Blair equates the constructs of working memory (WM), executive function, and general fluid intelligence (gF). We argue that there is good reason not to equate these constructs. We view WM and gF as separable but highly related, and suggest that the mechanism behind the relationship is controlled attention – an ability that is dependent on normal functioning of the prefrontal cortex. (Published Online April 5 2006).

An implicit ambiguity in the field of prediction-based decision-making concerns the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply refers to ‘fair prediction’. In this paper, we point out that a differentiation of these concepts is helpful when trying to implement algorithmic fairness. Even if fairness properties are related to the features of the used prediction model, what is more properly called (...) ‘fair’ or ‘unfair’ is a decision system, not a prediction model. This is because fairness is about the consequences on human lives, created by a decision, not by a prediction. In this paper, we clarify the distinction between the concepts of prediction and decision and show the different ways in which these two elements influence the final fairness properties of a prediction-based decision system. As well as discussing this relationship both from a conceptual and a practical point of view, we propose a framework that enables a better understanding and reasoning of the conceptual logic of creating fairness in prediction-based decision-making. In our framework, we specify different roles, namely the ‘prediction-modeler’ and the ‘decision-maker,’ and the information required from each of them for being able to implement fairness of the system. Our framework allows for deriving distinct responsibilities for both roles and discussing some insights related to ethical and legal requirements. Our contribution is twofold. First, we offer a new perspective shifting the focus from an abstract concept of algorithmic fairness to the concrete context-dependent nature of algorithmic decision-making, where different actors exist, can have different goals, and may act independently. In addition, we provide a conceptual framework that can help structure prediction-based decision problems with respect to fairness issues, identify responsibilities, and implement fairness governance mechanisms in real-world scenarios. (shrink)

The dramatic recent advances and emergent trends in technologies have brought to the fore many vital and challenging questions and dilemmas for leaders and organizations. These are issues that call for a critical, insightful examination of key questions such as: are modern technologies beneficial or problematic for the well-being of individuals, organizations, and societies at large; why do we seem to feel more disconnected in an age of technological connectivity; can organizations reduce technology-induced stresses and find ways to enable the (...) mindful use of technologies and how can organizations, governments and societies manage the use of technologies wisely? Such questions, when explored from various perspectives of wisdom, can yield significant insights, increase awareness of the issues, deepen the dialogue and help redesign an increasingly technology-driven future. However, there is little researched published material available on such questions and issues. Practical Wisdom in the Age of Technology: Insights, Issues and Questions for a New Millennium will provide a space to engage in thought-provoking dialogue and critical reflection on a variety of themes linking technology and practical wisdom. It will offer a foundation for exploration, inquiry, engagement, and discussion among organizational, political and social leaders, technology professionals, information systems academicians, wisdom researchers, managers, philosophers of technology, and other practitioners. (shrink)