1,721,656 research outputs found
Causality for Fair Machine Learning: Selected Topics and Applications
Full text linkThis thesis is about how we can use causality, in particular, in the form of structural causal models (SCM), to address fair machine learning (Fair ML) problems. We use SCM as auxiliary, declarative knowledge to contextualize and, in turn, enhance the formulation of such problems. We focus on automated decision-making (ADM) scenarios, in which a learned ML model, trained on past historical data, is tasked with predicting the outcomes of new incoming data. We address the following topics and applications: How can we use causal reasoning to better test for discrimination? The contribution to this question is twofold. First, we revisit the comparator used for testing the discrimination claim of a complainant. Finding (or generating) the comparator is at the center of all modeling tools for testing discrimination. We define two classes of comparators: the ceteris paribus(cp) comparator that represents an idealized comparison; and themutatis mutandis(mm) comparator that represents a “fairness given the difference” comparison. Second, we propose counterfactual situation testing (CST), a new algorithmic tool for testing discrimination that uses the mm-comparator. Using a k-NN implementation, we compare CST to its standard counterpart that uses the (cp) comparator.How can we use causal reasoning to operationalize subjective fairness? The contribution to this question is the causal perception (CP) framework, in which we use SCM to represent how individual agents interpret information. Perception occurs when two individual agents interpret the same information differently. It is largely overlooked in Fair ML since we often consider a single, objective view. With CP, we propose a partial, subjective problem formulation for Fair ML problems in which a set of decision-makers interpret and, in turn, decide differently on the same fairness problem. How can we use causal reasoning to mitigate the bias from using unrepresentative training data? We use SCM to formalize the problem of unrepresentative data, both as a sample selection bias and domain adaptation problem, and motivate the use of individual weights to correct for the bias. The contribution to this question is twofold with a focus on data science applications. First, we revisit partial dependence plots (PDP) and modify this visualization tool and propose the weighted PDP, or WPDP, as a solution. Under WPDP, the weights are used to correct for the contribution of each instance according to the underlying population distribution when drawing the plots. Second, we revisit the decision tree learning problem and propose a modification to the information gain split criterion, leading to what we define as domain adaptive decision trees (DADT). Under DADT, the entropy contribution for each instance when deciding the next split is weighted according to the target population distribution
Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference
Full text linkWe present counterfactual situation testing (CST), a causal data mining
framework for detecting discrimination in classifiers. CST aims to answer in an
actionable and meaningful way the intuitive question "what would have been the
model outcome had the individual, or complainant, been of a different protected
status?" It extends the legally-grounded situation testing of Thanh et al.
(2011) by operationalizing the notion of fairness given the difference using
counterfactual reasoning. For any complainant, we find and compare similar
protected and non-protected instances in the dataset used by the classifier to
construct a control and test group, where a difference between the decision
outcomes of the two groups implies potential individual discrimination. Unlike
situation testing, which builds both groups around the complainant, we build
the test group on the complainant's counterfactual generated using causal
knowledge. The counterfactual is intended to reflect how the protected
attribute when changed affects the seemingly neutral attributes used by the
classifier, which is taken for granted in many frameworks for discrimination.
Under CST, we compare similar individuals within each group but dissimilar
individuals across both groups due to the possible difference between the
complainant and its counterfactual. Evaluating our framework on two
classification scenarios, we show that it uncovers a greater number of cases
than situation testing, even when the classifier satisfies the counterfactual
fairness condition of Kusner et al. (2017)
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
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
Alvarez, Jose
No full textCentro Asturiano membership record of Jose Alvarez; Socio Number: 44319.https://digitalcommons.usf.edu/asturiano_membership/1001/thumbnail.jp
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
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