Introduction to Data Science unit 5 pptx

BCA 602- Introduction to
Data Science

Unit No. V
Centre for Distance and Online Education
Ethics and Data Science

After completion of Unit-V, you will be able to understand
the following areas:
 Doing Good Data Science & Data Ownership
 The Five Cs - Implementing the Five Cs
 Ethics and Security Training and Developing Guiding Principles.
 Building Ethics into a Data-Driven Culture, Regulation, Building Our Future,
Case Study.
Objectives

Doing Good Data Science & Data Ownership
Defining Data Science Ethics
Data scientists need to have a firm understanding of the concept of data
science ethics and the importance of upholding ethical principles.
Benefits of Data Ethics
Build trust and goodwill with their customers
Reduce the risk of unintended bias and show that
their decision-making is fair
Ensure that they comply with legal requirements

Government Norms for Collecting and Storing
Data under Legal and Regulatory Frameworks
Government around the world have established legal and regulatory
frameworks to govern the collecting and storing the data. THese norms
aims to protect individuals rights, ensure data privacy, and maintain the
integrity of information. Key aspects includes are:
 Data Protection Laws: Many countries have enacted data protection
laws that regulates that
 Security Standards: Governments often established standards and
regulations for the security of stored data to prevent unauthorized
access and protect against data breaches.
 Data Retention Policies: Governments may set guidelines on how
long organizations can retain specific types of data and ensures that
the data is not kept indefinitely and is disposed or appropriate.

Government Norms for Collecting and Storing
Data under Legal and Regulatory Frameworks
 Compliance and Audits: Compliance checks and audits may be
require to ensure that a particular organizations adhere to data
protection laws and other regulations.
 International Data Transfer Regulations: Some countries
restricts the data to transfer across the country , So for that
Cross boarder data transfer mechanisms, such as Standard
Contractual Clauses, may need to be implemented.

Doing Good Data Science & Data Ownership
Definition: Practicing data science in a manner that benefits individuals and
communities without causing harm.
Core Values:
• Fairness: Avoid discrimination or bias in data algorithms (e.g., gender or
racial bias in hiring algorithms).
• Transparency: Clearly explain data collection, processing, and model
decision-making.
• Accountability: Take responsibility for the consequences of data-driven
decisions.
• Responsibility: Consider social impacts, ensure ethical review processes are
in place.

Data Ownership (What is it) : The concept that individuals have the right to control
their personal data.
Key Aspects:
• Consent: Data should be collected only with informed consent.
• Access Rights: Individuals should be able to view, modify, or delete their
data.
• Legal Protections: Laws like GDPR (Europe), CCPA (California) give users legal
rights over data.
Ethical Implications:
• Misuse or selling of data without consent violates trust and privacy.
• Organizations must be transparent about data usage and ensure proper data
governance.

The Five Cs – Implementing the Five Cs
The Five Cs are essential pillars of ethical data science:
1.Consent – Users must give informed consent for data collection
and usage.
2.Clarity – Be clear about what data is collected and how it is used.
3. Consistency – Apply ethical standards consistently across projects.
4. Control – Give users control over their data (access, update, delete).
5. Compassion – Consider the human impact of data decisions.

1. Consent
• Individuals must explicitly agree to the collection and use of their data.
• Must be informed, freely given, and revocable.
• Example: Opt-in checkboxes for newsletters or app tracking.
2. Clarity
• Be clear about what data is being collected, why, and how it will be used.
• Use plain language in privacy policies and consent forms.
• Avoid hidden clauses or complex legal jargon.
3. Consistency
• Apply ethical standards and privacy policies across all departments, platforms,
and projects.
• Regular updates and audits to maintain alignment with policies.

4. Control
• Users should be able to manage their data (e.g., update, delete,
limit sharing).
• Design systems that empower users with data control dashboards.
5. Compassion
• Consider the human impact of data decisions.
• Don’t treat data subjects as just numbers; respect their dignity.
• Example: Avoid using data to manipulate emotions or behavior (as
in targeted political ads).

Ethics and Security Training & Developing Guiding Principles
Ethics Training
 Essential for all employees in data roles.
 Includes case studies, role-playing, and workshops.
 Focus on:
 Data bias awareness.
 Conflict of interest recognition.
 Understanding ethical dilemmas.
Security Training
 Protect data from unauthorized access, leaks, and breaches.
 Training includes:
 Phishing awareness.
 Password management.
 Data encryption and secure storage practices.
 Ongoing refresher courses are important.

Developing Guiding Principles
 Organizations should define a Code of Ethics.
 Should reflect:
 Transparency
 Privacy
 Inclusiveness
 Integrity
 Align with global frameworks like:
 OECD Privacy Guidelines
 IEEE Ethically Aligned Design
 ACM Code of Ethics

Building Ethics into a Data-Driven Culture
Why it Matters
A culture of ethics ensures long-term trust and sustainable innovation.
Helps avoid scandals, lawsuits, and public backlash.
How to Build It - Leadership Role:
• Leaders must endorse and live by ethical standards.
• Ethics committees or Chief Ethics Officers should be involved in
decision-making.
Cross-functional Collaboration:
• Involve ethicists, lawyers, product managers, and data scientists in
project planning.

Ethics by Design:
• Embed ethics into data science lifecycle (design, data
collection, modeling, deployment).
• Use tools like “Ethical Impact Assessments.”
Feedback Mechanisms:
• Enable users to report ethical concerns.
• Include internal whistleblower protections.
Recognition & Rewards:
• Recognize ethical behavior to encourage a positive culture.

Regulation, Building Our Future & Case Study
Regulation
General Data Protection Regulation (GDPR) – EU regulation that gives
individuals control over personal data.
California Consumer Privacy Act (CCPA) – Protects privacy rights for
California residents.
India’s DPDP Bill (2023) – India's framework for personal data protection.
Key Elements of Regulation
 Right to be informed.
 Right to access and correct data.
 Right to erasure (right to be forgotten).
 Data portability.

Building Our Future
Data ethics will shape the future of AI, IoT, healthcare, education, etc.
Focus areas:
• AI Governance: Ethical frameworks for autonomous systems.
• Sustainable Innovation: Balancing progress with rights and
dignity.
• Equity: Prevent algorithmic discrimination.
• Digital Literacy: Educate public about data rights.

Case Study: Cambridge Analytica and Facebook
What Happened:
• Personal data of millions was harvested from Facebook without proper
consent.
• Used to influence elections via targeted political ads.
Ethical Failures:
• Lack of transparency in data use.
• Inadequate consent and privacy controls.
• No proper oversight.
Lessons Learned:
• Consent must be meaningful and informed.
• Tech companies must be transparent and accountable.
• Regulations are critical to protecting public interests.

More Information
Videos Link:
https://www.youtube.com/watch?v=6BALGpobam0
https://www.youtube.com/watch?v=WU7McTUumxU
Website Link:
https://www.geeksforgeeks.org/ethics-in-data-science-and-proper-privacy-and-usag
e-of-data/
https://online.maryville.edu/online-masters-degrees/data-science/careers/data-sci
ence-ethics-issues-and-strategies/

Summary
• Doing Good Data Science & Data Ownership
• The Five Cs, Implementing the Five Cs
• Ethics and Security Training and Developing Guiding
Principles
• Building Ethics into a Data-Driven Culture, Regulation,
Building Our Future, Case Study.

Any Questions ?

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Introduction to Data Science unit 5 pptx

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