Report by Daniel Castro: “Data protection laws and regulations can contain restrictive provisions, which limit data sharing and use, as well as permissive provisions, which increase it. Data portability is an example of a permissive provision that allows consumers to obtain a digital copy of their personal information from an online service and provide this information to other services. By carefully crafting data portability provisions, policymakers can enable consumers to obtain more value from their data, create new opportunities for businesses to innovate with data, and foster competition….(More)”.
Article by Louise Lief: “Powered by thousands of early-career scientists and students, a global movement to transform scientific practice has emerged in recent years. The objective is to “expand the boundaries of what we consider science,” says Rajul Pandya, senior director of Thriving Earth Exchange at the American Geophysical Union (AGU), “to fundamentally transform science and the way we use it.”
These scientists have joined forces with community leaders and members of the public to establish new protocols and methods for doing community-driven science in an effort to make civic science even more inclusive and accessible to the public. Community science is an outgrowth of two earlier movements that emerged in response to the democratizing forces of the internet: open science, the push to make scientific research accessible and to encourage sharing and collaboration throughout the research cycle; and open data, the support for data that anyone can freely use, reuse, and share.
For open-science advocates, a reset of scientific practice is long overdue. For decades, the field has been dominated by what some experts call the “science-push” model, a top-down approach in which scientists decide which investigations to pursue, what questions to ask, how to do the science, and which results are significant. If members of the public are involved at all, they serve as research subjects or passive consumers of knowledge curated and presented to them by scientists.
The traditional approach to science has resulted in the public’s increasing distrust of scientists—their motives, values, and business interests. Science is a process that explores the world through observation and experiment, looking for evidence that may reveal larger patterns, often producing new discoveries. However, science itself does not decide the effects or outcomes of these results. The devastating opioid epidemic—in which manufacturers have aggressively promoted the highly addictive drugs, downplaying risks and misinforming doctors—has shown that the values and motives of those who practice science make all the difference.
Instead, open-science advocates believe science should be a joint enterprise between scientists and the public to demonstrate the value of science in people’s lives. Such collaboration will change the way scientists, communities, regulatory agencies, policy makers, academia, and funders work individually and collectively. Each player will be able to integrate science more easily into civic decision-making and target problems more efficiently and at lower costs. This collaborative work will create new opportunities for civic action and give the public a greater sense of ownership—making it their science….(More)”.
Paper by Sharon Zanti & M. Lori Thomas: “The evidence-based policymaking movement compels government leaders and agencies to rely on the best available research evidence to inform policy and program decisions, yet how to do this effectively remains a challenge. This paper demonstrates how the core concepts from two emerging fields—Implementation Science (IS) and Integrated Data Systems (IDS)—can help human service agencies and their partners realize the aims of the evidence-based policymaking movement. An IS lens can help agencies address the role of context when implementing evidence-based practices, complement other quality and process improvement efforts, simultaneously study implementation and effectiveness outcomes, and guide de-implementation of ineffective policies. The IDS approach offers governance frameworks to support ethical and legal data use, provides high-quality administrative data for in-house analyses, and allows for more time-sensitive analyses of pressing agency needs. Ultimately, IS and IDS can support human service agencies in more efficiently using government resources to deliver the best available programs and policies to the communities they serve. Although this paper focuses on examples within the United States context, key concepts and guidance are intended to be broadly applicable across geographies, given that IS, IDS, and the evidence-based policymaking movement are globally relevant….(More)”.
Essay by Stefan Schweinfest and Ronald Jansen: “In the digital age, data are generated continuously by many different devices and are being used by many different actors. National statistical offices (NSOs) should benefit from these opportunities to improve data for decision-making. What could be the expanding role for official statistics in this context and how does this relate to emerging disciplines like data science? This article explores some new ideas. In the avalanche of new data, society may need a data steward, and the NSO could take on that role, while paying close attention to the protection of privacy. Data science will become increasingly important for extracting meaningful information from large amounts of data. NSOs will need to hire data scientists and data engineers and will need to train their staff in these fast-developing fields. NSOs will also need to clearly communicate new and experimental data and foster a good understanding of statistics. Collaboration of official statistics with the private sector, academia, and civil society will be the new way of working and the fundamental principles of official statistics may have to apply to all those actors. This article envisions that we are gradually working toward such a new data culture…(More)”.
Book edited by Martin Ebers, Cristina Poncibò, and Mimi Zou: “This book provides original, diverse, and timely insights into the nature, scope, and implications of Artificial Intelligence (AI), especially machine learning and natural language processing, in relation to contracting practices and contract law. The chapters feature unique, critical, and in-depth analysis of a range of topical issues, including how the use of AI in contracting affects key principles of contract law (from formation to remedies), the implications for autonomy, consent, and information asymmetries in contracting, and how AI is shaping contracting practices and the laws relating to specific types of contracts and sectors.
The contributors represent an interdisciplinary team of lawyers, computer scientists, economists, political scientists, and linguists from academia, legal practice, policy, and the technology sector. The chapters not only engage with salient theories from different disciplines, but also examine current and potential real-world applications and implications of AI in contracting and explore feasible legal, policy, and technological responses to address the challenges presented by AI in this field.
The book covers major common and civil law jurisdictions, including the EU, Italy, Germany, UK, US, and China. It should be read by anyone interested in the complex and fast-evolving relationship between AI, contract law, and related areas of law such as business, commercial, consumer, competition, and data protection laws….(More)”.
Book by Eric Sauda, Ginette Wessel and Alireza Karduni: “The widespread adoption of smartphones has led to an explosion of mobile social media data, more than a billion messages per day that continuously track location, content, and time. Social Media in the Contemporary City focuses on the effects of social media on local communities and urban space in a variety of political and economic settings related to social activism, informal economic activity, public art, and global extremism.
