Big web data: Challenges related to data, technology, legality, and ethics

A framework for managing ethics in data science projects

“…Participants in the case study have also noted that the framework has distinct parts to identify and address ethical concerns regarding the secure collection of data from numerous sources, building a model to define target variables, comparing it to other models already in use, and finally assessing it for its impact from a behavioral science perspective. It is interesting to note that this result concurs with earlier studies 4,36,37 …”

“…It is interesting to note that this result concurs with earlier studies. 4,36,37 The primary objective was to establish a preliminary assessment of the suggested framework for overseeing ethical considerations in data science initiatives, as outlined by Wieringa. As per the findings of research methodologists, conducting an initial evaluation study is the primary measure toward gradually expanding a novel approach to practical circumstances.…”

“…34,35 Data cleaning, data modeling, and model deployment were recognized as the three main data-related issues in a data science project about data ethics. 4,36,37 With the recommendations made by Kitchenham & Charters, 38 we developed a data extraction process to find pertinent data from previously published research works (more specifically, 46 related research papers were reviewed) that are relevant to our goal of identifying the essential components for managing ethics in a data science project. As part of the data extraction process, we designed a form to keep track of the 46 publications' ideas, views, contributions, and conclusions.…”

“…In their research on the ethical issues affecting data science projects, many researchers 1,[3][4][5]34,36,37 ; concurred that the elements such as data privacy, data ownership, defining target variables used in a data model, fair evaluation of built-in data model, and foreseeing the potential consequences of the deployed data model are all set to invite ethical concerns or issues in a data science project. However, no practical answer or paradigm addressing these issues has emerged from the aforementioned studies.…”

“…It is important to pay attention to how the results of the analysis are presented as well as how the data was analyzed, and the people who design the analytics must fully comprehend and articulate how they will affect the data 34,35 . Data cleaning, data modeling, and model deployment were recognized as the three main data‐related issues in a data science project about data ethics 4,36,37 …”