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Debiaser for Multiple Variables (DEMV)
DEMV is a Debiaser for Multiple Variables that aims to increase Fairness in any given dataset, both binary and categorical, with one or more sensitive variables, while keeping...-
ipynb
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ipynb
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GiveMeSomeCreditSC
The GiveMeSomeCredit dataset - https://www.kaggle.com/c/GiveMeSomeCredit - contains different features of borrowers. The task is predicting the financial distress of a...-
ZIP
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ZIP
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Frank Experiments
Dataset with experimental results for the "Frank" hybrid decision-making system, with simulated users. Features: - CA. Co-evolutionary Accuracy. Accuracy reached by the user...-
JSON
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JSON
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HANSEN: Spoken Text Authorship Analysis
HANSEN encom- passes meticulous curation of existing speech datasets accompanied by transcripts, along- side the creation of novel AI-generated spo- ken text datasets.... -
Medical Dataset
The medical dataset contains a corpus of fully anonymized clinical text. Each document in the corpus is associated with a set of ICD-9 codes which represents the diagnosis...-
ZIP
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ZIP
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CSV
The resource: 'Churn Dataset' is not accessible as guest user. You must login to access it!
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CSV
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German Credit
In the german credit dataset each one of the 1,000 persons is classified as a good or bad creditor according to attributes like age, sex, checking_account, credit_amount,...-
CSV
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CSV
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Compas
The compas dataset contains the features used by the COMPAS algorithm for scoring defendants and their risk (Low, Medium and High), for over $4,000$ individuals. We considered...-
CSV
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CSV
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Dataset Adult
The adult dataset includes $48,842$ instances with demographic information like age, workclass, marital-status, race, capital-loss, capital-gain etc. The income attribute...-
CSV
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CSV
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Minimizing Hitting Time between Disparate Groups with Shortcut Edges
Experiments on real-world datasets to evaluate the effectiveness of the algorithms proposed in paper...-
Github
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Github
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Grounds for Trust. Essential Epistemic Opacity and Computational Reliabilism
Several philosophical issues in connection with computer simulations rely on the assumption that results of simulations are trustworthy. Examples of these include the debate... -
How the machine thinks. Understanding opacity in machine learning algorithms
This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud... -
Evaluating local explanation methods on ground truth
Evaluating local explanation methods is a difficult task due to the lack of a shared and universally accepted definition of explanation. In the literature, one of the most...-
HTML
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HTML
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Machine Learning Explainability Via Microaggregation and Shallow Decision Trees
Artificial intelligence (AI) is being deployed in missions that are increasingly critical for human life. To build trust in AI and avoid an algorithm-based authoritarian... -
Explanation in artificial intelligence. Insights from the social sciences
There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to provide more transparency to their algorithms.... -
Modeling Adversarial Behavior Against Mobility Data Privacy
Privacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed...-
HTML
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HTML
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The PGM-index a fully-dynamic compressed learned index with provable worst-ca...
We present the first learned index that supports predecessor, range queries and updates within provably efficient time and space bounds in the worst case. In the (static)...-
PDF
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PDF
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Seeing without knowing. Limitations of transparency and its application to al...
Models for understanding and holding systems accountable have long rested upon ideals and logics of transparency. Being able to see a system is sometimes equated with being able... -
Solving the Black Box Problem. A Normative Framework for Explainable Artifici...
Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial... -
Toward Accountable Discrimination Aware Data Mining
"Big Data" and data-mined inferences are affecting more and more of our lives, and concerns about their possible discriminatory effects are growing. Methods for...
