1,720,985 research outputs found
The Reasoning Under Uncertainty Trap: A Structural AI Risk
This report examines a novel risk associated with current (and projected) AI
tools. Making effective decisions about future actions requires us to reason
under uncertainty (RUU), and doing so is essential to many critical real world
problems. Overfaced by this challenge, there is growing demand for AI tools
like LLMs to assist decision-makers. Having evidenced this demand and the
incentives behind it, we expose a growing risk: we 1) do not currently
sufficiently understand LLM capabilities in this regard, and 2) have no
guarantees of performance given fundamental computational explosiveness and
deep uncertainty constraints on accuracy. This report provides an exposition of
what makes RUU so challenging for both humans and machines, and relates these
difficulties to prospective AI timelines and capabilities. Having established
this current potential misuse risk, we go on to expose how this seemingly
additive risk (more misuse additively contributed to potential harm) in fact
has multiplicative properties. Specifically, we detail how this misuse risk
connects to a wider network of underlying structural risks (e.g., shifting
incentives, limited transparency, and feedback loops) to produce non-linear
harms. We go on to provide a solutions roadmap that targets multiple leverage
points in the structure of the problem. This includes recommendations for all
involved actors (prospective users, developers, and policy-makers) and enfolds
insights from areas including Decision-making Under Deep Uncertainty and
complex systems theory. We argue this report serves not only to raise awareness
(and subsequently mitigate/correct) of a current, novel AI risk, but also
awareness of the underlying class of structural risks by illustrating how their
interconnected nature poses twin-dangers of camouflaging their presence, whilst
amplifying their potential effects.Comment: 51 pages (excluding references), 7 chapters, 9 figure
Opinion Cascades and Echo-Chambers in Online Networks:A Proof of Concept Agent-Based Model
Going Beyond Counting First Authors in Author Co-citation Analysis
The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Opinion Cascades and Echo-Chambers in Online Networks: A Proof of Concept Agent-Based Model
In online networks, the polarization of opinions (e.g., regarding presidential elections or referenda) has been associated with the creation of “echo-chambers” of like-minded peers, secluded from those of contrary viewpoints. Previous work has commonly attributed such phenomena to self-regarding preferences (e.g., confirmation bias), individual differences, and the pre-dispositions of users, with clusters forming over repeated interactions. The present work provides a proof of concept Agent-Based Model that demonstrates online networks are susceptible to echo-chambers from a single opinion cascade, due to the spatiotemporal order induced by lateral transmission. This susceptibility is found to vary as a function of degree of interconnectivity and opinion strength. Critically, such effects are found despite globally proportionate levels of opinions, equally rational agents (i.e. absent conformity, confirmation bias or pre-disposition architecture), and prior to cyclical interactions. The assumptions and implications of this work, including the value of Agent-Based Modelling to cognitive psychology, are discussed
Variations on the Author
“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
We provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
We conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
We have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
- …
