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In recent years, many incidents have been reported where machine learning models exhibited discrimination among people based on race, sex, age, etc. Research has been conducted to measure and mitigate unfairness in machine learning models. For a machine learning task, it is a common practice to build a pipeline that includes an ordered set of data preprocessing stages followed by a classifier. However, most of the research on fairness has considered a single classifier based prediction task. What are the fairness impacts of the preprocessing stages in machine learning pipeline? Furthermore, studies showed that often the root cause of unfairness is ingrained in the data itself, rather than the model. But no research has been conducted to measure the unfairness caused by a specific transformation made in the data preprocessing stage. In this paper, we introduced the causal method of fairness to reason about the fairness impact of data preprocessing stages in ML pipeline. We leveraged existing metrics to define the fairness measures of the stages. Then we conducted a detailed fairness evaluation of the preprocessing stages in 37 pipelines collected from three different sources. Our results show that certain data transformers are causing the model to exhibit unfairness. We identified a number of fairness patterns in several categories of data transformers. Finally, we showed how the local fairness of a preprocessing stage composes in the global fairness of the pipeline. We used the fairness composition to choose appropriate downstream transformer that mitigates unfairness in the machine learning pipeline. CCS CONCEPTS• Software and its engineering → Software creation and management; • Computing methodologies → Machine learning.

The popularity of Python programming language has surged in recent years due to its increasing usage in Data Science. The availability of Python repositories in Github presents an opportunity for mining software repository research, e.g., suggesting the best practices in developing Data Science applications, identifying bug-patterns, recommending code enhancements, etc. To enable this research, we have created a new dataset that includes 1,558 mature Github projects that develop Python software for Data Science tasks. By analyzing the metadata and code, we have included the projects in our dataset which use a diverse set of machine learning libraries and managed by a variety of users and organizations. The dataset is made publicly available through Boa infrastructure both as a collection of raw projects as well as in a processed form that could be used for performing large scale analysis using Boa language. We also present two initial applications to demonstrate the potential of the dataset that could be leveraged by the community.

Healthcare system can be enhanced vastly with the use of modern information technology. Still now in underdeveloped and developing countries, traditional paper based system is being used in healthcare. Although very few organizations use computer based system, they could not establish a ubiquitous network among patients, physicians and government. Cloud computing is the emerging technology which can be used to develop a heterogeneous network to improve the system. In this article, a three tier cloud based application "eHealth Cloud" has been developed which will involve different parties to improve old-fashioned healthcare system. RIA (Rich Internet Application) based client, SimpleDB based server and a logic layer have been designed to build an easily accessible network. By using the "eHealth Cloud", enormous electronic medical record (EMR) will be stored everyday. This huge size of data can lead us with new research opportunities. Data mining from the large amount of EMR has been proposed. The process of data mining, a standard for exchanging data and a mining model is described. Finally, the challenges and future research options are directed.

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