Harnessing Data Innovation for Migration Policy: A Handbook for Practitioners

Curated on Posted on by Stefaan Verhulst

Report by IOM: “The Practitioners’ Handbook provides first-hand insights into why and how non-traditional data sources can contribute to better understanding migration-related phenomena. The Handbook aims to (a) bridge the practical and technical aspects of using data innovations in migration statistics, (a) demonstrate the added value of using new data sources and innovative methodologies to analyse key migration topics that may be hard to fully grasp using traditional data sources, and (c) identify good practices in addressing issues of data access and collaboration with multiple stakeholders (including the private sector), ethical standards, and security and data protection issues…(More)” See also Big Data for Migration Alliance.

What AI Means For Animals

Curated on Posted on by Stefaan Verhulst

Article by Peter Singer and Tse Yip Fai: “The ethics of artificial intelligence has attracted considerable attention, and for good reason. But the ethical implications of AI for billions of nonhuman animals are not often discussed. Given the severe impacts some AI systems have on huge numbers of animals, this lack of attention is deeply troubling.

As more and more AI systems are deployed, they are beginning to directly impact animals in factory farms, zoos, pet care and through drones that target animals. AI also has indirect impacts on animals, both good and bad — it can be used to replace some animal experiments, for example, or to decode animal “languages.” AI can also propagate speciesist biases — try searching “chicken” on any search engine and see if you get more pictures of living chickens or dead ones. While all of these impacts need ethical assessment, the area in which AI has by far the most significant impact on animals is factory farming. The use of AI in factory farms will, in the long run, increase the already huge number of animals who suffer in terrible conditions.

AI systems in factory farms can monitor animals’ body temperature, weight and growth rates and detect parasites, ulcers and injuries. Machine learning models can be created to see how physical parameters relate to rates of growth, disease, mortality and — the ultimate criterion — profitability. The systems can then prescribe treatments for diseases or vary the quantity of food provided. In some cases, they can use their connected physical components to act directly on the animals, emitting sounds to interact with them — giving them electric shocks (when the grazing animal reaches the boundary of the desired area, for example), marking and tagging their bodies or catching and separating them.

You might be thinking that this would benefit the animals — that it means they will get sick less often, and when they do get sick, the problems will be quickly identified and cured, with less room for human error. But the short-term animal welfare benefits brought about by AI are, in our view, clearly outweighed by other consequences…(More)” See also: AI Ethics: The Case for Including Animals.

Including the underrepresented

Curated on Posted on by Stefaan Verhulst

Paper by FIDE: “Deliberative democracy is based on the premise that all voices matter and that we can equally participate in decision-making. However, structural inequalities might prevent certain groups from being recruited for deliberation, skewing the process towards the socially privileged. Those structural inequalities are also present in the deliberation room, which can lead to unconscious (or conscious) biases that hinder certain voices while amplifying others. This causes particular perspectives to influence decision-making unequally.

This paper presents different methods and strategies applied in previous processes to increase the inclusion of underrepresented groups. We distinguish strategies for the two critical phases of the deliberative process: recruitment and deliberation…(More)”.

The Surveillance Ad Model Is Toxic — Let’s Not Install Something Worse

Curated on Posted on by Stefaan Verhulst

Article by Elizabeth M. Renieris: “At this stage, law and policy makers, civil society and academic researchers largely agree that the existing business model of the Web — algorithmically targeted behavioural advertising based on personal data, sometimes also referred to as surveillance advertising — is toxic. They blame it for everything from the erosion of individual privacy to the breakdown of democracy. Efforts to address this toxicity have largely focused on a flurry of new laws (and legislative proposals) requiring enhanced notice to, and consent from, users and limiting the sharing or sale of personal data by third parties and data brokers, as well as the application of existing laws to challenge ad-targeting practices.

In response to the changing regulatory landscape and zeitgeist, industry is also adjusting its practices. For example, Google has introduced its Privacy Sandbox, a project that includes a planned phaseout of third-party cookies from its Chrome browser — a move that, although lagging behind other browsers, is nonetheless significant given Google’s market share. And Apple has arguably dealt one of the biggest blows to the existing paradigm with the introduction of its AppTrackingTransparency (ATT) tool, which requires apps to obtain specific, opt-in consent from iPhone users before collecting and sharing their data for tracking purposes. The ATT effectively prevents apps from collecting a user’s Identifier for Advertisers, or IDFA, which is a unique Apple identifier that allows companies to recognize a user’s device and track its activity across apps and websites.

