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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...-
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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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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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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...-
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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.... -
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... -
Fair Prediction with Disparate Impact A Study of Bias in Recidivism Predictio...
Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time.... -
Fair Transparent and Accountable Algorithmic Decision making Processes
The Premise, the Proposed Solutions, and the Open Challenges The combination of increased availability of large amounts of fine-grained human behavioral data and advances in... -
Fairer machine learning in the real world
Mitigating discrimination without collecting sensitive data Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used...
