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fionntan

Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algori... - 1 views

shared by fionntan on 08 Jun 20 - No Cached
  • In environmental studies, Lynch and Veland [45] introduced the concept of urgent governance, distinguishing between auditing for system reliability vs societal harm. For example, a power plant can be consistently productive while causing harm to the environment through pollution [42].
  • the organizations designing and deploying algorithms can through governance structures. Proposed standard ISO 37000 defines this structure as "the system by which the whole organization is directed, controlled and held accountable to achieve its core purpose over the long term.
  • he organizations designing and deploying algorithms can through governance structures. Proposed standard ISO 37000 defines this structure as "the system by which the whole organization is directed, controlled and held accountable to achieve its core purpose over the long term.
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  • The IEEE standard for software development defines an audit as “an independent evaluation of conformance of software products and processes to applicable regulations, standards, guidelines, plans, specifications, and procedures”
  • This is a repeatedly observed phenomenon in tax compliance auditing, where several international surveys of tax compliance demonstrate that a fixed and vetted tax audit methodology is one of the most effective strategies to convince companies to respect audit results and pay their full taxes
  • Complex systems tend to drift toward unsafe conditions unless constant vigilance is maintained [42]. It is the sum of the tiny probabilities of individual events that matters in complex systems—if this grows without bound, the probability of catastrophe goes to one. The Borel-Cantelli Lemmas are formalizations of this statistical phenomenon [13],
  • Failure Modes and Effects Analysis (FMEA), methodical and systematic risk management approach that examines a proposed design or technology for foreseeable failures [72]. The main purpose of a FMEA is to define, identify and eliminate potential failures or problems in different products, designs, systems and services.
Ben Snaith

