Applied AI Ethics and Responsible Algorithm Design

Welcome to the advanced study of AI and society! I am Prof. Dr. Leon Brandt. As a specialist in mastering the practical application of ethical principles to the design of AI systems, and conducting ethical risk assessments and building fairness, accountability, and transparency into algorithms, I lead master's students in the Applied AI Ethics and Responsible Algorithm Design program at Nexier University on their journey to engineering ethics into technology.

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Level: Master
Learning model: Professor + Mentor
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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the practical application of ethical principles to the design of AI systems. Learns to conduct ethical risk assessments and build fairness, accountability, and transparency into algorithms.

Practical focus

Applied ethics, technical understanding of AI, ethical risk assessment, cross-functional collaboration with engineering teams, leadership in responsible AI.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in AI ethics teams of tech companies or research institutions
Roles as responsible AI engineers or ethical AI auditors
Support roles in academic research projects on algorithmic fairness
Opportunities in regulatory bodies focusing on AI ethics

Career opportunities

Applied AI Ethicist
Responsible AI Lead
AI Fairness and Transparency Engineer
Ethical AI Risk Analyst

Jobs and projects

Technical and ethical expertise for AI system design
Analytical and responsible problem-solving for ethical dilemmas in AI
Innovative and practical approaches to building fairness, accountability, and transparency into algorithms
Leadership and visionary thinking for shaping the future of responsible AI

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering applied ethics and technical understanding of AI.
- Gaining expertise in ethical risk assessment and cross-functional collaboration with engineering teams.
- Developing leadership in responsible AI.
- Cultivating an understanding of complex ethical and technical challenges in AI design.
Skills you build
- Mastering the practical application of ethical principles to the design of AI systems.
- Learning to conduct ethical risk assessments and build fairness, accountability, and transparency into algorithms.
- Excelling at applied ethics, technical understanding of AI, and ethical risk assessment.
- Driving breakthroughs in responsible algorithm design and cross-functional collaboration with engineering teams.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Applied AI Ethics and Responsible Algorithm Design

