A turning point in the digital economy
Digitalisation has been a driving force of structural transformation for the last 20 years disrupting economic sectors like retail and media and transforming towns like Birmingham and Montreal into smart cities. Digital technologies powered by smart phones, cloud computing and artificial intelligence have boosted economic growth, increased the range of choice we have and shifted customer experience to seamless services. The forced living mode of the last 12 months has pushed almost the entire world into digitalization and it is now hard to imagine how shopping, work or learning would look like without digital platforms.
Digital transformation has been dominated by a narrative of access, connectivity and abundance with Silicon Valley big techs on the forefront. Nevertheless recent films like The Social Dilema and The Great Hack have raised serious issues around market concentration, value creation and extraction and data ownership (and use) by digital platforms. Digital business models operate by leveraging network effects (the more users, the more valuable they become then the expression “the winner takes it all”) and with the ability to extract, analyse and manage big data (the smart side of digitalisation). The world’s top digital platforms are highly concentrated geographically with US followed by China, both capturing 80% of the global market capitalization, while Africa and Latin America together accounting for only 1%. Data explored by digital technologies and boosted by artificial intelligence has become the most valuable commodity at the expense of asymmetric business models where “if we are not paying for the product, we are the product”, not to mention the misuse of data for political purposes. While digitalization has grown exponentially, the same has not happened with digital skills, as rapid advances in digital related technologies are happening in ever shorter cycles, changing the nature of the jobs that need to be done. “Talent is a precious resource” said Klaus Schwarb last December during an Amazon virtual event. Digital skills gap are affecting professional career prospects and business productivity as it has been hard to find and form digital talents. Working relationships between the “self-employed” and digital platforms have also been in the spotlight with gig economy precariat professionals protesting in China and more recently 70,000 Uber workers in the UK entitled to workers rights as part of a global upsurge of 39 legal cases for collective rights and social safeguards.
During 2021 governments should be seriously engaged in recovery programmes, like the one suggested by the Mayor of London, to promote immediate responses around the safety net, employment support and business resilience, whilst dealing with medium to long term challenges like the green new deal (transition towards a low carbon city), mental health and inclusive digitalisation. The latter implies that all actors (firms, government departments, civil society) need somehow to be involved in the policy making process and there is an emerging consensus on a combination of economic regulation, competition rules and data sovereignty. An innovative set of policy packages in this area may lead to more balanced outcomes and distribution of the gains generated by digitalisation..
The first set of policies regards opening up data standards (“the black box” of digital platforms) for collective use to increase public value. Following the adoption of the European GDPR (General Data Protection Regulation) in 2018 and its influence in many other countries own legal frameworks, in an attempt to establish minimum rights of individuals to opt in / out the use of personnel data, policies are now moving into a more controversial ground. The policy of Data Commons (or Data Collaboratives) advocates a new city-level data strategy that has transparency, security and ethical use of data with the support of data infra-structure (open standards, open API-application programming interface). Data Commons could be drawn on broadly dispersed data from government agencies, businesses, social organisations and communities and can gather together otherwise siloed data in areas like mobility, security and health in a fair and transparent manner. They can also operate as a national data fund co-owned by citizens under a licensing arrangement and business should adopt a “pay per use” scheme to access a highly regulated environment. Since 2017 the city of Barcelona has been adopting Data Commons that not only opens real time data for citizens and business but also requires that every business in procurement contracts with the municipality make their algorithms public. In 2019 the municipality of Toronto pushed back a smart city initiative called Sidewalk Labs (led by Google and championed by Prime Minister Justin Trudeau), amid skepticism over privacy, “surveillance capitalism” and techno dystopia of cameras and other sensors continuously collecting data and monitoring individual behaviors, reinforcing the need for an inclusive data strategy. Social pressures arise with collaborative initiatives like Decode and the Cities for Digital Rights, both an attempt to empower individuals to be in control of whether they keep their personal information private or share it for the common good.
Another set of policies argues that Artificial Intelligence (and its derivations into Machine and Deep Learning) should be treated as a general purpose technology (like the Internet) and deserves a proper governance system as a “force for good”. Dr Carina Prunkl from the Centre for the Governance of AI suggests that “human autonomy in the context of AI is our ability to remain in control over our lives as more and more tasks are outsourced to AI systems (…) governance responses are appropriate to respond to these potential impacts.” AI governance should then be designed to reduce social inequality, eliminate discrimination and increase social justice not mention the need for A.I. to act responsibly against job losses (technology should serve people, not the other way around). Transparency, accountability (having someone responsible for the decisions made by the algorithm) and evaluating and monitoring algorithms for effectiveness, risk and bias should be some of the guiding principles for a proper A.I. governance.
Those policy issues will require bold agency and tapping into controversial rather opaque areas so far the domain of coders, data scientists and data engineers who have silently designed and deployed powerful algorithms to collect big data under the “mass transformative purpose” of digital platforms. Increasing digitalization in the context of a post-covid recovery programme urges a call for action, a turning point for policy makers to reorient digital transformation towards more inclusive, open and fair infra-structures, guidelines and standards within a “New Deal” for data and AI.
