Fair Ai Certification
About the certification
Using the bloxberg blockchain (a consortium of research institutions including Max Planck Society, Georgia Tech, Carnegie Mellon, ETH Zurich, UCL, among others) and the Wolfram Language’s blockchain and computational logic functions, we implemented a prototype certification and verification workflow to recognize the efforts of companies that take actions to help mitigate the effects of automation-induced unemployment. The output (blockchain records) could be used as a scientific survey, the first data repository of its kind to track AI-responsible businesses, and can be queried by social scientists, funders in the public and private sectors, and, the general public.
Pilot project: Pandemics, AI, and, the Acceleration of Workforce Displacement
Social Distancing, Lockdowns, and similar mitigation measures prescribed to deal with epidemics and pandemics (such as the one related to the novel Coronavirus/Covid-19) may have long-term effects on the constitution of the fabric of society and the operation of the economy. One of the possible unintended implications is that scenarios of work displaced by automation can materialize faster than it has been projected: this will happen due to market reasons (i.e after having experienced the benefits of operating a remote workforce, companies, and institutions will face competitive and budgetary pressures to become leaner) as well as for reasons of force majeure (as funding and credit find it difficult to circulate across the economy, some furloughed employees will never come back to work). The human cost could be significant, especially if governments and the private sector have to deal with an economic depression in the aftermath. The need to plan and forecast to prepare for such challenges is clear, but the body of knowledge on fair, accountable, and trustable artificial intelligence is in the early stages of development -- and concerned mainly with creating standards and operational rules and methods, less so with creating awareness of the nature of this particular issue, and the policies necessary to deal with such extraordinary events in a macro context. The expected outcome, in the short term, will be to provide the data necessary for policy-makers to create a decision-making framework that can help mitigate the effects of automation-induced unemployment after the pandemic.
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