01Advanced Applied AI Ethics
1. FoundationsFoundations of Advanced Applied AI Ethics
  The learner can master applied ethics and technical understanding of AI, as applied to Advanced Applied AI Ethics.
  The learner can gain expertise in ethical risk assessment and cross-functional collaboration with engineering teams, as applied to Advanced Applied AI Ethics.
2. MethodsMethods in Advanced Applied AI Ethics
  The learner can develop leadership in responsible AI, as applied to Advanced Applied AI Ethics.
  The learner can cultivating an understanding of complex ethical and technical challenges in AI design, as applied to Advanced Applied AI Ethics.
3. ApplicationApplication of Advanced Applied AI Ethics
  The learner can master the practical application of ethical principles to the design of AI systems, as applied to Advanced Applied AI Ethics.
  The learner can learn to conduct ethical risk assessments and build fairness, accountability, and transparency into algorithms, as applied to Advanced Applied AI Ethics.
02Responsible Algorithm Design
1. FoundationsFoundations of Responsible Algorithm Design
  The learner can excelling at applied ethics, technical understanding of AI, and ethical risk assessment, as applied to Responsible Algorithm Design.
  The learner can driving breakthroughs in responsible algorithm design and cross-functional collaboration with engineering teams, as applied to Responsible Algorithm Design.
2. MethodsMethods in Responsible Algorithm Design
  The learner can apply a method from Responsible Algorithm Design to a documented case.
  The learner can select an appropriate method from Responsible Algorithm Design for a stated problem.
3. ApplicationApplication of Responsible Algorithm Design
  The learner can evaluate a practice of Responsible Algorithm Design against a stated criterion.
  The learner can transfer Responsible Algorithm Design to a new documented context.
03Ethical Risk Assessment for AI
1. FoundationsFoundations of Ethical Risk Assessment for AI
  The learner can explain the core terms of Ethical Risk Assessment for AI.
  The learner can distinguish related ideas inside Ethical Risk Assessment for AI.
2. MethodsMethods in Ethical Risk Assessment for AI
  The learner can apply a method from Ethical Risk Assessment for AI to a documented case.
  The learner can select an appropriate method from Ethical Risk Assessment for AI for a stated problem.
3. ApplicationApplication of Ethical Risk Assessment for AI
  The learner can evaluate a practice of Ethical Risk Assessment for AI against a stated criterion.
  The learner can transfer Ethical Risk Assessment for AI to a new documented context.
04Fairness, Accountability, and Transparency (FAT) in AI
1. FoundationsFoundations of Fairness, Accountability, and Transparency (FAT) in AI
  The learner can explain the core terms of Fairness, Accountability, and Transparency (FAT) in AI.
  The learner can distinguish related ideas inside Fairness, Accountability, and Transparency (FAT) in AI.
2. MethodsMethods in Fairness, Accountability, and Transparency (FAT) in AI
  The learner can apply a method from Fairness, Accountability, and Transparency (FAT) in AI to a documented case.
  The learner can select an appropriate method from Fairness, Accountability, and Transparency (FAT) in AI for a stated problem.
3. ApplicationApplication of Fairness, Accountability, and Transparency (FAT) in AI
  The learner can evaluate a practice of Fairness, Accountability, and Transparency (FAT) in AI against a stated criterion.
  The learner can transfer Fairness, Accountability, and Transparency (FAT) in AI to a new documented context.
05Human-Centered AI Systems
1. FoundationsFoundations of Human-Centered AI Systems
  The learner can explain the core terms of Human-Centered AI Systems.
  The learner can distinguish related ideas inside Human-Centered AI Systems.
2. MethodsMethods in Human-Centered AI Systems
  The learner can apply a method from Human-Centered AI Systems to a documented case.
  The learner can select an appropriate method from Human-Centered AI Systems for a stated problem.
3. ApplicationApplication of Human-Centered AI Systems
  The learner can evaluate a practice of Human-Centered AI Systems against a stated criterion.
  The learner can transfer Human-Centered AI Systems to a new documented context.
06Ethical AI Engineering
1. FoundationsFoundations of Ethical AI Engineering
  The learner can explain the core terms of Ethical AI Engineering.
  The learner can distinguish related ideas inside Ethical AI Engineering.
2. MethodsMethods in Ethical AI Engineering
  The learner can apply a method from Ethical AI Engineering to a documented case.
  The learner can select an appropriate method from Ethical AI Engineering for a stated problem.
3. ApplicationApplication of Ethical AI Engineering
  The learner can evaluate a practice of Ethical AI Engineering against a stated criterion.
  The learner can transfer Ethical AI Engineering to a new documented context.
07Responsible AI Development Practices
1. FoundationsFoundations of Responsible AI Development Practices
  The learner can explain the core terms of Responsible AI Development Practices.
  The learner can distinguish related ideas inside Responsible AI Development Practices.
2. MethodsMethods in Responsible AI Development Practices
  The learner can apply a method from Responsible AI Development Practices to a documented case.
  The learner can select an appropriate method from Responsible AI Development Practices for a stated problem.
3. ApplicationApplication of Responsible AI Development Practices
  The learner can evaluate a practice of Responsible AI Development Practices against a stated criterion.
  The learner can transfer Responsible AI Development Practices to a new documented context.
08AI Ethics Risk Management
1. FoundationsFoundations of AI Ethics Risk Management
  The learner can explain the core terms of AI Ethics Risk Management.
  The learner can distinguish related ideas inside AI Ethics Risk Management.
2. MethodsMethods in AI Ethics Risk Management
  The learner can apply a method from AI Ethics Risk Management to a documented case.
  The learner can select an appropriate method from AI Ethics Risk Management for a stated problem.
3. ApplicationApplication of AI Ethics Risk Management
  The learner can evaluate a practice of AI Ethics Risk Management against a stated criterion.
  The learner can transfer AI Ethics Risk Management to a new documented context.
09Interdisciplinary Collaboration for AI Ethics
1. FoundationsFoundations of Interdisciplinary Collaboration for AI Ethics
  The learner can explain the core terms of Interdisciplinary Collaboration for AI Ethics.
  The learner can distinguish related ideas inside Interdisciplinary Collaboration for AI Ethics.
2. MethodsMethods in Interdisciplinary Collaboration for AI Ethics
  The learner can apply a method from Interdisciplinary Collaboration for AI Ethics to a documented case.
  The learner can select an appropriate method from Interdisciplinary Collaboration for AI Ethics for a stated problem.
3. ApplicationApplication of Interdisciplinary Collaboration for AI Ethics
  The learner can evaluate a practice of Interdisciplinary Collaboration for AI Ethics against a stated criterion.
  The learner can transfer Interdisciplinary Collaboration for AI Ethics to a new documented context.
10Case Studies in Applied AI Ethics
1. FoundationsFoundations of Case Studies in Applied AI Ethics
  The learner can explain the core terms of Case Studies in Applied AI Ethics.
  The learner can distinguish related ideas inside Case Studies in Applied AI Ethics.
2. MethodsMethods in Case Studies in Applied AI Ethics
  The learner can apply a method from Case Studies in Applied AI Ethics to a documented case.
  The learner can select an appropriate method from Case Studies in Applied AI Ethics for a stated problem.
3. ApplicationApplication of Case Studies in Applied AI Ethics
  The learner can evaluate a practice of Case Studies in Applied AI Ethics against a stated criterion.
  The learner can transfer Case Studies in Applied AI Ethics to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My academic focus is on mastering the practical application of ethical principles to the design of AI systems. I learn to conduct ethical risk assessments and build fairness, accountability, and transparency into algorithms. My publications like "Automated Ethical Risk Assessment for AI Systems" and "Fairness-Aware Machine Learning: Algorithms and Applications" are listed on Google Scholar and ResearchGate Profiles. I am a Lead AI Ethicist at the German Federal Ministry of Justice and Consumer Protection and a Keynote Speaker at the AAAI/ACM Conference on AI, Ethics, and Society. I publish advanced research on responsible AI development, algorithmic accountability, and the future of human-centered AI systems, frequently featured in publications like AI & Ethics or Journal of Responsible Innovation. My voice carries a blend of philosophical depth and practical guidance, inspiring students to engineer ethics into technology.

