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Data Science

Grade IX

Version 1.0

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Chapter 4: Ethics in data science

At the end of this chapter, students will understand the following:

  • Ethical guidelines around data analysis
  • Need for ethical guidelines in data analysis
  • Goals of ethical guidelines in data analysis
  • Data governance framework
  • Why do we need to govern data?
  • Goals of data governance

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What is data ethics?

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Data ethics refers to the principles and practices that guide the responsible and ethical use, handling, and management of data.

Data ethics ensures respect for privacy, fairness, and transparency in decision-making processes.

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Key aspects of data ethics include:

  1. Privacy
  2. Transparency
  3. Fairness
  4. Accountability
  5. Accuracy
  6. Prevention of Harm

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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

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Need of ethical guidelines

  • Guidelines encourage facts, knowledge, and error avoidance. 

For Example, prohibitions against falsifying, fabricating, or misrepresenting data promote the truth and minimize error.

Falsifying: Manipulating research data with the intention of giving a false impression. 

Fabricating Data: A researcher creates data points or entire datasets that never existed.

Misrepresenting data: representing incorrect data.

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Need of ethical guidelines

  • Ethical guidelines in data analysis also help to build public support for the analysis. People are more likely to confide in the analysis if they can trust data quality and integrity.

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Data quality refers to the accuracy, completeness, and consistency of the data.

Data integrity means the data has been maintained in a secure, unaltered state, free from manipulation or falsification.

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Need of ethical guidelines

  • Beneficence in data analysis: Conduct new analysis, if it benefits the people. If it doesn’t, then do not do it.

It suggests that new analyses should only be conducted if they provide clear value or benefit to people. If an analysis has no meaningful positive impact or could potentially cause harm, it should not be pursued.

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Need of ethical guidelines

Minimizing data usage: refers to the practice of collecting, storing, and analyzing only the data that is necessary for a specific purpose.

Use the least amount of data necessary to meet the desired objective, understanding that reducing data usage encourages more sustainable and less risky analysis.

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Goals of ethical guidelines

The goal of ethical guidelines is to help data analysts make decisions ethically. Moreover, the ethical guidelines aim to encourage accountability by enlightening those who rely on data analysis of the standards they should expect.

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Goals of ethical guidelines

The key goals are:

• Professional integrity and accountability

• The integrity of data and methods

• Follow informed-consent rules

• Respect confidentiality and privacy

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Data governance framework

  • The data governance framework is used for determining who has control and power over data assets within a group and how such data assets can be used.
  • It includes the entities, procedures, and technology needed to handle and secure data assets

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Data governance framework

  • 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

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Why do we 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

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Goals of data governance

  • The goal of data governance is to create methods, set of responsibilities, and processes to standardize, integrate, protect, and store data.

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Goals of data governance

  • To improve internal and external communication
  • To increase the value of data
  • To reduce costs
  • To implement compliance requirements
  • To minimize risks
  • To establish internal rules for data use