Business models for sustainable research data repositories | OECD - 3 views

shared by Ben Snaith on 01 Jun 20 - No Cached
  • However, for the benefits of open science and open research data to be realised, these data need to be carefully and sustainably managed so that they can be understood and used by both present and future generations of researchers. Data repositories - based in local and national research institutions and international bodies - are where the long-term stewardship of research data takes place and hence they are the foundation of open science. Yet good data stewardship is costly and research budgets are limited. So, the development of sustainable business models for research data repositories needs to be a high priority in all countries.
  • The 47 data repositories analysed reported 95 revenue sources. Typically, repository business models combine structural or host funding with various forms of research and other contract-for-services funding, or funding from charges for access to related value-added services or facilities. A second popular combination is deposit-side funding combined with a mix of structural or host institutional funding, or with revenue from the provision of research, value-added, and other services.
  • Research data repositories themselves can take advantage of the underlying economic differences between research data, which exhibit public good characteristics, and value-adding services and facilities, which typically do not, to develop business models that support free and open data while charging some or all users for access to value-adding services or related facilities
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  • Over the centuries, libraries, archives, and museums have shown the practical and policy advantages of preserving sources of knowledge for society. Research and other types of data constitute a relatively new subject that requires our serious attention. Although some research data repositories were founded in the 1960s and even earlier, the data that are now being generated have resulted in the establishment of many new repositories and related infrastructure. Societies need such repositories to ensure that the most useful or unique data are preserved over the long term.
  • First, there are substantial and positive efficiency impacts, not only reducing the cost of conducting research, but also enabling more research to be done, to the benefit of researchers, research organisations, their funders, and society more widely
  • substantial additional reuse of the stored data, with between 44% and 58% of surveyed users across the studies saying they could neither have created the data for themselves nor obtained them elsewhere.
  • While these studies tend to provide a snapshot of the repository's value, which can be affected by the scale, age and prominence of the data repository concerned, it is important to note that in most cases, data archives are appreciating rather than depreciating assets. Most of the economic impact is cumulative and it grows in value over time, whereas most infrastructure (such as ships or buildings) has a declining value as it ages. Like libraries, data collections become more valuable as they grow and the longer one invests in them, provided that the data remain accessible, usable, and used.
  • Openness of public information strengthens freedom and democratic institutions by empowering citizens, and supporting transparency of political decision-making and trust in governance. It is no coincidence that the most repressive regimes have the most secretive institutions and activities (Uhlir, 2004). Open factual datasets also enhance public decision-making from the national to the local levels (Nelson, 2011), and open data policies demonstrate confidence of leadership and generally can broaden the influence of governments (Uhlir and Schröder, 2007). Countries that may be lagging behind socioeconomically frequently can benefit even more from access to public data resources (NRC, 2012b, 2002).
  • The survey of repositories undertaken for this and the previous RDA-WDS study classified the principal research data repository revenue sources as follows: • Structural funding (i.e. central funding or contract from a research or infrastructure funder that is in the form of a longer-term, multi-year contract). We use the term “structural” to underline the difference between this and project funding. The research data repository is considered as a form of research infrastructure or as providing an ongoing service. Although the funding may be regularly reviewed, it is a form of funding that is substantively different to project funding.
  • Host institution funding and support (i.e. direct or indirect support from a host institution). Some research data repositories are hosted by a research performing institution, e.g. a university, and receive direct funding or indirect (but costed) support from their host. • Data deposit fees (i.e. in the form of annual contracts with depositing institutions or per-deposit fees). As indicated, this can take the form of a period contract or a charge per deposit. In either case, the cost is borne by the entity that wishes to ensure that the data are preserved and curated for the long term. • Access charges (i.e. charging for access to standard data or to value-added services and facilities). This covers charges of various sorts (e.g. contract or per-access charges) and can be levied either for standard data or value-added services. In all cases, the cost is borne by the entity that wishes to access and use the data. • Contract services or project funding (i.e. charges for contract services to other parties or for research contracts). This covers short-term contracts and projects for various activities not covered above (i.e. these are not contracts to deposit or access data, but cover other services that may be provided). Similarly, this category of funding is distinct from structural funding because, although it may come from a research or infrastructure funder, it is for specific, time- and objective-limited projects, rather than for ongoing services or infrastructure.
  • The 47 data repositories analysed reported 95 revenue sources, an average of two per repository. Twenty-four repositories reported funding from more than one source, and seven reported more than three revenue sources. Combining revenue sources is an important element in developing a sustainable research data infrastructure.
  • A large majority (more than 80%), said they would not be considering any revenue sources that are incompatible with the open data principle.
  • The stage of development of a repository, its institutional or disciplinary context, its scale, and level of federation are also important determinants of what might be a sustainable business model. Referring to the dynamic of the evolution of firms, some economists draw a human parallel, talking of the phases as births, deaths, and marriages (and sometimes divorces). All phases are needed and should be accommodated. Indeed, sometimes it may not be desirable, effective, or efficient for a repository to be sustainable - provided that the data can continue to be hosted elsewhere.
  • This is the situation facing research data repositories. To be sustainable, data repositories need to generate sufficient revenue to cover their costs, but setting a price above the marginal cost of copying and distribution will reduce net welfare
  • Actions needed to develop a successful research data repository business model include: • Understanding the lifecycle phase of the repository's development (e.g. the need for investment funding, development funding, ongoing operational funding, or transitional funding) • Identifying who the stakeholders are (e.g. data depositors, data users, research institutions, research funders, and policy makers) • Developing the product/service mix (e.g. basic data, value-added data, value-added services and related facilities, and contract and research services) • Understanding the cost drivers and matching revenue sources (e.g. scaling with demand for data ingest, data use, the development and provision of value-adding services or related facilities, research priorities, and policy mandates) • Identifying revenue sources (e.g. structural funding, host institutional funding, deposit-side charges, access charges, and value-added services or facilities charges ) • Making the value proposition to stakeholders (e.g. measuring impacts and making the research case, measuring value and making the economic case, informing, and educating) (Figure 6).
Ben Snaith

AnnualReport2018LandPortal.pdf - 0 views

shared by Ben Snaith on 01 Jun 20 - No Cached
  • We are proud to say that the Land Portal has become far and wide the world’s leading source of data and information on land, with more than 30,000 visits a month, a majority of which are from the Global South. The Land Portal now counts more than 760 land-related indicators from 45 datasets aggregated from trusted sources around the world. This great diversity of information feeds into a dozen thematic portfolios and more than 60 country portfolios that combine data with the latest news, relevant publications, organizations and more.
  • Land Portal is leading the way on the adoption of open data and making land governance information more accessible. We brought together the land governance community for the Partnership for Action workshop to set the stage for building an information ecosystem on land governance, resulting in an action plan on data collection, management and dissemination. The Land Portal’s approach to capacity building was further refined at a workshop in Pretoria, South Africa, which created a great deal of momentum to adopt open data practices in this country
  • We are grateful for the United Kingdom’s Department for International Development (DFID) steadfast support, as well as the support of the Omidyar Network. We are also thankful for the support of Food and Agriculture Organization of the United Nations (FAO), GIZ - German Cooperation and the collaboration of all of our partners, without which our work would not be possible
Ben Snaith

TheDiamondReport_TheSecondCut_2018-FINAL.pdf - 0 views

  • CDN will seek partnership with others in the immediate sector and beyond who have an interest in, and access to, other relevant data sets, so that we might collaborate and find ways to integrate data, to build a bigger picture about diversity in the industry.
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