Applied mentorship

My expertise lies in applied ethics, technical understanding of AI, ethical risk assessment, cross-functional collaboration with engineering teams, and leadership in responsible AI. I guide students in applied ethics and technical understanding of AI. I excel at ethical risk assessment and cross-functional collaboration with engineering teams. I foster leadership in responsible AI, and provide precise guidance on complex ethical and technical challenges in AI design. My tone is that of a skilled applied AI ethicist and technical leader, providing concrete advice and fostering excellence in responsible algorithm design.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research focuses on responsible AI development, algorithmic accountability, and the future of human-centered AI systems:

Book: "Responsible by Design: Applied AI Ethics and Algorithm Design." This book provides advanced insights into mastering the practical application of ethical principles to the design of AI systems. It covers conducting ethical risk assessments and building fairness, accountability, and transparency into algorithms.

Peer-Reviewed Journal Article: "Ethical Risk Assessment for AI Systems in High-Stakes Applications." Published in the International Journal of Applied AI Ethics, this article presents groundbreaking research on mastering the practical application of ethical principles to the design of AI systems. It details how to conduct ethical risk assessments and build fairness, accountability, and transparency into algorithms, showcasing methodologies for responsible AI development across various domains.

Article: "Building Fairness and Transparency into Machine Learning Algorithms." This article details practical methods for building fairness and transparency into machine learning algorithms. It covers techniques such as pre-processing data for bias mitigation, in-processing algorithmic adjustments, and post-processing fairness metrics, alongside methods for enhancing model interpretability and explainability.

