Ethical Issues in the Big Data Industry
Overview
This paper examines multiple ethical challenges within the big data industry, including data quality issues, algorithmic bias and discrimination, privacy concerns, and organizational responsibilities. The author discusses how data integrity problems, predictive algorithm risks, and the need for informed consent in healthcare settings represent critical ethical considerations for organizations utilizing big data.
What this coursework covers
- Data Quality and Integrity Issues: Analysis of how correct and incorrect data, duplication, and poor data entry harm organizational decision-making
- Algorithmic Challenges and Developer Responsibility: Examination of predictive algorithms' potential to create discrimination and social engineering risks
- Data Sources and Market Dynamics: Discussion of primary and secondary big data markets and their relationships
- Privacy, Consent, and Healthcare Applications: Ethical requirements for organizations to obtain user consent and protect privacy in healthcare contexts
- Innovation and Questionable Uses: Overview of big data's benefits in creating new services while identifying potential misuse by organizations
Preview
Ethical Issues in the Big Data Industry
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Ethical Issues in the Big Data Industry
Question 1
Many of the analytics in big data are harmed by the inclusion of correct and incorrect data. A simple error in big data input can cause massive and alter the decision-making process, leading to harm across an organization. Big data costs business massive sums of money, and this can only be addressed through proactive strategies to mitigate errors. Notably, correct and incorrect data causes harm and lead to severe challenges within businesses. The two data forms are dangerous due to poor entry or even data duplication, making it hard for systems to run smoothly. Incorrect data includes wrong emails and even phone numbers, which may spread information to unauthorized individuals. On the other hand, correct data usually possesses the necessary information required by the systems (Sagiroglu & Sinanc, 2013). However, incorrect data causes harm when there is duplication of the data and cause malfunctioning of the system or provide wrong analytics.
Question 2
In the future of big data, algorithms will continue to have much influence in the coming years. However, there are numerous challenges associated with the use of algorithms. First, predictive algorithms can create discrimination as well as enable social engineering. This can produce other negative impacts. One of the main negative outcome is data volume leading to ingestion and unstructured data type due to data variety arises from turning predictive algorithms.