118 research outputs found
Steering Responsible AI: A Case for Algorithmic Pluralism
In this paper, I examine questions surrounding AI neutrality through the
prism of existing literature and scholarship about mediation and media
pluralism. Such traditions, I argue, provide a valuable theoretical framework
for how we should approach the (likely) impending era of AI mediation. In
particular, I suggest examining further the notion of algorithmic pluralism.
Contrasting this notion to the dominant idea of algorithmic transparency, I
seek to describe what algorithmic pluralism may be, and present both its
opportunities and challenges. Implemented thoughtfully and responsibly, I
argue, Algorithmic or AI pluralism has the potential to sustain the diversity,
multiplicity, and inclusiveness that are so vital to democracy.Comment: 10 pages, working pape
WANTED: DATA STEWARDS - (RE-)DEFINING THE ROLES AND RESPONSIBILITIES OF DATA STEWARDS FOR AN AGE OF DATA COLLABORATION
This paper is meant to inform the on-going exploration of how to enable systematic, sustainable, and responsible re-use of data through cross-sector data collaboration in the public interest (often called Data for Good). Data stewards build trust between organizations, agilely creating relationships between leaders from different sectors and backgrounds.Specifically, the position paper seeks to outline the roles and responsibilities of the emergent data steward profession. It is intended to support data-holding businesses and public institutions to create and promote data stewards in the public and private sectors; and to establish a network of these data stewardsâas recently recommended by the High Level Expert Group to the European Commission on Business-to-Government Data Sharing.
Mapping Digital Media: Net Neutrality and the Media
Outlines the arguments and design principles involved in the debate over changing the Internet and telecom architecture to prioritize and/or block certain kinds of traffic; approaches to securing net neutrality; and implications for the media
The Potential of Social Media Intelligence to Improve Peoples Lives: Social Media Data for Good
In this report, developed with support from Facebook, we focus on an approach to extract public value from social media data that we believe holds the greatest potential: data collaboratives. Data collaboratives are an emerging form of public-private partnership in which actors from different sectors exchange information to create new public value. Such collaborative arrangements, for example between social media companies and humanitarian organizations or civil society actors, can be seen as possible templates for leveraging privately held data towards the attainment of public goals
The Speculative Influence of Academic Research on the Making of Communications Policy: Reflections, Recollections and Informal Perspectives
This informal collection is designed to further a dialogue about the relationship between communications research and policy making. In particular it focuses on the impact of academic research on communications policy, and whether, and how, policy draws upon research (if at all). As quasi-editors (and commissioners of these essays) we have been highlighting various assumptions in the process. These assumptions mark every stage of the question (of the relevance of what academics do to what policy makers do). They mark an idealized mode of thinking about policy-makingâan idealized mode sometimes articulated in legislation or judicial decision (or agency practice). The assumptions include the following: Good and democratic policy making should be based upon an informed deliberation, and include relevant research findings. Policy making involves problem solving, guided change and conflict resolution. Communications research should be (designed to be) an important input into policy making. Policy makers have an appetite for (or can be compelled to have an appetite) research There is room for âdisinterested researchâ and possibly academic research has that quality Academic research has a kind of methodological purity or excellence or at least strives for that There is a disconnect between the demand and supply of policy relevant communications research. In part, this is a problem of access to research and data (although with the Internet, this has become more a âtranslationâ and âcommunicationsâ problem, i.e. researchers fail to communicate timely and for a broader audience). In part, the disconnect is a result of the difference between academic research and policymaking with regard to: Incentives (e.g. tenure/peer review vs political viability) Timetables (e.g. journal deadlines vs immediately) Format preferences (lengthy vs succinct) Agenda and relevance (old vs new challenges and technologies) Quality and validity standards (neutral vs political) Information about demand and supply In part, the problem is related with the ignorance and capacity of policy makers vis-Ă -vis using research.
What this effort hopes to do is to deepen and challenge these assumptions, as they relate to communications research and policy
Data Collaboratives as a New Frontier of Cross-Sector Partnerships in the Age of Open Data: Taxonomy Development
Data collaboratives present a new form of cross-sector and public-private partnership to leverage (often corporate) data for addressing a societal challenge. They can be seen as the latest attempt to make data accessible to solve public problems. Although an increasing number of initiatives can be found, there is hardly any analysis of these emerging practices. This paper seeks to develop a taxonomy of forms of data collaboratives. The taxonomy consists of six dimensions related to data sharing and eight dimensions related to data use. Our analysis shows that data collaboratives exist in a variety of models. The taxonomy can help organizations to find a suitable form when shaping their efforts to create public value from corporate and other data. The use of data is not only dependent on the organizational arrangement, but also on aspects like the type of policy problem, incentives for use, and the expected outcome of data collaborative
Open Data in Developing Economies
Recent years have witnessed considerable speculation about the potential of open data to bring about wide-scale transformation. The bulk of existing evidence about the impact of open data, however, focuses on high-income countries. Much less is known about open dataâs role and value in low- and middle-income countries, and more generally about its possible contributions to economic and social development.
Open Data for Developing Economies features in-depth case studies on how open data is having an impact across the developing world-from an agriculture initiative in Colombia to data-driven healthcare projects in Uganda and South Africa to crisis response in Nepal. The analysis built on these case studies aims to create actionable intelligence regarding: (a) the conditions under which open data is most (and least) effective in development, presented in the form of a Periodic Table of Open Data; (b) strategies to maximize the positive contributions of open data to development; and (c) the means for limiting open dataâs harms on developing countries
Operationalizing digital self-determination
A proliferation of data-generating devices, sensors, and applications has led to unprecedented amounts of digital data. We live in an era of datafication, one in which life is increasingly quantified and transformed into intelligence for private or public benefit. When used responsibly, this offers new opportunities for public good. The potential of data is evident in the possibilities offered by open data and data collaborativesâboth instances of how wider access to data can lead to positive and often dramatic social transformation. However, three key forms of asymmetry currently limit this potential, especially for already vulnerable and marginalized groups: data asymmetries, information asymmetries, and agency asymmetries. These asymmetries limit human potential, both in a practical and psychological sense, leading to feelings of disempowerment and eroding public trust in technology. Existing methods to limit asymmetries (such as open data or consent) as well as some alternatives under consideration (data ownership, collective ownership, personal information management systems) have limitations to adequately address the challenges at hand. A new principle and practice of digital self-determination (DSD) is therefore required. The study and practice of DSD remain in its infancy. The characteristics we have outlined here are only exploratory, and much work remains to be done so as to better understand what works and what does not. We suggest the need for a new research framework or agenda to explore DSD and how it can address the asymmetries, imbalances, and inequalitiesâboth in data and society more generallyâthat are emerging as key public policy challenges of our era
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