Big Data Analysis

The value of big data is predicated on the ability to detect trends and patterns and more generally to make sense of the large volumes of data that is often comprised of a heterogeneous mix of format, structure, and semantics. Big data analysis is the component of the big data value chain that focuses on transforming raw acquired data into a coherent usable resource suitable for analysis. Using a range of interviews with key stakeholders in small and large companies and academia, this chapter outlines key insights, state of the art, emerging trends, future requirements, and sectorial case studies for data analysis

Why They're Worried: Examining Experts' Motivations for Signing the 'Pause Letter'

This paper presents perspectives on the state of AI, as held by a sample of experts. These experts were early signatories of the recent open letter from Future of Life, which calls for a pause on advanced AI development. Utmost effort was put into accurately representing the perspectives of our interviewees, and they have all read and approved of their representation. However, no paper could offer a perfect portrayal of their position. We feel confident in what opinions we do put forward, but we do not hold them tightly. In such dynamic times, we feel that no one should be resolved in their expectations for AI and its future.Comment: 29 pages, 10 figure

Government Strategies to Attract R&D-intensive FDI

The final publication is available at Springer via 10.1007 / s10961-008-9091-1La competición entre países y regiones por atraer las actividades de I+D de las empresas multinacionales ha aumentado sustancialmente durante los últimos años, pero no hay suficientes estudios sobre las estrategias utilizadas por los gobiernos en esta competición. Este artículo propone una taxonomía de los principales instrumentos políticos para estimular la IED intensiva en I+D y presenta los resultados de un estudio de caso comparativo de dos países europeos: España e Irlanda. La principal conclusión es que una promoción eficiente de la IED intensiva en I+D requiere una mayor coordinación entre las políticas de innovación y las políticas de atracción de la IED, que tradicionalmente han funcionado de forma independiente. Otra recomendación que emana de este estudio es que las agencias de promoción de inversiones que deseen priorizar la IED intensiva en I+D deberían reconfigurar la gama de servicios que prestan para pasar a centrarse en los servicios postinversión (o “after-care”), ya que la IED-intensiva en I+D tiende a ocurrir a través de un proceso evolutivo y no tanto a través de inversiones “greenfield”.Competition among countries and regions to attract the R&D activities of multinational enterprises has increased substantially during the last years, but the strategies used by governments in this competition remain largely unexplored. This paper proposes a taxonomy of the main policy instruments available to stimulate inward R&D-intensive FDI and presents the results of a comparative case-study of two European countries: Spain and Ireland. The main conclusion is that an efficient promotion of R&D-intensive FDI calls for a closer coordination between innovation policy and inward investment promotion, which are two policy areas that have traditionally operated rather independently from each other. In addition, inward investment agencies targeting R&D-intensive FDI are advised to reconfigure the scope of services they provide by placing more emphasis on after-care, since R&D-intensive FDI tends to be evolutionary rather than greenfield

Government strategies to attract R&D-Intensive FDI

Competition among countries and regions to attract the R&D activities of multinational enterprises has increased substantially during the last years, but the strategies used by governments in this competition remain largely unexplored. This paper proposes a taxonomy of the main policy instruments available to stimulate inward R&D-intensive FDI and presents the results of a comparative case-study of two European countries: Spain and Ireland. The main conclusion is that an efficient promotion of R&D-intensive FDI calls for a closer coordination between innovation policy and inward investment promotion, which are two policy areas that have traditionally operated rather independently from each other. In addition, inward investment agencies targeting R&D-intensive FDI are advised to reconfigure the scope of services they provide by placing more emphasis on after-care, since R&D-intensive FDI tends to be evolutionary rather than greenfield

A Nutritional Label for Rankings

Algorithmic decisions often result in scoring and ranking individuals to determine credit worthiness, qualifications for college admissions and employment, and compatibility as dating partners. While automatic and seemingly objective, ranking algorithms can discriminate against individuals and protected groups, and exhibit low diversity. Furthermore, ranked results are often unstable --- small changes in the input data or in the ranking methodology may lead to drastic changes in the output, making the result uninformative and easy to manipulate. Similar concerns apply in cases where items other than individuals are ranked, including colleges, academic departments, or products. In this demonstration we present Ranking Facts, a Web-based application that generates a "nutritional label" for rankings. Ranking Facts is made up of a collection of visual widgets that implement our latest research results on fairness, stability, and transparency for rankings, and that communicate details of the ranking methodology, or of the output, to the end user. We will showcase Ranking Facts on real datasets from different domains, including college rankings, criminal risk assessment, and financial services.Comment: 4 pages, SIGMOD demo, 3 figuress, ACM SIGMOD 201