The book covers events ranging from Banksy art installations, mobile food trucks, and underground restaurants, to a Black Lives Matter protest, the Christchurch mosque shootings, and the Pulse nightclub shooting. The interplay between urban space, local community, and social media in each case study requires diverse methodologies that are both computational (i.e. machine learning, social network analysis, and natural language processing) and ethnographic (i.e. semi-structured interviews, thematic analysis, and site analysis). The book views social media not as a replacement for the local community or urban space but rather as a translation of the uses and meanings of all three realms….(More)”.
Paper by Suha Mohamed: “…Mobility data refers to information (often passively captured) that provides insights into the location and movement of a population – often through their interactions with digital mobility devices (like our smartphones) or transport services. Sources of mobility data, while diverse, include call detail records from telecom companies, GPS details from phones or vehicles, geotagged social media data or first or third-party software data.
Geolocation, a subset of mobility data, may be useful in shaping responsive courses of action as it can be leveraged in granular form to understand hyperlocal realities or, when aggregated, regional, national or international patterns. However, privacy concerns arise from the sensitive or personal data that may be inferred from these records and the often opaque conditions around its usage. The ongoing deployment of contact tracing applications, which largely depend on individual-level location data, have demonstrated extensive potential for misuse and surveillance….
Despite the surveillance and privacy concerns around the use of contact tracing apps and mobility data, it is undeniable that this data has immense public value and has helped officials understand the development of the COVID-19 virus and map its variants and waves. It has also been used to track: areas of mobility that contribute towards increased transmission of the virus, adherence to social distancing norms and the effectiveness of measures like lockdowns or restrictions….(More)”.
Report by the World Economic Forum: “The distinct characteristics and dynamics of data – contextual, relational and cumulative – call for new approaches to articulating its value. Businesses should value data based on cases that go beyond the transactional monetization of data and take into account the broader context, future opportunities to collaborate and innovate, and value created for its ecosystem stakeholders. Doing so will encourage companies to think about the future value data can help generate, beyond the existing data lakes they sit on, and open them up to collaboration opportunities….(More)”.
Press Release: “In 2018, Audrey Azoulay, Director-General of UNESCO, launched an ambitious project: to give the world an ethical framework for the use of artificial intelligence. Three years later, thanks to the mobilization of hundreds of experts from around the world and intense international negotiations, the 193 UNESCO’s member states have just officially adopted this ethical framework….
The Recommendation aims to realize the advantages AI brings to society and reduce the risks it entails. It ensures that digital transformations promote human rights and contribute to the achievement of the Sustainable Development Goals, addressing issues around transparency, accountability and privacy, with action-oriented policy chapters on data governance, education, culture, labour, healthcare and the economy.
- Protecting data
The Recommendation calls for action beyond what tech firms and governments are doing to guarantee individuals more protection by ensuring transparency, agency and control over their personal data. It states that individuals should all be able to access or even erase records of their personal data. It also includes actions to improve data protection and an individual’s knowledge of, and right to control, their own data. It also increases the ability of regulatory bodies around the world to enforce this.
- Banning social scoring and mass surveillance
The Recommendation explicitly bans the use of AI systems for social scoring and mass surveillance. These types of technologies are very invasive, they infringe on human rights and fundamental freedoms, and they are used in a broad way. The Recommendation stresses that when developing regulatory frameworks, Member States should consider that ultimate responsibility and accountability must always lie with humans and that AI technologies should not be given legal personality themselves.
- Helping to monitor and evaluate
The Recommendation also sets the ground for tools that will assist in its implementation. Ethical Impact Assessment is intended to help countries and companies developing and deploying AI systems to assess the impact of those systems on individuals, on society and on the environment. Readiness Assessment Methodology helps Member States to assess how ready they are in terms of legal and technical infrastructure. This tool will assist in enhancing the institutional capacity of countries and recommend appropriate measures to be taken in order to ensure that ethics are implemented in practice. In addition, the Recommendation encourages Member States to consider adding the role of an independent AI Ethics Officer or some other mechanism to oversee auditing and continuous monitoring efforts.
- Protecting the environment
The Recommendation emphasises that AI actors should favour data, energy and resource-efficient AI methods that will help ensure that AI becomes a more prominent tool in the fight against climate change and on tackling environmental issues. The Recommendation asks governments to assess the direct and indirect environmental impact throughout the AI system life cycle. This includes its carbon footprint, energy consumption and the environmental impact of raw material extraction for supporting the manufacturing of AI technologies. It also aims at reducing the environmental impact of AI systems and data infrastructures. It incentivizes governments to invest in green tech, and if there are disproportionate negative impact of AI systems on the environment, the Recommendation instruct that they should not be used….(More)”.
OECD Report: “Evaluations of representative deliberative processes do not happen regularly, not least due to the lack of specific guidance for their evaluation. To respond to this need, together with an expert advisory group, the OECD has developed Evaluation Guidelines for Representative Deliberative Processes. They aim to encourage public authorities, organisers, and evaluators to conduct more comprehensive, objective, and comparable evaluations.
These evaluation guidelines establish minimum standards and criteria for the evaluation of representative deliberative processes as a foundation on which more comprehensive evaluations can be built by adding additional criteria according to specific contexts and needs.
The guidelines suggest that independent evaluations are the most comprehensive and reliable way of evaluating a deliberative process.
For smaller and shorter deliberative processes, evaluation in the form of self-reporting by members and/or organisers of a deliberative process can also contribute to the learning process…(More)”.