In the last decades, the world economy is facing a massive rise in automation, robotics and Artificial Intelligence (AI) which, according to some analysts, could lead to significant job losses or job polarization and hence widen income and wealth disparities. This scenario may impede the achievement of the Sustainable Development Goal 8 (SDG 8). In this context, the role of government and regulation becomes crucial in order to prevent an undesirable scenario, where technological change, namely automation and AI, comes at the cost of mass unemployment and growing inequality. This paper focuses on the role of taxation as a possible tool for sharing the gains from automation and AI. Nowadays, advances in technology may have a direct impact on tax systems, which should be re-adapted to take into account new forms of jobs and new business models. The paper discusses pros and cons of several possible solutions and then compares progresses achieved in different countries. Concerning robot tax and digital taxes there are already some concrete steps undertaken both at national and international level, while other proposals remain still nebulous. Of course, taxation per se, and any single policy in general, is not sufficient to achieve a more inclusive and equal growth. It is instead crucial to create synergies across policies and a strong link between employment creation strategies, redistributive policies, skill development and social protection systems.

This opinion piece is forthcoming in Il Sole 24 Ore. The Muller-Lyer optical illusion consists of two lines of equal length that differ only in the direction of arrowheads at either end. Yet, to most observers, the line with arrowheads pointing outwards looks longer than the other. If you grew up in and among buildings with straight walls and 90 degree angles, you have learned to perceive lines according to geometric patterns. Your view of the Muller-Lyer lines is biased.

The collaborative efforts are the key to accelerate technology diffusion by promoting innovations that democratise the access of new technologies, enhancing research and development in AI that address the issues of data protection, transparency and accountability so that it gains public trust and encourages greater investment.

From relentless automation to algorithmic bias and human rights abuses, artificial intelligence (AI) has a laundry list of well-known potential costs and risks that do not bode well for the future of inclusion. Take Amazon's recent foray into mass surveillance, for example. Designed by Amazon Web Services, Amazon Rekognition is a deep learning AI that makes it easier for businesses to add image and video analysis to their applications. Common use cases include searchable image and video libraries, face-based user verification, and unsafe content detection. But documents obtained by the American Civil Liberties Union (ACLU) of Northern California show that Amazon is now supplying its facial recognition technology to governments and law enforcement agencies, as well. Last month, a coalition of over 70 civil liberties and human rights organizations signed a letter that calls on Amazon to stop powering the government's surveillance infrastructure.

In a'national strategy for artificial intelligence', prepared to give the country an edge in this area, Niti Aayog suggested ways to promote adoption of …

The future of work looks full of promise. Combining human brainpower with artificial intelligence, virtual reality and automatization will revolutionize how we work. "The future of work looks full of promise." Already, robotic enhancement is helping humans exceed their natural capabilities. AI is opening the door to real-time, personalized intelligent services, cutting waste and maximizing results.

CAMBRIDGE – How can we improve the state of the world? How can we make countries more competitive, growth more sustainable and inclusive, and genders more equal? One way is to have a correct theory of the relationship between actions and outcomes and then to implement actions that achieve our goals. But, in most of the situations we face, we lack such a theory, or if we have one, we are not sure that it is correct. Should we postpone action until we learn about what works? But how will we learn if we do not act?