Ethics in Data Science plays a very important role in ensuring that data is collected, analyzed, and used responsibly. In Class 9 Data Science (Subject Code 419), the topic “Ethical Issues in Data Science” highlights concepts like data privacy, data security, bias in data, responsible use of data, and avoiding misuse of personal information.
Ethical in Data Science Class 9 Notes
Ethical guidelines around data analysis
Ethical means doing the right thing wheneveryou are using a computer. It is important to become responsible, not hurt others and be honest and fair. The few ethical guidelines around data analysis are:
- Data governance is critical.
- Protect your customer
- Do not lie.
- Understand the role of data quality
- Private data and identity should remain private.
- Shared private information should be treated confidentially.
Need for ethical guidelines
There are many reasons why adhering to ethical guidelines in data analysis is essential.
- Promote Truth and Reduce Mistakes: Ethical rules say not to hide the truth. This helps to find the real facts and avoid errors.
- Build Public Trust: When the people know that the data is handled ethically, then they feel safe.
- Ask, Will This Help People: Before starting any analysis,People? Before first we have to understand if this will benefit others; if not, then don’t do it.
- Use Less Data: We should only collect the minimum data needed. This protects the privacy of others.
Goals of Ethical Guidelines
The goal of ethical guidelines is to help data analysts make decisions ethically. The key goals are:
- Professional integrity and accountability
- The integrity of data and methods
- Follow informed-consent rules
- Respect confidentiality and privacy
Data governance framework
What Is a Data Governance Framework?
The data governance framework is a system that helps organisations decide:
A data governance framework provides a comprehensive approach to managing, collecting, securing, and storing data. Data governance means cleaner, leaner, better data, which means better analytics, which means better decisions, which means better results. Efficient data governance means the data is consistent and credible and is not misused.
Need to govern data
- To improve data quality through efforts to identify and fix errors in data sets.
- To increase analytics accuracy and give decision-makers reliable information.
- To ensure compliance with data privacy laws and other regulations
- To implement and enforce policies that help prevent data errors and misuse
- To avoid inconsistent data in different departments and business units.
- To come to an agreement on standard data definitions for a shared understanding of data.
Goals of data governance
The goal of data governance is to create methods, sets of responsibilities, and processes to standardise, integrate, protect, and store data. The key objectives are:
- To improve internal and external communication
- To increase the value of data
- To reduce costs
- To implement compliance requirements
- To minimise risks
- To establish internal rules for data use
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