Marek HavrdaMarek Havrda is Director of AI Policy & Social Impacts at GoodAI, a private AI R&D company. Marek is a member of ONE AI (OECD Network of Experts on AI). He also actively contributes to international AI ethics and governance initiatives including IEEE Ethically Aligned Design, Global Governance of AI Roundtable, International Congress for the Governance of AI and AI4EU. He is a member of the Regulatory Impact Assessment Board of the Czech Government. He served as an advisor to the Czech Ministry of Finance and Ministry of Education. Marek is on a long-term personal leave from the European Commission, where he focused on Impact Assessment, Behavioral Insights, consumer protection, sustainability, and innovation policy. He held leadership or consultancy positions at non-profit and private sectors. He studied at Charles University, Central European University, Warwick Business School, Johns Hopkins University and Georgetown University.
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Covid-19 provides „Sputnik Moment“ not only in terms of our technical and organizational preparedness to deal with large impact situations, but maybe even more importantly in terms of values.
When expressed in monetary terms, McKinsey found that the pandemic’s negative impact on well-being was almost 3.5 times the losses in GDP. The calculation was based on the amount of money people would need to be given to offset the perceived decline in happiness. The main contributing factor was reduced satisfaction with relationships.
The well-being lens might guide our reflection on values. To build on a well-being approach also means to deal with many uncertainties and trade-offs. AI could help us develop and assess scenarios in terms of their potential to increase well-being and also alert us to various trade-offs.
I suggest we start with scenarios of automation. To-date the main driver of automation has been economics. The aim would be to focus public investments in research and innovation towards automating tasks which have the highest potential in terms of well-being.
Also directly related to automation, the scenarios could help guide publicly financed job creation support and related parts of fiscal stimulus packages, in order to focus concrete upskilling efforts in line with expected impact on the well-being of employees.
Such AI-powered scenarios would allow us to discuss potential futures, their impacts and related probabilities. This approach would also significantly increase our ability of quantitative decision-making. Our communities and their leaders would become empowered to discuss expected benefits as well as costs of todays’ actions
When expressed in monetary terms, McKinsey found that the pandemic’s negative impact on well-being was almost 3.5 times the losses in GDP. The calculation was based on the amount of money people would need to be given to offset the perceived decline in happiness. The main contributing factor was reduced satisfaction with relationships.
The well-being lens might guide our reflection on values. To build on a well-being approach also means to deal with many uncertainties and trade-offs. AI could help us develop and assess scenarios in terms of their potential to increase well-being and also alert us to various trade-offs.
I suggest we start with scenarios of automation. To-date the main driver of automation has been economics. The aim would be to focus public investments in research and innovation towards automating tasks which have the highest potential in terms of well-being.
Also directly related to automation, the scenarios could help guide publicly financed job creation support and related parts of fiscal stimulus packages, in order to focus concrete upskilling efforts in line with expected impact on the well-being of employees.
Such AI-powered scenarios would allow us to discuss potential futures, their impacts and related probabilities. This approach would also significantly increase our ability of quantitative decision-making. Our communities and their leaders would become empowered to discuss expected benefits as well as costs of todays’ actions
