1,721,267 research outputs found
Exploring Fairness Interpretability with FairnessFriend: A Chatbot Solution
No full textIn the contemporary world, artificial intelligence and machine learning algorithms are an important driver for decision-making, by leveraging real-world data for future predictions. Despite clearly improving efficiency, the lack of transparency in their predictions raises concerns about the degree of fairness of machine learning models, well highlighted by recent instances of algorithmic unfairness, from automated decisions on criminal recidivism to disease prediction. Increased user awareness of algorithmic fairness is met with a deficiency in systems guiding data analysts and practitioners in comprehending the implications of their outputs. To tackle the challenge of fairness interpretability, we propose FairnessFriend, a chatbot solution that combines data science with a human-computer interaction perspective. Given a dataset and a trained machine learning model with established fairness metrics, our system facilitates users in understanding these metrics and their significance in the context of the training data. FairnessFriend provides meanings for various statistical fairness metrics, and presents the resulting metrics values with detailed explanations, offering specific insights into their implications
FAIR-DB: A system to discover unfairness in datasets
No full textIn our everyday lives, technologies based on data play an increasingly important role. With the widespread adoption of decision making systems also in very sensitive environments, fairness has become a very important topic of discussion within the data science community. In this context, it is crucial to ensure that the data on which we base these decisions, are fair, and do not reflect historical biases. In this demo, we propose FAIR-DB (FunctionAl dependencIes to discoveR Data Bias), a system that exploiting the notion of Functional Dependency, a particular type of constraint on the data, can discover unethical behaviours in a dataset. The proposed solution is implemented as a web-based application, that, given an input dataset, generates such dependencies, walks the user trough their analysis, and finally provides many insights about bias present in the data. Our tool uses a novel metric to evaluate the unfairness present in datasets, identifies the attributes that encompass discrimination (e.g. ethnicity, sex or religion), and provides very precise information about the groups treated unequally. We also provide a detailed description of the system architecture and present a demonstration scenario, based on a real-world dataset frequently used in the field of computer ethics
FAIR-DB: Function Al dependencies to discover data bias
Full text linkComputers and algorithms have become essential tools that pervade all aspects of our daily lives; this technology is based on data and, for it to be reliable, we have to make sure that the data on which it is based on is fair and without bias. In this context, Fairness has become a relevant topic of discussion within the field of Data Science Ethics, and in general in Data Science. Today's applications should therefore be associated with tools to discover bias in data, in order to avoid (possibly unintentional) unethical behavior and consequences; as a result, technologies that accurately discover discrimination and bias in databases are of paramount importance. In this work we propose FAIR-DB (FunctionAl dependencIes to discoveR Data Bias), a novel solution to detect biases and discover discrimination in datasets, that exploits the notion of Functional Dependency, a particular type of constraint on the data. The proposed solution is implemented as a framework that focuses on the mining of such dependencies, also proposing some new metrics for evaluating the bias found in the input dataset. Our tool can identify the attributes of the database that encompass discrimination (e.g. gender, ethnicity or religion) and the ones that instead verify various fairness measures; moreover, based on special aspects of these metrics and the intrinsic nature of dependencies, the framework provides very precise information about the groups treated unequally, obtaining more insights regarding the bias present in dataset compared to other existing tools. Finally, our system also suggests possible future steps, by indicating the most appropriate (already existing) algorithms to correct the dataset on the basis of the computed results
E-FAIR-DB: Functional Dependencies to Discover Data Bias and Enhance Data Equity
No full textDecisions based on algorithms and systems generated from data have become essential tools that pervade all aspects of our daily lives; for these advances to be reliable, the results should be accurate but should also respect all the facets of data equity [11]. In this context, the concepts of Fairness and Diversity have become relevant topics of discussion within the field of Data Science Ethics and, in general, in Data Science. Although data equity is desirable, reconciling this property with accurate decision-making is a critical tradeoff, because applying a repair procedure to restore equity might modify the original data in such a way that the final decision is inaccurate w.r.t. the ultimate objective of the analysis. In this work, we propose E-FAIR-DB, a novel solution that, exploiting the notion of Functional Dependency - a type of data constraint - aims at restoring data equity by discovering and solving discrimination in datasets. The proposed solution is implemented as a pipeline that, first, mines functional dependencies to detect and evaluate fairness and diversity in the input dataset, and then, based on these understandings and on the objective of the data analysis, mitigates data bias, minimizing the number of modifications. Our tool can identify, through the mined dependencies, the attributes of the database that encompass discrimination (e.g., gender, ethnicity, or religion); then, based on these dependencies, it determines the smallest amount of data that must be added and/or removed to mitigate such bias. We evaluate our proposal both through theoretical considerations and experiments on two real-world datasets
A short account of FAIR-DB: A system to discover Data Bias
No full textComputers and algorithms are increasingly pervading our daily lives, therefore to trust these systems we have to make sure that the data they use are fair and without bias. As a result, Fairness has become a relevant topic of discussion within the field of Data Science, and technologies that accurately discover discrimination and bias present in datasets are of paramount importance. In this work we present FAIR-DB (FunctionAl dependencIes to discoveR Data Bias), a novel framework to detect biases and discover discrimination in datasets. By exploiting various kinds of functional dependencies, our tool can identify those attributes in a database that encompass discrimination (e.g. gender, ethnicity or religion) and the ones that instead satisfy various fairness criteria. We compared our framework with two state-of-the-art systems for detecting unfairness in datasets, obtaining overall similar results on a real-world dataset; specifically, the comparison highlighted that FAIR-DB not only provides very precise information about the groups treated unequally, but also that, in comparison with other existing tools, may obtain more insights regarding the bias present in datasets
Functional Dependencies to Mitigate Data Bias
No full textTechnologies based on data are frequently adopted in many sensitive environments to build models that support important and life-changing decisions. As a result, for an application to be ethically reliable, it should be associated with tools to discover and mitigate bias in data, in order to avoid (possibly unintentional) unethical behaviors and the associated consequences. In this paper we propose a novel solution that, exploiting the notion of Functional Dependency and its variants - well-known data constraints - aims at enforcing fairness by discovering and solving discrimination in datasets. Our system first identifies the attributes of a dataset that encompass discrimination (e.g. gender, ethnicity or religion), generating a list of dependencies, then, based on this information, determines the smallest set of tuples that must be added or removed to mitigate such bias in the dataset. Experimental results on two real-world datasets demonstrated that our approach can greatly improve the ethical quality of data sources
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
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