As artificial intelligence continues to influence decision-making in healthcare, finance, justice, and beyond, questions around fairness, accountability, and transparency have moved to the forefront of AI development. This course introduces the ethical challenges posed by AI systems and focuses on explainable AI (XAI) as a critical component for trust, compliance, and responsible innovation.

Participants will explore the intersection of ethics, regulation, and technical solutions designed to make AI systems understandable and fair. The course offers a balance of conceptual foundations, real-world case studies, and practical tools to assess and improve the transparency of AI models—especially in high-stakes or regulated environments.

By the end of this course, participants will be able to:

Understand key ethical issues in AI development and deployment.
Identify risks related to bias, discrimination, and privacy.
Apply frameworks and tools for assessing ethical AI practices.
Explore methods and tools for achieving explainability in AI models.
Balance model performance with transparency and interpretability.
Navigate AI governance, compliance, and regulatory standards.
Build or evaluate AI systems that are socially responsible and auditable.

This course is ideal for:

Data scientists, AI engineers, and machine learning practitioners.
Product managers and tech leads developing AI-based services.
Compliance officers and legal professionals in tech organizations.
Policymakers and regulators overseeing AI governance.
Academics and researchers in AI, ethics, or digital rights.
NGO representatives and advocates focused on AI fairness.
IT consultants implementing AI solutions in sensitive domains.

This course uses a mix of instructor-led sessions, ethical dilemma discussions, real-world case analysis, and hands-on explainability exercises. Participants will explore AI audit tools, compare black-box vs. interpretable models, and evaluate case studies through ethical frameworks. The course promotes critical thinking, dialogue, and practical implementation.

Day 5 of each course is reserved for a Q&A session, which may occur off-site. For 10-day courses, this also applies to day 10

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ID	التواريخ المتاحة	المدينة	الرسوم	الإجراءات

محتوى الكورس

Section 1: Foundations of AI Ethics

Why AI ethics matters: trust, safety, and social responsibility.
Key ethical principles: fairness, accountability, transparency, autonomy.
Common risks: bias, discrimination, opacity, surveillance.
Real-world case studies: when AI fails or harms.
Ethical frameworks: consequentialism, deontology, virtue ethics in AI.
Stakeholder impact and ethical design considerations.
Role of human oversight and moral reasoning.

Section 2: Understanding Bias and Fairness in AI

How bias enters AI systems (data, design, deployment).
Types of bias: historical, algorithmic, selection, measurement.
Evaluating fairness: individual vs. group fairness metrics.
Tools for bias detection and mitigation (Fairlearn, AI Fairness 360, etc.).
Fairness trade-offs: performance vs. equity
Inclusive dataset design and demographic parity.
Guidelines for equitable model development.

Section 3: Explainable AI (XAI) Principles and Techniques

What is XAI and why it matters.
Black-box vs. white-box models: strengths and limitations.
Post-hoc explanation methods: SHAP, LIME, counterfactuals.
Interpretable models: decision trees, rule-based systems, linear models.
Domain-specific challenges in explainability (e.g., healthcare, finance).
Communicating AI decisions to non-technical stakeholders.
Hands-on walkthrough: explaining a black-box classifier.

Section 4: Governance, Compliance, and Regulation

Overview of global AI regulations and guidelines (EU AI Act, OECD, UNESCO).
Industry standards: ISO/IEC 24028, NIST AI Risk Management Framework.
Ethics review boards, model documentation, and audit trails.
AI in high-risk sectors: health, law, public services.
Data protection, consent, and the right to explanation.
Building internal governance structures for ethical AI.
Preparing for regulatory audits and external evaluations.

Section 5: Designing Ethical and Transparent AI Systems

Ethical design thinking in AI product development.
Balancing accuracy, explainability, and user trust.
Human-in-the-loop systems and oversight protocols.
Transparency by design: UI, feedback, documentation.
Building ethical AI cultures inside organizations.
Scenario planning for ethical decision-making.
Action planning: applying XAI and ethics to your projects.

تفاصيل الشهادة

عند إتمام هذه الدورة التدريبية بنجاح، سيحصل المشاركون على شهادة إتمام التدريب من Holistique Training. وبالنسبة للذين يحضرون ويكملون الدورة التدريبية عبر الإنترنت، سيتم تزويدهم بشهادة إلكترونية (e-Certificate) من Holistique Training.

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يتم منح نقاط التطوير المهني المستمر (CPD) لهذه الدورة من خلال شهاداتنا، وستظهر هذه النقاط على شهادة إتمام التدريب من Holistique Training. ووفقًا لمعايير خدمة اعتماد CPD، يتم منح نقطة CPD واحدة عن كل ساعة حضور في الدورة. ويمكن المطالبة بحد أقصى قدره 50 نقطة CPD لأي دورة واحدة نقدمها حاليًا.