Intrinsic value has traditionally been thought to lie at the heart of ethics. Philosophers use a number of terms to refer to such value. The intrinsic value of something is said to be the value that that thing has “in itself,” or “for its own sake,” or “as such,” or “in its own right.” Extrinsic value is value that is not intrinsic.

The ongoing energy transition toward more sustainable energy systems implies a change in the values for which such systems are designed. The energy transition however is not just about sustainability but also about values like energy security and affordability, and we witness the emergence of new values like energy justice and energy democracy. How can we understand such value changes and how can or should they affect the design of future energy systems? This introduction to the special section on value (...) change in energy systems introduces the main themes and questions. It discusses different understandings of values and value change, explains why the topic is important and how it can be methodologically studied. (shrink)

Organizations such as the EU High-Level Expert Group on AI and the IEEE have recently formulated ethical principles and (moral) values that should be adhered to in the design and deployment of artificial intelligence (AI). These include respect for autonomy, non-maleficence, fairness, transparency, explainability, and accountability. But how can we ensure and verify that an AI system actually respects these values? To help answer this question, I propose an account for determining when an AI system can be said to embody (...) certain values. This account understands embodied values as the result of design activities intended to embed those values in such systems. AI systems are here understood as a special kind of sociotechnical system that, like traditional sociotechnical systems, are composed of technical artifacts, human agents, and institutions but—in addition—contain artificial agents and certain technical norms that regulate interactions between artificial agents and other elements of the system. The specific challenges and opportunities of embedding values in AI systems are discussed, and some lessons for better embedding values in AI systems are drawn. (shrink)

The first goal of this paper is to develop a theoretical and practical framework which can help to measure the emotional value generated by organizations in quantitative terms. Its second goal is to use data obtained from the UCAN in Spain as a case study to illustrate the quantification of the emotional value generated, with a view to factoring that value into a social accounting system. Ever greater recognition of the social role of organizations in recent years has led to (...) a need for a consistent definition of the concept of socio-emotional value, and for instruments that can be used to measure that value in terms of generic social accounting. Taking the current lack of standardization in such models and instruments, especially those that deal with emotional value as its starting point, the paper proposes a new instrument for measuring that value quantitatively in such a way as to overcome some of the limitations of earlier proposals. The underlying perspective is that the monetary values identified in market and non-market transactions do not accurately account for all the value generated for different stakeholders, and that adjustments are required through a correction factor applied to the value variables identified. The quantification of the socio-emotional value generated by an organization is seen as a more comprehensive indicator of its performance, given that it provides more information and takes into account the value generated for stakeholders as a whole in all dimensions. (shrink)