Secure Border Initiative: Observations on Selected Aspects of SBInet Program Implementation

Testimony issued by the Government Accountability Office with an abstract that begins "In November 2005, the Department of Homeland Security (DHS) established the Secure Border Initiative (SBI), a multiyear, multibillion dollar program to secure U.S. borders. One element of SBI is SBInet--the U.S. Customs and Border Protection (CBP) program responsible for developing a comprehensive border protection system through a mix of security infrastructure (e.g., fencing), and surveillance and communication technologies (e.g., radars, sensors, cameras, and satellite phones). The House Committee on Homeland Security asked GAO to monitor DHS progress in implementing the SBInet program. This testimony provides GAO's observations on (1) SBInet technology implementation; (2) SBInet infrastructure implementation; (3) the extent to which CBP has determined the impact of SBInet technology and infrastructure on its workforce needs and operating procedures; and (4) how the CBP SBI Program Management Office (PMO) has defined its human capital goals and the progress it has made to achieve these goals. GAO's observations are based on analysis of DHS documentation, such as program schedules, contracts, status, and reports. GAO also conducted interviews with DHS officials and contractors, and visits to sites in the southwest border where SBInet deployment is underway. GAO performed the work from April 2007 through October 2007. DHS generally agreed with GAO's findings.

Designing a Blockchain Model for the Paris Agreement’s Carbon Market Mechanism

This paper examines the benefits and constraints of applying blockchain technology for the Paris Agreement carbon market mechanism and develops a list of technical requirements and soft factors as selection criteria to test the feasibility of two different blockchain platforms. The carbon market mechanism, as outlined in Article 6.2 of the Paris Agreement, can accelerate climate action by enabling cooperation between national Parties. However, in the past, carbon markets were limited by several constraints. Our research investigates these constraints and translates them into selection criteria to design a blockchain platform to overcome these past limitations. The developed selection criteria and assumptions developed in this paper provide an orientation for blockchain assessments. Using the selection criteria, we examine the feasibility of two distinct blockchains, Ethereum and Hyperledger Fabric, for the specific use case of Article 6.2. These two blockchain systems represent contrary forms of design and governance; Ethereum constitutes a public and permissionless blockchain governance system, while Hyperledger Fabric represents a private and permissioned governance system. Our results show that both blockchain systems can address present carbon market constraints by enhancing market transparency, increasing process automation, and preventing double counting. The final selection and blockchain system implementation will first be possible, when the Article 6 negotiations are concluded, and governance preferences of national Parties are established. Our paper informs about the viability of different blockchain systems, offers insights into governance options, and provides a valuable framework for a concrete blockchain selection in the future.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Vulnerability to Broker-Related Forced Labor Among Migrant Workers in Information Technology Manufacturing in Taiwan and Malaysia

This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.V_HELP_WANTED_A_Verit%C3%A9_Report_Workers_in_Taiwan___Malaysia.pdf: 1735 downloads, before Oct. 1, 2020

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering

Responsible AI is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of AI. Recently, a number of AI ethics principles frameworks have been published. However, without further guidance on best practices, practitioners are left with nothing much beyond truisms. Also, significant efforts have been placed at algorithm-level rather than system-level, mainly focusing on a subset of mathematics-amenable ethical principles, such as fairness. Nevertheless, ethical issues can arise at any step of the development lifecycle, cutting across many AI and non-AI components of systems beyond AI algorithms and models. To operationalize responsible AI from a system perspective, in this paper, we present a Responsible AI Pattern Catalogue based on the results of a Multivocal Literature Review (MLR). Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle. The Responsible AI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and responsible-AI-by-design product patterns. These patterns provide systematic and actionable guidance for stakeholders to implement responsible AI

A Brokering Framework for Assessing Legal Risks in Big Data and the Cloud

“Cloud computing” and “Big Data” are amongst the most hyped-up terms and buzzwords of the moment. After decades in which individuals and companies used to host their data and applications using their own IT infrastructure, the world has seen the stunning transformation of the Internet. Major shifts occurred when these infrastructures began to be outsourced to public Cloud providers to match commercial expectations. Storing, sharing and transferring data and databases over the Internet is convenient, yet legal risks cannot be eliminated. Legal risk is a fast-growing area of research and covers various aspects of law. Current studies and research on Cloud computing legal risk assessment have been, however, limited in scope and focused mainly on security and privacy aspects. There is little systematic research on the risks, threats and impact of the legal issues inherent to database rights and “ownership” rights of data. Database rights seem to be outdated and there is a significant gap in the scientific literature when it comes to the understanding of how to apply its provisions in the Big Data era. This means that we need a whole new framework for understanding, protecting and sharing data in the Cloud. The scheme we propose in this chapter is based on a risk assessment-brokering framework that works side by side with Service Level Agreements (SLAs). This proposed framework will provide better control for Cloud users and will go a long way to increase confidence and reinforce trust in Cloud computing transactions

Ontology-Based Knowledge Representation for Self-Governing Systems

Self-governing systems need a reliable set of semantics and a formal theoretic model in order to facilitate automated reasoning. We present an ontology-based knowledge representation that will use data from information models while preserving the semantics and the taxonomy of existing systems. This will facilitate the decomposition and validation of high level goals by autonomous, self-governing components. Our solution reuses principles and standards from the Semantic Web and the OMG to precisely describe the managed entities and the shared objectives that these entities are trying to achieve by autonomously correlating their behavior. We describe how we created UML2, MOF, OCL and QVT ontologies, and we give a case study using the NGOSS Shared Information and Data model. We also set the requirements for integrating existing information models and domain ontologies into a unique knowledge base