Investigation of the governance structure of Nairobi dairy value chain and its influence on food safety

The dairy value chain of Nairobi is comprised, in its majority, of small-scale independent enterprises that operate within a complex interlinked system. In this complexity, the coordination and power structures of the system may have major influences on the management of dairy food safety. Therefore, the aim of this study was to investigate the governance and challenges issues faced by stakeholders throughout the Nairobi dairy value chain and assess their potential implications on food safety. Qualitative data was collected through focus group discussions and key informant interviews based on a dairy value chain mapping framework previously developed. Thematic analysis enabled identification of governance themes, key challenges and their implication on food safety. Themes were organized depending on their association with farmers (informal settlement or peri-urban), dairy cooperatives, dairy traders, processing companies, retailers or government officers. The identified governance themes included: i) weak linkage between government and farmers, ii) inadequate compliance with government regulations by traders and retailers, iii) emphasis on business licenses and permits for revenue rather than for food safety, iv) multiple licensing resulting in high business cost and lack of compliance, v) fragmented regulation, vi) unfair competition and vii) sanctions that do not always result in compliance. The key challenges identified included, amongst others: i) inadequate farmer support, ii) harassment of traders and retailers and iii) high business costs by traders, retailers, dairy cooperatives and large processors. The implication of governance and challenges of food safety were, amongst others: i) inadequate extension services, ii) insufficient cold chain, iii) delivery of adulterated and low milk quality to bulking centres, iv) inadequate food safety training and v) lack of policies for management of waste milk. The range of issues highlighted are based on stakeholders’ perceptions and reflects the complexity of the relationships between them. Many of the governance themes demonstrate the linkages that are both beneficial or confrontational between the formal and informal sectors, and between industry and regulatory authorities, with possible direct food safety consequences. Findings obtained provide indications to decision-makers of potential governance areas that could help improve efficiency and food safety along the dairy value chain

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Adaptive Financial Regulation and RegTech: A Concept Article on Realistic Protection for Victims of Bank Failures

Frustrated by the seeming inability of regulators and prosecutors to hold bank executives to account for losses inflicted by their companies before, during, and since the financial crisis of 2008, some scholars have suggested that private-attorney-general suits such as class action and shareholder derivative suits might achieve better results. While a few isolated suits might be successful in cases where there is provable fraud, such remedies are no general panacea for preventing large-scale bank-inflicted losses. Large losses are nearly always the result of unforeseeable or suddenly changing economic conditions, poor business judgment, or inadequate regulatory supervision—usually a combination of all three. Yet regulators face an increasingly complex task in supervising modern financial institutions. This Article explains how the challenge has become so difficult. It argues for preserving regulatory discretion rather than reducing it through formal congressional direction. The Article also asserts that regulators have to develop their own sophisticated methods of automated supervision. Although also not a panacea, the development of “RegTech” solutions will help clear away volumes of work that understaffed and underfunded regulators cannot keep up with. RegTech will not eliminate policy considerations, nor will it render regulatory decisions noncontroversial. Nevertheless, a sophisticated deployment of RegTech should help focus regulatory discretion and public-policy debate on the elements of regulation where choices really matter

Unpredictability of AI

The young field of AI Safety is still in the process of identifying its challenges and limitations. In this paper, we formally describe one such impossibility result, namely Unpredictability of AI. We prove that it is impossible to precisely and consistently predict what specific actions a smarter-than-human intelligent system will take to achieve its objectives, even if we know terminal goals of the system. In conclusion, impact of Unpredictability on AI Safety is discussed

An Ontology for Sustainability Reporting Based on Global Reporting Initiative (GRI) G4

The aim of this research is to fill the gap by developing ontology for Sustainability Reporting based on GRI G4 Guidelines. The chief research question is: What is the best approach to developing an Ontological Model for the knowledge domain Sustainability Reporting? The main objective of this research is to develop such ontology for Sustainability Reporting based on GRI G4.The developed ontology for Sustainability Reporting was validated by applying it to existing business data