On Discrimination Discovery and Removal in Ranked Data using Causal Graph

Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discrimination in ranked datasets and reconstruct the fair ranking. Existing methods in fairness-aware ranking are mainly based on statistical parity that cannot measure the true discriminatory effect since discrimination is causal. On the other hand, existing methods in causal-based anti-discrimination learning focus on classification problems and cannot be directly applied to handle the ranked data. To address these limitations, we propose to map the rank position to a continuous score variable that represents the qualification of the candidates. Then, we build a causal graph that consists of both the discrete profile attributes and the continuous score. The path-specific effect technique is extended to the mixed-variable causal graph to identify both direct and indirect discrimination. The relationship between the path-specific effects for the ranked data and those for the binary decision is theoretically analyzed. Finally, algorithms for discovering and removing discrimination from a ranked dataset are developed. Experiments using the real dataset show the effectiveness of our approaches.Comment: 9 page

Fairness of Exposure in Rankings

Rankings are ubiquitous in the online world today. As we have transitioned from finding books in libraries to ranking products, jobs, job applicants, opinions and potential romantic partners, there is a substantial precedent that ranking systems have a responsibility not only to their users but also to the items being ranked. To address these often conflicting responsibilities, we propose a conceptual and computational framework that allows the formulation of fairness constraints on rankings in terms of exposure allocation. As part of this framework, we develop efficient algorithms for finding rankings that maximize the utility for the user while provably satisfying a specifiable notion of fairness. Since fairness goals can be application specific, we show how a broad range of fairness constraints can be implemented using our framework, including forms of demographic parity, disparate treatment, and disparate impact constraints. We illustrate the effect of these constraints by providing empirical results on two ranking problems.Comment: In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK, 201

The Initial Screening Order Problem

In this paper we present the initial screening order problem, a crucial step within candidate screening. It involves a human-like screener with an objective to find the first k suitable candidates rather than the best k suitable candidates in a candidate pool given an initial screening order. The initial screening order represents the way in which the human-like screener arranges the candidate pool prior to screening. The choice of initial screening order has considerable effects on the selected set of k candidates. We prove that under an unbalanced candidate pool (e.g., having more male than female candidates), the human-like screener can suffer from uneven efforts that hinder its decision-making over the protected, under-represented group relative to the non-protected, over-represented group. Other fairness results are proven under the human-like screener. This research is based on a collaboration with a large company to better understand its hiring process for potential automation. Our main contribution is the formalization of the initial screening order problem which, we argue, opens the path for future extensions of the current works on ranking algorithms, fairness, and automation for screening procedures

A stemming algorithm for Latvian

The thesis covers construction, application and evaluation of a stemming algorithm for advanced information searching and retrieval in Latvian databases. Its aim is to examine the following two questions: Is it possible to apply for Latvian a suffix removal algorithm originally designed for English? Can stemming in Latvian produce the same or better information retrieval results than manual truncation? In order to achieve these aims, the role and importance of automatic word conflation both for document indexing and information retrieval are characterised. A review of literature, which analyzes and evaluates different types of stemming techniques and retrospective development of stemming algorithms, justifies the necessity to apply this advanced IR method also for Latvian. Comparative analysis of morphological structure both for English and Latvian language determined the selection of Porter's suffix removal algorithm as a basis for the Latvian sternmer. An extensive list of Latvian stopwords including conjunctions, particles and adverbs, was designed and added to the initial sternmer in order to eliminate insignificant words from further processing. A number of specific modifications and changes related to the Latvian language were carried out to the structure and rules of the original stemming algorithm. Analysis of word stemming based on Latvian electronic dictionary and Latvian text fragments confirmed that the suffix removal technique can be successfully applied also to Latvian language. An evaluation study of user search statements revealed that the stemming algorithm to a certain extent can improve effectiveness of information retrieval