Master Ethical & AI-Driven Market Decision-Making

This course is part of AI for Business Ethics & Financial Strategy Mastery Specialization

Instructor: EDUCBA

Access provided by Xavier School of Management, XLRI

4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Analyze ethical risks, fairness, and governance considerations in real-world AI applications.
Apply responsible AI frameworks to ensure transparency, compliance, and sustainable AI adoption.
Use AI-driven analytics to interpret market insights and support informed strategic decisions.

Skills you'll gain

Strategic Decision-Making
Strategic Leadership
Social Studies
Information Privacy
Diversity and Inclusion
Data Management
Strategic Thinking
Business Ethics
Market Research
Governance
Regulatory Requirements
Artificial Intelligence
Responsible AI
Market Analysis
Data Ethics
Ethical Standards And Conduct
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Assessments

16 assignments

Taught in English

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This course is part of the AI for Business Ethics & Financial Strategy Mastery Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

Learners will develop the ability to analyze ethical risks, evaluate AI fairness, apply governance frameworks, and use AI tools to interpret market insights and guide strategic decisions. This course equips professionals with the skills to confidently navigate the ethical, regulatory, and analytical dimensions of modern AI adoption.

Throughout the program, learners explore how AI impacts business operations, social systems, employment, and data management. They gain hands-on understanding of transparency, explainability, bias mitigation, and ethical leadership—ensuring AI is implemented responsibly and sustainably. The course also introduces essential AI-driven market research methods, including sentiment analysis, customer segmentation, and trend forecasting, enabling learners to make informed, data-backed strategic decisions. What makes this course unique is its integrated approach, combining ethical AI foundations with practical market analytics, preparing learners not only to use AI, but to lead responsible AI initiatives in real business environments. By the end, learners will be equipped to drive innovation while maintaining trust, compliance, and long-term organizational value.

This module introduces learners to the foundational concepts of ethical AI in business, including the role of ethics, common sources of bias, fairness considerations, data privacy principles, security requirements, and regulatory frameworks needed for responsible AI deployment.

What's included

11 videos4 assignments

11 videos Total 52 minutes

What is Ethics in AI 4 minutes
The Importance of Ethics in Business AI 4 minutes
Overview of AI Applications in Business 3 minutes
Types of Bias in AI Models 5 minutes
Ensuring Fairness in AI Decisions 5 minutes
Case Studies Bias in AI Applications Part 1 6 minutes
Case Studies Bias in AI Applications Part 2 2 minutes
Principles of Data Privacy in AI 7 minutes
Managing Data Security in AI Systems 7 minutes
Legal and Regulatory Considerations for AI Data Management Part 1 6 minutes
Legal and Regulatory Considerations for AI Data Management Part 2 3 minutes

4 assignments Total 75 minutes

UntiUnderstanding AI Ethics and Business Applicationstled 15 minutes
Bias, Fairness, and Real-World Case Studies 15 minutes
Data Privacy and Regulatory Foundations 15 minutes
Foundations of Ethical AI in Business 30 minutes

This module explores the importance of transparency and explainability in AI systems, their impact on user trust, and the broader social and economic implications of AI. Learners also examine ethical decision-making frameworks that support fair and accountable AI use.

What's included

7 videos4 assignments

7 videos Total 36 minutes

The Need for Transparency in AI 6 minutes
Strategies for Achieving AI Explainability 5 minutes
Tools and Techniques for Transparent AI Systems 5 minutes
AI and its Impact on Employment 4 minutes
AI's Role in Social Inequality 4 minutes
Ethical Implications of AI in Decision-Making 6 minutes
Frameworks for Ethical AI Decision-Making 6 minutes

4 assignments Total 75 minutes

Making AI Transparent and Explainable 15 minutes
AI’s Social and Economic Effects 15 minutes
Ethical Decision-Making in Business AI 15 minutes
Transparency, Explainability & Social Impacts of AI 30 minutes

This module focuses on establishing governance structures, policies, and leadership practices that promote responsible AI development and deployment. Learners examine ethical AI policies, best practices, successful implementation examples, and emerging challenges that future-proof AI initiatives.

What's included

8 videos4 assignments

8 videos Total 43 minutes

Balancing Profit and Ethics in AI Implementations 7 minutes
Ethical Leadership in AI Deployment 6 minutes
Developing an Ethical AI Policy 5 minutes
Best Practices for Ethical AI Development and Deployment 5 minutes
Case Studies Successful Ethical AI Implementations 7 minutes
Emerging Ethical Challenges in AI 4 minutes
Preparing for the Future of Ethical AI 4 minutes
Continuous Learning and Improvement in AI Ethics 6 minutes

4 assignments Total 75 minutes

Balancing Profitability and Ethical Responsibility 15 minutes
Governance, Policies, and Best Practices 15 minutes
Ethical AI Success Stories and Future Challenges 15 minutes
Responsible AI Governance & Future-Proof Leadership 30 minutes

This module covers how AI transforms market research, including tools, applications, customer segmentation, sentiment analysis, and competitor insights. Learners also examine how AI supports strategic decision-making and the future outlook of AI in business intelligence.