Sustainability management : insights from the viable system model

A review of current literature on sustainability standards reveals a significant gap between their adoption and the implementation of sustainability into every level of the organisation. In this paper, it is argued that in order to overcome this challenge, an appropriate model of an organisation is needed. The Viable System Model (VSM) is proposed as such a model and, in order to illustrate this argument, it is used to interpret the ISO 26000 standard on Social Responsibility (SR). First, the VSM theory is introduced and presented by modelling the hypothetical company Widget Co. Then, the clauses of ISO 26000 are mapped on the Widget Co. model, together with detailed descriptions and examples on the organisational and managerial implications of its adopting the standard's guidelines. The result is the identification of generic SR functions that need to be performed by the various organisational governance systems, as well as their dynamic interrelations, thus clarifying implementation issues. Moreover, by identifying different SR management layers, VSM is suggested as a way forward to develop an integration model for SR issues and respective sustainability tools. Finally, a discussion is given on the implications of using this approach to integrate sustainability standards and the way this research contributes to recent developments in sustainability research

Solomonoff Induction Violates Nicod's Criterion

Nicod's criterion states that observing a black raven is evidence for the hypothesis H that all ravens are black. We show that Solomonoff induction does not satisfy Nicod's criterion: there are time steps in which observing black ravens decreases the belief in H. Moreover, while observing any computable infinite string compatible with H, the belief in H decreases infinitely often when using the unnormalized Solomonoff prior, but only finitely often when using the normalized Solomonoff prior. We argue that the fault is not with Solomonoff induction; instead we should reject Nicod's criterion.Comment: ALT 201

Human pol II promoter prediction: time series descriptors and machine learning

Although several in silico promoter prediction methods have been developed to date, they are still limited in predictive performance. The limitations are due to the challenge of selecting appropriate features of promoters that distinguish them from non-promoters and the generalization or predictive ability of the machine-learning algorithms. In this paper we attempt to define a novel approach by using unique descriptors and machine-learning methods for the recognition of eukaryotic polymerase II promoters. In this study, non-linear time series descriptors along with non-linear machine-learning algorithms, such as support vector machine (SVM), are used to discriminate between promoter and non-promoter regions. The basic idea here is to use descriptors that do not depend on the primary DNA sequence and provide a clear distinction between promoter and non-promoter regions. The classification model built on a set of 1000 promoter and 1500 non-promoter sequences, showed a 10-fold cross-validation accuracy of 87% and an independent test set had an accuracy >85% in both promoter and non-promoter identification. This approach correctly identified all 20 experimentally verified promoters of human chromosome 22. The high sensitivity and selectivity indicates that n-mer frequencies along with non-linear time series descriptors, such as Lyapunov component stability and Tsallis entropy, and supervised machine-learning methods, such as SVMs, can be useful in the identification of pol II promoters

The Survey, Taxonomy, and Future Directions of Trustworthy AI: A Meta Decision of Strategic Decisions

When making strategic decisions, we are often confronted with overwhelming information to process. The situation can be further complicated when some pieces of evidence are contradicted each other or paradoxical. The challenge then becomes how to determine which information is useful and which ones should be eliminated. This process is known as meta-decision. Likewise, when it comes to using Artificial Intelligence (AI) systems for strategic decision-making, placing trust in the AI itself becomes a meta-decision, given that many AI systems are viewed as opaque "black boxes" that process large amounts of data. Trusting an opaque system involves deciding on the level of Trustworthy AI (TAI). We propose a new approach to address this issue by introducing a novel taxonomy or framework of TAI, which encompasses three crucial domains: articulate, authentic, and basic for different levels of trust. To underpin these domains, we create ten dimensions to measure trust: explainability/transparency, fairness/diversity, generalizability, privacy, data governance, safety/robustness, accountability, reproducibility, reliability, and sustainability. We aim to use this taxonomy to conduct a comprehensive survey and explore different TAI approaches from a strategic decision-making perspective

Responding to Paradoxical Organisational Demands for AI-Powered Systems considering Fairness

Developing and maintaining fair AI is increasingly in demand when unintended ethical issues contaminate the benefits of AI and cause negative implications for individuals and society. Organizations are challenged by simultaneously managing the divergent needs derived from the instrumental and humanistic goals of employing AI. In responding to the challenge, this paper draws on the paradox theory from a sociotechnical lens to first explore the contradictory organizational needs salient in the lifecycle of AI-powered systems. Moreover, we intend to unfold the responding process of the company to illuminate the role of social agents and technical artefacts in the process of managing paradoxical needs. To achieve the intention of the study, we conduct an in-depth case study on an AI-powered talent recruitment system deployed in an IT company. This study will contribute to research and practice regarding how organizational use of digital technologies generates positive ethical implications for individuals and society

Decision-Making: A Neuroeconomic Perspective

This article introduces and discusses from a philosophical point of view the nascent field of neuroeconomics, which is the study of neural mechanisms involved in decision-making and their economic significance. Following a survey of the ways in which decision-making is usually construed in philosophy, economics and psychology, I review many important findings in neuroeconomics to show that they suggest a revised picture of decision-making and ourselves as choosing agents. Finally, I outline a neuroeconomic account of irrationality