Digital Ethics and Responsible Innovation
الحصول على الكتيب
للحصول على كتيب بكامل محتويات الدورة برجاء ملء النموذج التالي ببياناتك الصحيحة
Program Professional Framework
Digital Ethics and Responsible Innovation
Program Overview
AI, analytics and automation create real value, but they also raise questions of privacy, fairness, transparency and accountability that organisations can no longer treat as afterthoughts. This programme equips leaders and practitioners to embed ethics into the design and deployment of digital products and services. It covers ethical principles and national AI ethics guidance in the Gulf, personal data protection laws in Saudi Arabia and the UAE alongside GDPR, algorithmic bias, explainability, human oversight, and governance tools such as ethics impact assessments, the NIST AI Risk Management Framework and ISO/IEC 42001.
Learning Outcomes
- ✓Apply core digital ethics principles, including fairness, transparency, privacy and accountability, to a digital product decision.
- ✓Conduct an ethics and privacy impact assessment for an AI or data analytics use case.
- ✓Identify sources of algorithmic bias in data and models and propose mitigation measures.
- ✓Map organisational obligations under applicable Gulf personal data protection laws and GDPR where relevant.
- ✓Design a responsible innovation governance process with review gates, roles and documentation.
Target Audience
- Digital transformation and innovation managers
- Data protection officers and privacy specialists
- Data scientists, AI product owners and solution architects
- Risk, compliance and internal audit professionals overseeing technology
Detailed Program Modules
01Foundations of Digital Ethics
- Ethical theories applied to technology decisions
- Principles of responsible AI and data use
- National AI ethics principles in the Gulf
02Privacy and Data Protection
- Personal data protection laws in Saudi Arabia and the UAE
- GDPR concepts: lawful basis, consent and data subject rights
- Privacy by design and data minimisation
03Fairness, Bias and Explainability
- Sources of bias in data and algorithms
- Fairness testing and mitigation techniques
- Explainability and meaningful human oversight
04Responsible Innovation in Practice
- Ethics and impact assessments in product design
- Anticipating misuse and unintended consequences
- Stakeholder engagement and public trust
05Governance Frameworks and Standards
- NIST AI Risk Management Framework
- ISO/IEC 42001 AI management systems
- Ethics committees, policies and accountability
06Embedding Ethics in the Organisation
- Ethical decision-making tools for teams
- Monitoring deployed systems and incident handling
- Building a culture of responsible innovation
Training Methodology
Applied Project & Assessment
Applied Project
Participants complete an ethics and privacy impact assessment for a real or proposed AI or data use case in their organisation, with bias analysis, legal mapping, mitigation actions and a proposed governance workflow.
Assessment
Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.