OAIN 2110 - Big Data and Artificial Intelligence for Intelligence

***UPDATED for 2023/24***

Big Data and Artificial Intelligence (AI) are game changing technologies that have long captured the attention of Intelligence Communities (ICs). Despite ICs wielding substantial data capabilities positioning them as key global data collectors, there is a notable gap in exploring the epistemological, strategic, organizational, ethical, and political implications of these technologies. This course provides a comprehensive examination through analytical, technical, practical and critical lenses, delving into key techniques for large dataset collection, analysis, prediction and decision-making. It explores the dynamic nexus of technology with international relations, geopolitics, and security, highlighting opportunities and addressing uncharted challenges through a cross-disciplinary approach,

Learning Outcomes

1. Knowledge Acquisition: Providing students with essential knowledge on Big Data analytics and AI technologies used for intelligence. Preparing non-technical students to be cognizant of core concepts, methodologies, technical aspects, applications, and challenges of Big Data and AI.

2. Domain Knowledge: Making students aware of the different logics behind intelligence communities' interest in Big Data and Artificial Intelligence; understanding needs, gaps, resources, and ecosystems, while considering different contexts, organizational cultures, and geopolitical environments.

3. Critical Analysis: Developing students' critical analysis skills through reflective/analytic and problem-solving methods to define the risks and societal, ethical, and (geo)political impacts of datafication and automation of intelligence production and practice.

4. Strategic and Policy Analysis: Preparing students to provide recommendations to ICs and political decision-makers.

Professional Skills

• Research, innovation

• Technology assessment

• Critical thinking

• Strategic thinking

• Policy analysis and impact assessment

• Strategic and political consulting

Ayse CEYHAN
Séminaire
English
• Reading and preparation for each class: approximately 3 hours/week: 36 hours a semester.

• Research and writing for individual assessments: approximately 1 hour/week,:12 hours a semester.

• Research and preparation for Group Work: approximately 2 hours a week (for about 3 weeks prior to the presentation), 6 hours a semester.

Final paper: approximately 2 hours a week (for about 3 weeks prior to the submission), 6 hours a semester

Background in Intelligence and Security Studies will be helpful. Equally important is a genuine interest in emerging and disruptive technologies that will impact intelligence communities and a readiness to delve into the study of a complex subject through a cross-disciplinary approach, weekly reading assignments, in-class and take-home exercises, group work, and a final paper. Knowledge of mathematics and computer/data sciences is not required.

Spring 2023-2024
1. Reading memos and understanding, lessons learned memos, in-class/online exercises: 30%

2. Group work: 30%

3. Final paper: 30%

4. Participation, progression and the level of knowledge acquired: 10%

• This course delivers theoretical, technical and practical knowledge, combining lectures with diverse readings and case studies for active student engagement and deep knowledge acquisition.

• Readings: Given the novelty and complexity of the subject matter, readings play a pivotal role in comprehension. Students will submit 2-page reading memos for the first six sessions, followed by selected submissions in the second quarter of the semester. Students will also write two 2-3-page reflection memos on the lessons learned during Sessions 1-6 and Sessions 6-11. Feedback will be provided after each submission.

• Group work: ideally five-student groups will submit an 8-10-page paper on a given topic, which will be presented in class ( using the POLITICO debate style). Feedback will be provided after presentation.

• Individual final paper: a 6-7-page individual paper will conclude the semester. Feedback will be given in the final grading sheet.

Office Hours: scheduled for additional student support.

1. Anja Bechman, Geoffrey C.Bowles, 2019 Unsupervised by Any Other Means: Hidden Layers of Knowledge Production in Artificial Intelligence, Big Data & Society, Jan- June. Disponible / Available
2. Miah Hammond-Errey, 2023, Secrecy, sovereignty and sharing: How data and emerging technologies are transforming intelligence, United States Studies Centre at the University of Sydney, February. Available on Moodle.
3. Rob Kitchin, Gavin McArdle, 2016, What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets, Big Data and Society, January-June, 1-10.
4. Lim, Kevjn. « Big Data and Strategic Intelligence », Intelligence and national security. 2016, vol.31 no 4. p. 619635.
5. Christopher R. Moran, Joe Burton, and George Christou, 2023, "The US Intelligence Community, Global Security, and AI: From Secret Intelligence to Smart Spying" Journal of Global Security Studies, 8(2).
1. Broeders, Dennis, Erik Schrijvers, Bart van der Sloot, et al. 2017, « Big Data and security policies: Towards a framework for regulating the phases of analytics and use of Big Data », The computer law and security report. 2017, vol.33 no 3. p. 309323.
2. GCHQ, 2021, Pioneering a new national security. The Ethics of Artificial Intelligence.
3. Hare, Nick et Peter Coghill. 2016, « The future of the intelligence analysis task », Intelligence and National Security, vol.31 no 6.
4. Van Puyvelde, Damien, Stephen Coulthart, et M. Shahriar Hossain. « Beyond the buzzword: big data and national security decision-making », International affairs (London). 2017, vol.93 no 6.
5. Kathleen M.Vogel, Gwendolynne Reid, Christopher Kampe & Paul Jones, 2021, The impact of AI on intelligence analysis: tackling issues of collaboration, algorithmic transparency, accountability, and management, Intelligence and National Security, vol 36