But the shift away from third-party cookies on the Web and third-party tracking of mobile device identifiers does not equate to the end of tracking or even targeted ads; it just changes who is doing the tracking or targeting and how they go about it. Specifically, it doesn’t provide any privacy protections from first parties, who are more likely to be hegemonic platforms with the most user data. The large walled gardens of Apple, Google and Meta will be less impacted than smaller players with limited first-party data at their disposal…(More)”.

Innovating Democracy? The Means and Ends of Citizen Participation in Latin America

Curated on Posted on by Stefaan Verhulst

Book by Thamy Pogrebinschi: “Since democratization, Latin America has experienced a surge in new forms of citizen participation. Yet there is still little comparative knowledge on these so-called democratic innovations. This Element seeks to fill this gap. Drawing on a new dataset with 3,744 cases from 18 countries between 1990 and 2020, it presents the first large-N cross-country study of democratic innovations to date. It also introduces a typology of twenty kinds of democratic innovations, which are based on four means of participation, namely deliberation, citizen representation, digital engagement, and direct voting. Adopting a pragmatist, problem-driven approach, this Element claims that democratic innovations seek to enhance democracy by addressing public problems through combinations of those four means of participation in pursuit of one or more of five ends of innovations, namely accountability, responsiveness, rule of law, social equality, and political inclusion…(More)”.

Soft power, hard choices: Science diplomacy and the race for solutions

Curated on Posted on by Stefaan Verhulst

Article by Stephan Kuster and Marga Gual Soler: “…Global challenges demand that we build consensus for action. But reaching agreement on how – and even if – science and technology should be applied, for the aggregate benefit of all, is complex, and increasingly so.

Science and technology are tightly intertwined with fast-changing economic, geopolitical, and ideological agendas. That pace of change complicates, and sometimes deviates, the discussions and decisions that could unlock the positive global impact of scientific advances.

Therefore, anticipation is key. Understanding the societal, economic, and geopolitical consequences of emerging and possible new technologies before they are deployed is critical. Just recently, for example, artificial intelligence (AI) labs have been urged by a large number of researchers and leading industry figures to pause the training of powerful AI systems, given the inherent risks to society and humanity’s existence.

Indeed, the rapid pace of scientific development calls for more effective global governance when it comes to emerging technology. That in turn requires better anticipatory tools and new mechanisms to embed the science community as key stakeholder and influencer in this work.

The Geneva Science and Diplomacy Anticipator (GESDA) was created with those goals in mind. GESDA identifies the most significant science breakthroughs in the next five, 10, and 25 years. It assesses those advances with the potential to most profoundly to impact people, society, and the planet. It then brings together scientific and policy leaders from around the world to devise the diplomatic envelopes and approaches needed to embrace these advances, while minimizing downsides risks of unintended consequences…(More)”.

The Technology/Jobs Puzzle: A European Perspective

Curated on Posted on by Stefaan Verhulst

Blog by Pierre-Alexandre Balland, Lucía Bosoer and Andrea Renda as part of the work of the Markle Technology Policy and Research Consortium: “In recent years, the creation of “good jobs” – defined as occupations that provide a middle-class living standard, adequate benefits, sufficient economic security, personal autonomy, and career prospects (Rodrik and Sabel 2019; Rodrik and Stantcheva 2021) – has become imperative for many governments. At the same time, developments in industrial value chains and in digital technologies such as Artificial Intelligence (AI) create important challenges for the creation of good jobs. On the one hand, future good jobs may not be found only in manufacturing, ad this requires that industrial policy increasingly looks at services. On the other hand, AI has shown the potential to automate both routine and also non-routine tasks (TTC 2022), and this poses new, important questions on what role humans will play in the industrial value chains of the future. In the report drafted for the Markle Technology Policy and Research Consortium on The Technology/Jobs Puzzle: A European Perspective, we analyze Europe’s approach to the creation of “good jobs”. By mapping Europe’s technological specialization, we estimate in which sectors good jobs are most likely to emerge, and assess the main opportunities and challenges Europe faces on the road to a resilient, sustainable and competitive future economy.The report features an important reflection on how to define job quality and, relatedly “good jobs”. From the perspective of the European Union, job quality can be defined along two distinct dimensions. First, while the internationally agreed definition is rather static (e.g. related to the current conditions of the worker), the emerging interpretation at the EU level incorporates the extent to which a given job leads to nurturing human capital, and thereby empowering workers with more skills and well-being over time. Second, job quality can be seen from a “micro” perspective, which only accounts for the condition of the individual worker; or from a more “macro” perspective, which considers whether the sector in which the job emerges is compatible with the EU’s agenda, and in particular with the twin (green and digital) transition. As a result, we argue that ideally, Europe should avoid creating “good” jobs in “bad” sectors, as well as “bad” jobs in “good” sectors. The ultimate goal is to create “good” jobs in “good” sectors….(More)”

How public money is shaping the future of AI

Curated on Posted on by Stefaan Verhulst

Report by Ethica: “The European Union aims to become the “home of trustworthy Artificial Intelligence” and has committed the biggest existing public funding to invest in AI over the next decade. However, the lack of accessible data and comprehensive reporting on the Framework Programmes’ results and impact hinder the EU’s capacity to achieve its objectives and undermine the credibility of its commitments. 

