Investment Management Code of Conduct for Endowments, Foundations, and Charitable Organizations

The Investment Management Code of Conduct for Endowments, Foundations, and Charitable Organizations represents best practice for the oversight of an entity's financial assets. Governing Body members should uphold the general principles of the Code in the management of the assets and the organization

Reclaiming human machine nature

Extending and modifying his domain of life by artifact production is one of the main characteristics of humankind. From the first hominid, who used a wood stick or a stone for extending his upper limbs and augmenting his gesture strength, to current systems engineers who used technologies for augmenting human cognition, perception and action, extending human body capabilities remains a big issue. From more than fifty years cybernetics, computer and cognitive sciences have imposed only one reductionist model of human machine systems: cognitive systems. Inspired by philosophy, behaviorist psychology and the information treatment metaphor, the cognitive system paradigm requires a function view and a functional analysis in human systems design process. According that design approach, human have been reduced to his metaphysical and functional properties in a new dualism. Human body requirements have been left to physical ergonomics or "physiology". With multidisciplinary convergence, the issues of "human-machine" systems and "human artifacts" evolve. The loss of biological and social boundaries between human organisms and interactive and informational physical artifact questions the current engineering methods and ergonomic design of cognitive systems. New developpment of human machine systems for intensive care, human space activities or bio-engineering sytems requires grounding human systems design on a renewed epistemological framework for future human systems model and evidence based "bio-engineering". In that context, reclaiming human factors, augmented human and human machine nature is a necessityComment: Published in HCI International 2014, Heraklion : Greece (2014

"So, Tell Me What Users Want, What They Really, Really Want!"

Equating users' true needs and desires with behavioural measures of 'engagement' is problematic. However, good metrics of 'true preferences' are difficult to define, as cognitive biases make people's preferences change with context and exhibit inconsistencies over time. Yet, HCI research often glosses over the philosophical and theoretical depth of what it means to infer what users really want. In this paper, we present an alternative yet very real discussion of this issue, via a fictive dialogue between senior executives in a tech company aimed at helping people live the life they `really' want to live. How will the designers settle on a metric for their product to optimise

Do Open Access Articles Have a Greater Research Impact?

While many authors believe that their work has a greater research impact if it is freely available, studies to demonstrate that impact are few. This study looks at articles in four disciplines at varying stages of adoption of open access—philosophy, political science, electrical and electronic engineering and mathematics—to see if they have a greater impact, as measured by citations in the ISI Web of Science database, if their authors make them freely available on the Internet. The finding is that, across all four disciplines, freely available articles do have a greater research impact. Shedding light on this category of open access reveals that scholars in diverse disciplines are both adopting open access practices and being rewarded for it

Normalized entropy aggregation for inhomogeneous large-scale data

It was already in the fifties of the last century that the relationship between information theory, statistics, and maximum entropy was established, following the works of Kullback, Leibler, Lindley and Jaynes. However, the applications were restricted to very specific domains and it was not until recently that the convergence between information processing, data analysis and inference demanded the foundation of a new scientific area, commonly referred to as Info-Metrics. As huge amount of information and large-scale data have become available, the term "big data" has been used to refer to the many kinds of challenges presented in its analysis: many observations, many variables (or both), limited computational resources, different time regimes or multiple sources. In this work, we consider one particular aspect of big data analysis which is the presence of inhomogeneities, compromising the use of the classical framework in regression modelling. A new approach is proposed, based on the introduction of the concepts of info-metrics to the analysis of inhomogeneous large-scale data. The framework of information-theoretic estimation methods is presented, along with some information measures. In particular, the normalized entropy is tested in aggregation procedures and some simulation results are presented.publishe

What May Visualization Processes Optimize?

In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.Comment: 10 page

Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

There has been much discussion of the right to explanation in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to explanation that opens the black box of algorithmic decision-making faces major legal and technical barriers. Explaining the functionality of complex algorithmic decision-making systems and their rationale in specific cases is a technically challenging problem. Some explanations may offer little meaningful information to data subjects, raising questions around their value. Explanations of automated decisions need not hinge on the general public understanding how algorithmic systems function. Even though such interpretability is of great importance and should be pursued, explanations can, in principle, be offered without opening the black box. Looking at explanations as a means to help a data subject act rather than merely understand, one could gauge the scope and content of explanations according to the specific goal or action they are intended to support. From the perspective of individuals affected by automated decision-making, we propose three aims for explanations: (1) to inform and help the individual understand why a particular decision was reached, (2) to provide grounds to contest the decision if the outcome is undesired, and (3) to understand what would need to change in order to receive a desired result in the future, based on the current decision-making model. We assess how each of these goals finds support in the GDPR. We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims. These counterfactual explanations describe the smallest change to the world that can be made to obtain a desirable outcome, or to arrive at the closest possible world, without needing to explain the internal logic of the system