what are the ethical issues in data science?
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['Unfair discrimination. ... ', 'Reinforcing human biases. ... ', 'Lack of transparency. ... ', 'Privacy. ... ', 'Lack of transparency. ... ', 'Consent and power.']
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Data Science and Artificial Intelligence, are the two most important technologies in the world today. While Data Science makes use of Artificial Intelligence in its operations, it does not completely represent AI.
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Two important ethical issues to adhere to when conducting a survey are confidentiality and informed consent. The respondent's right to confidentiality should always be respected and any legal requirements on data protection adhered to.
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Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. ... A Data Scientist will look at the data from many angles, sometimes angles not known earlier.
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['Honesty: Honestly report data, results, methods and procedures, and publication status. ... ', 'Objectivity: ... ', 'Integrity: ... ', 'Carefulness: ... ', 'Openness: ... ', 'Respect for Intellectual Property: ... ', 'Confidentiality: ... ', 'Responsible Publication:']
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The main issues discussed include the ethics of access to the IoT-based software, issues of discrimination and equality that may arise from the use of the IoT-based software, the importance of informed consent, the potential of malicious use, issues of privacy, responsibility, as well as transparency and trust with a ...
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Ethical, Legal and Professional Issues in Computing provides a comprehensive treatment of the issues facing computer professionals in today's modern, working environment. It features ethical material and codes of practice, using them throughout the book to highlight issues.
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['Unorganized data. Big data is highly versatile. ... ', 'Data storage and retention. This is one of the most obvious risks associated with big data. ... ', 'Cost management. The process of storing, archiving, analyzing, reporting and managing big data involves costs. ... ', 'Incompetent analytics. ... ', 'Data privacy.']
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['Lawfulness, fairness and transparency.', 'Purpose limitation.', 'Data minimisation.', 'Accuracy.', 'Storage limitation.', 'Integrity and confidentiality (security)', 'Accountability.', 'International Transfers.']
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Some of the important data science algorithms include regression, classification and clustering techniques, decision trees and random forests, machine learning techniques like supervised, unsupervised and reinforcement learning.
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['Volume. Massive volumes of data, challenges in cost-effective storage and analysis.', 'Velocity. The rate at which data is produced and changes, and also how fast the data must be processed to meet business requirements.', 'Variety. The diversity in the formats and types of data. ... ', 'Variability. ... ', 'Veracity. ... ', 'Value.']
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Here are some of the challenges and possible solutions. Lack of big, clean data. All computational processes need good data, and artificial intelligence is no exception. Machine learning (ML) in particular requires huge volumes of accurate data in order to train algorithms and develop predictive models.
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Other important ethical issues relate to informed consent of both donors of gametes and embryos as well as recipients of stem cells and stem cell products. Further, there has been some concern related to the commercialization of the process, justice, and the responsible conduct of research.
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Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity.
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['Problem solving intuition. Being good at problem solving is very important to being a good data scientist. ... ', 'Statistical knowledge. ... ', 'Programming in an analytic language (R or Python) ... ', 'Curiosity (keep asking why)']
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['Lawfulness, fairness and transparency.', 'Purpose limitation.', 'Data minimisation.', 'Accuracy.', 'Storage limitation.', 'Integrity and confidentiality (security)', 'Accountability.']
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ETHICAL ISSUES. Seven basic ethical issues arise in social science research: informed consent, deception, privacy (including confidentiality and anonymity), physical or mental distress, problems in sponsored research, scientific misconduct or fraud, and scientific advocacy.