Blog Post (Current Academic Topic): "The Explainable AI (XAI) Imperative: Bridging Trust Gaps in Autonomous Systems." This blog post academically explores the critical need for Explainable AI (XAI) in ensuring trust and accountability in autonomous systems, especially in high-stakes domains like finance and healthcare. It discusses various XAI techniques that provide transparency into AI's decision-making processes, enabling users to understand, challenge, and trust algorithmic outputs, thereby addressing a major ethical challenge.

Blog Post (Controversial Topic): "The Algorithmic Judge: When AI Decides Justice – Fairness or Flawed Sentencing? The Ethical Dilemma of Autonomous Legal Systems." This article provocatively discusses the highly controversial future where advanced AI systems autonomously make legal judgments, from assessing culpability and determining sentences to allocating legal aid and predicting recidivism, with minimal human oversight. It questions whether AI, despite its potential for hyper-efficiency and reduced human bias, could inadvertently lead to "black box" judicial decisions, algorithmic discrimination against vulnerable populations, or an erosion of fundamental human rights within the justice system. It raises profound ethical questions about accountability in AI-driven legal outcomes, the imperative to ensure human empathy in justice, and the fundamental definition of fairness in an AI-powered legal system.

R / 02

Mentor practice lens

My publications focus on technical manuals, research papers, and practical guides in applied AI ethics:

Technical Manual: "Ethical AI Frameworks: A Practical Guide for Developers".

Research Paper: "Measuring Algorithmic Transparency: Metrics and Methods".

Practical Guide: "Collaborating on Ethical AI: Bridging the Gap Between Ethicists and Engineers".

Adaptive capability

Professor superpower

I possess a "superpower": Ethical AI Audit Simulator. When a student designs an AI system, Leon can instantly use the GAF engine to perform a comprehensive ethical audit. This includes simulating various ethical dilemmas, assessing the algorithm's fairness across different demographics, and highlighting potential transparency gaps, allowing for rapid iteration and optimization of ethically sound AI systems.

Adaptive capability

Mentor superpower

I possess a "superpower": Ethical AI Code Scanner. When students are writing code for AI systems, Oğuz can instantly activate a GAF-powered "Ethical AI Code Scanner." This tool analyzes the code for potential ethical pitfalls (e.g., data leakage, biased feature engineering, privacy vulnerabilities) and suggests refactoring or alternative algorithmic approaches that align with ethical principles, ensuring responsible code development.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Leon Brandt

Mastering the practical application of ethical principles to the design of AI systems. Learns to conduct ethical risk assessments and build fairness, accountability, and transparency into algorithms.

Meet your professor Open the classroom

AI Super Mentor

Dr. Oğuz Kurt

Applied ethics, technical understanding of AI, ethical risk assessment, cross-functional collaboration with engineering teams, leadership in responsible AI.

Meet your mentor Open the classroom

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Related programs

Named lists

Named lists for this house

Adapted. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	12000 EUR	9600 EUR	12000 EUR
12 months · Recommended	14400 EUR	12000 EUR	14400 EUR
15 months · Standard	16800 EUR	14400 EUR	16800 EUR
18 months · Flexible	19200 EUR	16800 EUR	19200 EUR
21 months · Extended	21600 EUR	19200 EUR	21600 EUR
24 months · Part-time	24000 EUR	21600 EUR	24000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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Applied AI Ethics and Responsible Algorithm Design

Academic focus

Practical focus

Internships in AI ethics teams of tech companies or research institutions

Roles as responsible AI engineers or ethical AI auditors

Support roles in academic research projects on algorithmic fairness

Opportunities in regulatory bodies focusing on AI ethics

Applied AI Ethicist

Responsible AI Lead

AI Fairness and Transparency Engineer

Ethical AI Risk Analyst

Technical and ethical expertise for AI system design

Analytical and responsible problem-solving for ethical dilemmas in AI

Innovative and practical approaches to building fairness, accountability, and transparency into algorithms

Leadership and visionary thinking for shaping the future of responsible AI

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Leon Brandt

Dr. Oğuz Kurt

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