this talk, we will discuss a number of important and timely questions pertaining to AI: How does it work? What is fueling its growing popularity? How is it transforming industries and business models? Can it be creative? Can it make fair decisions? Can AI decisions be interpreted? When is AI better than human decision makers? When will AI take over the world?

    The End of Privacy

    A growing proportion of human activities―such as social interactions, entertainment, shopping, and gathering information―are now mediated by digital devices and services. Such digitally mediated activities can be easily recorded, offering an unprecedented opportunity to study and assess psychological traits using actual (rather than self-reported) behavior. Our research shows that digital records of behavior―such as facial images, samples of text, Tweets, Facebook Likes, or web-browsing logs―can be used to accurately measure a wide range of psychological traits. Such predictions do not require participants' active involvement; can be easily and inexpensively applied to large populations; and are relatively immune to misrepresentation. Consequently, the predictability of psychological traits offers a promise to improve research and practice in fields ranging from psychology, sociology, and education to management and marketing. However, if applied unethically, the same models pose unprecedented risks to the privacy and well-being of entire societies.

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