This research commissioned by the European AI & Society Fund, recommends publicly accessible data, effective evaluation of the real-world impacts of funding, and mechanisms for civil society participation in funding before investing further public funds to achieve the EU’s goal of being the epicenter of trustworthy AI.

Among its findings, the research has highlighted the negative impact of the European Union’s investment in artificial intelligence (AI). The EU invested €10bn into AI via its Framework Programmes between 2014 and 2020, representing 13.4% of all available funding. However, the investment process is top-down, with little input from researchers or feedback from previous grantees or civil society organizations. Furthermore, despite the EU’s aim to fund market-focused innovation, research institutions and higher and secondary education establishments received 73% of the total funding between 2007 and 2020. Germany, France, and the UK were the largest recipients, receiving 37.4% of the total EU budget.

The report also explores the lack of commitment to ethical AI, with only 30.3% of funding calls related to AI mentioning trustworthiness, privacy, or ethics. Additionally, civil society organizations are not involved in the design of funding programs, and there is no evaluation of the economic or societal impact of the funded work. The report calls for political priorities to align with funding outcomes in specific, measurable ways, citing transport as the most funded sector in AI despite not being an EU strategic focus, while programs to promote SME and societal participation in scientific innovation have been dropped….(More)”.

Data Cooperatives as Catalysts for Collaboration, Data Sharing, and the (Trans)Formation of the Digital Commons

Curated on Posted on by Stefaan Verhulst

Paper by Michael Max Bühler et al: “Network effects, economies of scale, and lock-in-effects increasingly lead to a concentration of digital resources and capabilities, hindering the free and equitable development of digital entrepreneurship (SDG9), new skills, and jobs (SDG8), especially in small communities (SDG11) and their small and medium-sized enterprises (“SMEs”). To ensure the affordability and accessibility of technologies, promote digital entrepreneurship and community well-being (SDG3), and protect digital rights, we propose data cooperatives [1,2] as a vehicle for secure, trusted, and sovereign data exchange [3,4]. In post-pandemic times, community/SME-led cooperatives can play a vital role by ensuring that supply chains to support digital commons are uninterrupted, resilient, and decentralized [5]. Digital commons and data sovereignty provide communities with affordable and easy access to information and the ability to collectively negotiate data-related decisions. Moreover, cooperative commons (a) provide access to the infrastructure that underpins the modern economy, (b) preserve property rights, and (c) ensure that privatization and monopolization do not further erode self-determination, especially in a world increasingly mediated by AI. Thus, governance plays a significant role in accelerating communities’/SMEs’ digital transformation and addressing their challenges. Cooperatives thrive on digital governance and standards such as open trusted Application Programming Interfaces (APIs) that increase the efficiency, technological capabilities, and capacities of participants and, most importantly, integrate, enable, and accelerate the digital transformation of SMEs in the overall process. This policy paper presents and discusses several transformative use cases for cooperative data governance. The use cases demonstrate how platform/data-cooperatives, and their novel value creation can be leveraged to take digital commons and value chains to a new level of collaboration while addressing the most pressing community issues. The proposed framework for a digital federated and sovereign reference architecture will create a blueprint for sustainable development both in the Global South and North…(More)”

Knowledge monopolies and the innovation divide: A governance perspective

Curated on Posted on by Stefaan Verhulst

Paper by Hani Safadi and Richard Thomas Watson: “The rise of digital platforms creates knowledge monopolies that threaten innovation. Their power derives from the imposition of data obligations and persistent coupling on platform participation and their usurpation of the rights to data created by other participants to facilitate information asymmetries. Knowledge monopolies can use machine learning to develop competitive insights unavailable to every other platform participant. This information asymmetry stifles innovation, stokes the growth of the monopoly, and reinforces its ascendency. National or regional governance structures, such as laws and regulatory authorities, constrain economic monopolies deemed not in the public interest. We argue the need for legislation and an associated regulatory mechanism to curtail coercive data obligations, control, eliminate data rights exploitation, and prevent mergers and acquisitions that could create or extend knowledge monopolies…(More)”.