Disease surveillance and patient care in remote regions: an exploratory study of collaboration among healthcare professionals in Amazonia

The development and deployment of information technology, particularly mobile tools, to support collaboration between different groups of healthcare professionals has been viewed as a promising way to improve disease surveillance and patient care in remote regions. The effects of global climate change combined with rapid changes to land cover and use in Amazonia are believed to be contributing to the spread of vector-borne emerging and neglected diseases. This makes empowering and providing support for local healthcare providers all the more important. We investigate the use of information technology in this context to support professionals whose activities range from diagnosing diseases and monitoring their spread to developing policies to deal with outbreaks. An analysis of stakeholders, their roles and requirements, is presented which encompasses results of fieldwork and of a process of design and prototyping complemented by questionnaires and targeted interviews. Findings are analysed with respect to the tasks of diagnosis, training of local healthcare professionals, and gathering, sharing and visualisation of data for purposes of epidemiological research and disease surveillance. Methodological issues regarding the elicitation of cooperation and collaboration requirements are discussed and implications are drawn with respect to the use of technology in tackling emerging and neglected diseases

Collective Phenomena and Non-Finite State Computation in a Human Social System

We investigate the computational structure of a paradigmatic example of distributed social interaction: that of the open-source Wikipedia community. We examine the statistical properties of its cooperative behavior, and perform model selection to determine whether this aspect of the system can be described by a finite-state process, or whether reference to an effectively unbounded resource allows for a more parsimonious description. We find strong evidence, in a majority of the most-edited pages, in favor of a collective-state model, where the probability of a "revert" action declines as the square root of the number of non-revert actions seen since the last revert. We provide evidence that the emergence of this social counter is driven by collective interaction effects, rather than properties of individual users.Comment: 23 pages, 4 figures, 3 tables; to appear in PLoS ON

Robot Mindreading and the Problem of Trust

This paper raises three questions regarding the attribution of beliefs, desires, and intentions to robots. The first one is whether humans in fact engage in robot mindreading. If they do, this raises a second question: does robot mindreading foster trust towards robots? Both of these questions are empirical, and I show that the available evidence is insufficient to answer them. Now, if we assume that the answer to both questions is affirmative, a third and more important question arises: should developers and engineers promote robot mindreading in view of their stated goal of enhancing transparency? My worry here is that by attempting to make robots more mind-readable, they are abandoning the project of understanding automatic decision processes. Features that enhance mind-readability are prone to make the factors that determine automatic decisions even more opaque than they already are. And current strategies to eliminate opacity do not enhance mind-readability. The last part of the paper discusses different ways to analyze this apparent trade-off and suggests that a possible solution must adopt tolerable degrees of opacity that depend on pragmatic factors connected to the level of trust required for the intended uses of the robot