Type of course

The course can be taken as a singular course. The course can be organized for participants who are registered at DLN transdisciplinary course participants and DIKU’s Utforsk Project "SEER" partners and UiT and IIT(ISM) Dhanbad, India, students and during summer/winter school and/or otherwise. It will be conducted as a concentrated course in the style of summer/winter school/courses conducted under DLN/ SEER Project etc.

NOTE: First lecture will be in the end of May and will be given digitally (online).

Admission requirements

PhD students or holders of a Norwegian master´s degree of five years (300 ECTS) or 3 (180 ECTS) + 2 years (120 ECTS) or equivalent may be admitted.

PhD students must upload a document from their university stating that there are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee.

Holders of a Master´s degree must upload a Master´s Diploma with Diploma Supplement / English PhD students at UiT The Arctic University of Norway register for the course through StudentWeb.

External applicants apply for admission through SøknadsWeb. All external applicants must attach a confirmation of their status as a PhD student from their home institution.

Students who hold a Master of Science degree but are not yet enrolled as a PhD-student must attach a copy of their master's degree diploma. These students are also required to pay the semester fee.

Recommended prerequisites: Programming skills in python and / or INF-1400. Hands on knowledge of python programming for deep learning.

Application code: 9503

Application deadline: April 15th, 2025. Apply here: https://fsweb.no/soknadsweb/login.jsf

The course is limited to 25 places. Qualified applicants are ranked based on a lottery if there are more applicants than available places.

Course content

This course delves into the essential topics of Gender, Diversity, and Fairness in AI, aiming to equip students with the knowledge and skills needed to create inclusive, unbiased, and trustworthy AI systems. Students will explore the different types of bias that can occur in AI and understand their impact on society. The course covers various strategies and methodologies to reduce and eliminate bias in AI models, providing hands-on experience in applying these approaches to real-world systems. Additionally, students will learn about the importance of developing AI that reflects the values of a diverse society and promotes fairness, ensuring their work aligns with emerging laws and policies. By the end of the course, participants will be well-prepared to identify and address bias in AI, contributing to the development of ethical and equitable AI technologies.

• Introduction to Bias in AI
• Types of Bias (Gender, Racial, Socioeconomic, etc.)
• Societal Impact of Bias in AI
• Methodologies for Mitigating Bias in AI Models
• Inclusive and Diverse AI System Design
• Ethical AI Development Practices
• Fairness in AI: Definitions and Concepts
• Policy, Legal, and Regulatory Frameworks for Fair AI
• Case Studies: Biased AI Systems and Mitigation Strategies
• Hands-on Lab: Applying Fairness Algorithms to Real-world AI Models

Self-reading

• one research articles - comparison, weaknesses, strengths, application domain

Extensive lab work, self-exercises and groups are planned for competence development is also included.

Recommended prerequisites

Objectives of the course

Knowledge - The student

• Understands the different types of bias in AI, including gender, racial, socioeconomic, and algorithmic bias.
• Gains insights into the societal, ethical, and legal implications of biased AI systems.
• Acquires knowledge of fairness-aware AI design principles and bias mitigation techniques.
• Understands key policy, legal, and regulatory frameworks governing fairness and diversity in AI.
• Recognizes the importance of inclusive AI development in ensuring equitable technological advancements.

Skills - The student can

• Identify and assess bias in AI systems using fairness metrics and auditing techniques.
• Implement methodologies to detect, mitigate, and prevent bias in AI models.
• Develop AI models that align with fairness, diversity, and inclusion principles.
• Analyze real-world case studies of AI bias and propose strategies for ethical AI development.
• Apply fairness-aware algorithms and tools to practical AI applications in various domains.
• Navigate global AI policies and legal frameworks to ensure compliance with fairness regulations.

General competence - The student has developed

• A critical mindset to evaluate the ethical and social consequences of AI systems.
• The ability to design AI solutions that prioritize inclusivity, fairness, and diversity.
• Interdisciplinary collaboration skills to work with diverse teams on fairness-aware AI projects.
• Awareness of the broader societal impact of AI and the responsibility of AI developers in shaping equitable technology.
• Leadership and communication skills to advocate for fair AI practices in academic, industrial, and policy-driven settings.

Language of instruction and examination

Teaching methods

Lecture: 12 hours

Self-study session: 25 hours

Project work: spread over 4 weeks - 25 hours

Group / Self-work session: 12 hours

Hands-on session: 3 hours

Project consultation session: 2 hours

Oral Presentations/ Presentation preparation: 3 hours

Net effort (~82 hours)

Note! First lecture will be in the end of May and will be given digitally.