1,721,057 research outputs found
Algorithmic Fairness Datasets: Curation, Selection, and Applications
Full text linkQuesta tesi supporta la misurazione dell'equità algoritmica da una prospettiva incentrata sui dati. In primo luogo, affrontiamo il problema della selezione dei dataset, evidenziando le pratiche sbagliate prevalenti nel settore e fornendo soluzioni per approcci più conformi ad un uso corretto. In secondo luogo, passiamo alla cura dei dataset. Progettiamo e raccogliamo un dataset per un audit dell'equità di algoritmi distribuiti a livello nazionale in Italia e studiamo la cura dei dataset in modo più ampio. Distilliamo una serie di migliori prassi per la cura dei dati basata su centinaia di dataset utilizzati nella ricerca sull'equità algoritmica. In terzo luogo, affrontiamo il problema della misurazione dell'equità in contesti pratici in cui le informazioni sugli attributi sensibili non sono disponibili. Infine, miriamo al divario tra le definizioni di equità e la loro formulazione matematica, proponendo e convalidando nuove misure di equità nell'accesso alle informazioni. Nel complesso, questa tesi esplora la tensione tra l'equità algoritmica e la complessità dell'acquisizione dei dati, favorendo l'equità, la responsabilità e la trasparenza di algoritmi sviluppati da e per una società responsabile.This thesis supports measurements of algorithmic fairness from a data-centric perspective. First, we tackle the problem of dataset selection, highlighting misguided practices prevalent in the field, and providing solutions for more principled approaches. Second, we turn to dataset curation. We design and collect datasets for a fairness audit of algorithms deployed nation-wide in Italy and zoom out to study dataset curation more broadly. We distill a set of best practices for data curation based on hundreds of datasets used in algorithmic fairness research. Third, we tackle the problem of measuring fairness in practical settings where information on sensitive attributes is not available. Finally, we target the gap between fairness definitions and their mathematical formulation, proposing and validating novel measures of equity in information access. Overall, this thesis navigates the tension between algorithmic equity and the complexity of data acquisition, supporting fairness, accountability, and transparency for technology developed by and for a responsible society
Algorithmic Fairness Datasets: the Story so Far
Full text linkData-driven algorithms are studied in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing
community of researchers has been investigating the equity of existing
algorithms and proposing novel ones, advancing the understanding of risks and
opportunities of automated decision-making for historically disadvantaged
populations. Progress in fair Machine Learning hinges on data, which can be
appropriately used only if adequately documented. Unfortunately, the
algorithmic fairness community suffers from a collective data documentation
debt caused by a lack of information on specific resources (opacity) and
scatteredness of available information (sparsity). In this work, we target data
documentation debt by surveying over two hundred datasets employed in
algorithmic fairness research, and producing standardized and searchable
documentation for each of them. Moreover we rigorously identify the three most
popular fairness datasets, namely Adult, COMPAS and German Credit, for which we
compile in-depth documentation.
This unifying documentation effort supports multiple contributions. Firstly,
we summarize the merits and limitations of Adult, COMPAS and German Credit,
adding to and unifying recent scholarship, calling into question their
suitability as general-purpose fairness benchmarks. Secondly, we document and
summarize hundreds of available alternatives, annotating their domain and
supported fairness tasks, along with additional properties of interest for
fairness researchers. Finally, we analyze these datasets from the perspective
of five important data curation topics: anonymization, consent, inclusivity,
sensitive attributes, and transparency. We discuss different approaches and
levels of attention to these topics, making them tangible, and distill them
into a set of best practices for the curation of novel resources.Comment: Published in Data Mining and Knowledge Discovery
https://doi.org/10.1007/s10618-022-00854-
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Lazy data practices harm fairness research
Full text linkData practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancement of the field by highlighting shortcomings and proposing recommendations for improvement. In this work, we present a comprehensive analysis of fair ML datasets, demonstrating how unreflective yet common practices hinder the reach and reliability of algorithmic fairness findings. We systematically study protected information encoded in tabular datasets and their usage in 280 experiments across 142 publications.
Our analyses identify three main areas of concern: (1) a \textbf{lack of representation for certain protected attributes} in both data and evaluations; (2) the widespread \textbf{exclusion of minorities} during data preprocessing; and (3) \textbf{opaque data processing} threatening the generalization of fairness research. By conducting exemplary analyses on the utilization of prominent datasets, we demonstrate how unreflective data decisions disproportionately affect minority groups, fairness metrics, and resultant model comparisons. Additionally, we identify supplementary factors such as limitations in publicly available data, privacy considerations, and a general lack of awareness, which exacerbate these challenges. To address these issues, we propose a set of recommendations for data usage in fairness research centered on transparency and responsible inclusion. This study underscores the need for a critical reevaluation of data practices in fair ML and offers directions to improve both the sourcing and usage of datasets
Variations on the Author
Full text link“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
Full text linkWe 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
Full text linkWe 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
No full textWe 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
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