Digital marketting

1. UNIT II
FRAMEWORK
AND MODELS

2. Introduction to AI
Governance
AI governance is a critical framework for managing the ethical use
of artificial intelligence. It involves creating policies, guidelines,
and regulations to ensure that AI systems are developed and used
in a responsible and fair manner.

3. Human-right centered design in AI
Governance
1 Human Rights Integration
Integrating human rights principles into AI governance ensures that the design,
development, and deployment of AI systems respect and protect fundamental
human rights.
2 Community Engagement
Engaging diverse communities in the design process helps prioritize the rights
and needs of marginalized groups, promoting inclusivity and fairness in AI
governance.
3 Ethical Impact Assessment
Conducting comprehensive ethical impact assessments ensures that human
rights considerations are deeply embedded in AI governance processes at all
stages.

4. Normative models for AI Governance
Legal Frameworks
Establishing legal and regulatory frameworks
helps define the boundaries and responsibilities
of AI development and use within a society.
Technical Standards
Developing technical standards provides a basis
for consistent and ethical AI governance
practices, ensuring interoperability and
compatibility across systems.
Ethical Guidelines
Formulating ethical guidelines contributes to
creating a moral compass for developers and
users, guiding the ethical use of AI technologies.

5. Role of professional norms in AI
Governance
1 Ethical Education
Professional norms provide the foundation for ethical education, shaping the behavior
and decision-making of AI professionals.
2 Accountability Frameworks
Establishing accountability frameworks ensures that AI professionals are held
responsible for the ethical implications of their work, fostering trust and transparency in
AI governance.
3 Peer Review Mechanisms
Implementing peer review mechanisms enables professionals to assess each other's
work, encouraging quality and ethical standards in AI development and deployment.

6. Teaching Machines and AI Governance
Educational Curricula
Educational programs and curricula focusing on AI governance are essential to equip
future professionals with ethics-driven AI design and management skills.
Ethics Integration
Integrating ethical considerations into machine learning and AI algorithm design is
crucial for embedding responsible decision-making into AI systems.
Training Initiatives
Developing specialized training initiatives for AI governance fosters a culture of
responsibility and awareness among AI practitioners and developers.

7. Challenges in implementing AI
Governance
Algorithmic Bias
Addressing and mitigating algorithmic bias remains a crucial challenge to ensure fairness
and equity in AI governance and decision-making processes.
Data Privacy
Protecting user privacy and ensuring data transparency pose significant challenges in the
implementation of effective AI governance policies and practices.
Regulatory Compliance
Navigating complex and evolving regulatory landscapes presents challenges for
organizations in complying with AI governance standards and regulations.

8. Case studies in AI Governance
Healthcare Applications
Exploring AI governance case
studies in healthcare highlights
challenges and best practices
in applying ethical AI principles
to life-critical systems.
Financial Sector
Examining AI governance in
the financial sector reveals
strategies and implications for
responsible AI implementation
and risk management.
Smart City Solutions
Smart city initiatives present AI
governance case studies that
demonstrate the balance
between innovation, privacy,
and public interest in urban
environments.

9. Conclusion and key takeaways
3
Ethical Principles
The foundation of AI
governance lies in promoting
ethical principles, transparency,
and accountability across all AI
initiatives.
10
Global Impact
AI governance has a global
impact, influencing economic,
social, and legal landscapes
worldwide.
100K+
Future Prospects
AI governance shapes the
future, with projections of
exponential growth and
continuous evolution in ethical
AI practices and regulations.