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Big data ethics, also known simply as data ethics, refers to systemizing, defending, and recommending concepts of right and wrong conduct in relation to data, in particular personal data. [1] Since the dawn of the Internet the sheer quantity and quality of data has dramatically increased and is continuing to do so exponentially.
This is where I think there is a poignant opportunity for those collecting data to more ethically partner with communities, activists, and organizers to create equitable and impactful programming ...
Workplace privacy is related with various ways of accessing, controlling, and monitoring employees' information in a working environment. Employees typically must relinquish some of their privacy while in the workplace, but how much they must do can be a contentious issue. The debate rages on as to whether it is moral, ethical and legal for ...
Censorship is an issue commonly involved in the discussion of information ethics because it describes the inability to access or express opinions or information based on the belief it is bad for others to view this opinion or information. [12] Sources that are commonly censored include books, articles, speeches, art work, data, music and photos ...
When collecting data and publishing results, I can refer to the specific actions of editors or quote them using their username. I can also publish information they have made public on userpage, their edit/log history, and the results of various programs that analyse publicly available data like Interiot's edit counter.
Cuban, the CEO of Cost Plus Drugs, an online prescription service, said workers must supervise AI and ensure that the data the models are being trained on and the resulting output are correct. "It ...
Use of focus groups to study workplace bullying, therefore, serves as both an efficacious and ethical venue for collecting such data (see, e.g., Tracy, Lutgen-Sandvik, & Alberts, 2006) [29] Of course, collecting data on workplace bullying requires the research team to protect the members of the group and put an end to the bullying.
The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. I2. (Meta)data use vocabularies that follow FAIR principles I3.