MAN 5194 Responsible AI Leadership
MAN 5194 Responsible AI Leadership
In the last few years, we have seen widespread adoption of Artificial intelligence (AI) technologies both by private and public agencies. These technologies increasingly mediate our interaction with organizations both private and public, through for example newsfeeds, recommendations, diagnostics, and analytics. AI systems are also at the heart of many governments’ efforts to reduce crime through automated policing, improve public health through precision medicine, and effective distribution of welfare benefits, among others. With the increased adoption comes also risks associated with lack of transparency, discrimination, manipulation, and dangers to the democratic processes. Thus, increasingly, companies and public agencies must navigate evolving accountability demands and be aware of developing regulation surrounding the use of AI and related technologies.
In this course, participants will explore the ethical, normative, and societal implications of AI and mechanisms to ensure that AI systems remain accountable and advance the social good. Combining theoretical foundations from data ethics, law and governance, and real-world inquiry, participants will build their ethical imaginations and skills for responsible use of AI. The course will put a strong emphasis on the positives of AI technologies (AI for good) as much as the challenges.
To this end, the course will examine legal, policy, and ethical issues that arise throughout the full lifecycle of data science, digital platforms and autonomous, artificial intelligence, systems, from data collection, to storage, processing, analysis and use, including, privacy, surveillance, security, classification, discrimination, decisional-autonomy, and duties to warn or act.
Practically, using case studies, participants will explore current applications of quantitative reasoning in organizations, algorithmic transparency, and unintended automation of discrimination via data that contains biases rooted in race, gender, class, and other characteristics. The cases will be considered in the light of existing and proposed regulations in the European Union (EU) such as the General Data Protection Regulation (GDPR), the proposed AI Act and proposed Data Governance Regulation. The course will also introduce students to the global regulatory landscape, and ongoing efforts to make AI more accountable and work towards sustainable implementations of it.
Classes will be conducted in English (the term paper may be written in Norwegian).
- Students will have an in-depth understanding of the foundational paradigms of data ethics, and their relationships to different ways of framing and resolving ethical issues.
- Students will be able to reflect on the ethical implications of data, AI and algorithms as either tools or agents in modern work and living environments.
- Students will be able to ascertain trajectories data collection and usage, AI and algorithmic development may take, and identify which are best-case and worst-case scenarios.
- Students will have an advanced understanding of what the main regulatory challenges created by the use of AI are, and how regulation can foster innovation in the realm of AI.
- Students will have thorough knowledge of how to manage data, AI and algorithms responsibly in the context of business analytics, especially in light of key legislations such as the GDRP and the EU AI Act.
- Students will be able to identify and assess the ethical impacts of a given course of action in data-driven organizations
- Students will be able to understand and apply techniques for protecting privacy, sharing data ethically, and minimizing both collective and individual harm associated with data-driven organizational processes.
- Students will be able to identify and assess compliance with GDPR and other emerging regulations
- Students will be able to identify main concerns associated with AI and assess the pros and cons of using regulation to address these concerns
- Students will practice performing an ethical audit of data-driven processes in a given organizational context.
- Critical reflection on ethical and policy issues, and to perceive the various facets and viewpoints surrounding complex data ethics questions
- Reflection of the ethical and social implications that the application of advanced data analytics may bring to business and society, and the impact biased or incomplete data sets may have on stakeholders
- Be able to apply their understanding of ethical and policy issues to problematize specific technologies, analyze and reflect critically on their impacts, think through various interventions and argue for how to develop technologies in more thoughtful ways.
- Be able to discuss and reflect on the need for regulation, and its impact on innovation.
- Ability to participate in policy debates about AI in light of its ethical, regulatory and social impacts
1. Foundationas of AI and Data Governance
- The Nature of Intelligence
- The History and Core Concepts of Artificial Intelligence
- Recent Developments and Impact of Artificial Intelligence
- The AI Life Cycle
- The Nature and Pitfalls of Data
- Navigating the Realites and Tradeoffs in Data Science
2. Practices of Responsible AI Leadership
- Al Accountability, Transparency and Explainability
- AI Risk Management
- AI and Global Perspectives
- AI and Public Service Perspectives
- Governance frameworks for AI Implementations
3. AI Regulatiosn and Governance
- Risks and Benefits of Regulation
- Regulations governing AI
- Regulation through AI
- Upcoming regulatory developments
- Participation in Regulation – AI Sandboxes
4. AI for Good
- Open-Sourcing and Communities of Practice
- AI Impact Assessment
- Co-Design and Stakeholder Engagement with and around AI
- Tackling Grand Challenges with AI
The program consists of a combination of physical gatherings and digital activities (Two physical sessions of two days over a semester). A total of approx. 75 hours.
The supervision offer will be somewhat different in the various Executive Master of Management Programs. Personal guidance and guidance will be given during the lecture. In general, students can expect advisory guidance, not evaluative guidance. The guidance offer is estimated at 2 hours per. task.
In programs where there is no compulsory attendance, it is the student's responsibility to obtain information that is given during the lecture, but which does not appear on the program's website /Insendi or other course material.
The students are evaluated through a project assignment that counts for 60% of the total grade and an individual 72-hour home exam that counts for 40%. The project assignment can be written individually or in groups of up to three people. All exams must be passed for diplomas in the program to be awarded.
The project assignment is part of the degree's independent work, cf. Regulations on requirements for a master's degree, corresponding to 9 credits per. program. For the degree Executive Master of Management, the independent work will consist of the sum of project assignments from the programs taken.
In all BI Executive courses and programs, there is a mutual requirement
for the student and the course responsible regarding the involvement of the student's experience in the planning and implementation of courses, modules and programmes. This means that the student has the right and duty to get involved with their own knowledge and practice relevance, through the active sharing of their relevant experience and knowledge.
Bachelor degree, corresponding to 180 credits from an accredited university, university college or similar educational institution. The applicant must be at least 25 years of age and at least four years of work experience. For applicants who have already completed a master’s degree, three years of work experience are required.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 60 Grouping: Group/Individual (1 - 3) Duration: 1 Semester(s) Comment: Project assignment, counts 60% of the total grade. Exam code: MAN 51941 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Individual Duration: 72 Hour(s) Comment: Written home assignment, counts 40% of the total grade. Exam code: MAN 51942 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 25 Hour(s) | Lectures on campus. |
Teaching | 50 Hour(s) | Online lectures. |
Prepare for teaching | 125 Hour(s) | |
Student's own work with learning resources | 200 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 15 ECTS credit corresponds to a workload of at least 400 hours.