Honors Program: Trust in the Information Age

Lecturers:

• Prof. dr. Ronald Leenes, Prof. dr. Esther Keymolen (TLS-TILT), Dr. Irene Kamara (TLS-TILT)
• Coordinator: Prof. dr. Ronald Leenes (TLS-TILT)

Course content

This course offers an ethics (of technology) and legal perspective on trust in the information age. It provides a lens for understanding trust mediated by smart artefacts and internet platforms, Keymolen's four C model: context, curation, codifcation, construction, and provides an analysis of law as a means to 'cementing' trust and trustworthiness.

It explores a number of mechanisms (such as liability, rules, principles and standards, fiduciaries, and regulators). Examples will be drawn from areas such as product and service seals and marks, cybersecurity trustmarks, blockchain, social media platforms (fake news), AI (chatbots, deepfakes). 

Lecturer: 

• Dr. Frédéric Tomas (TSHD-DCC)

Course content

This course, titled "(Dis)Trust in Connected Societies," offers a comprehensive exploration of the multifaceted nature of trust and distrust in contemporary societies. Drawing from various disciplines including social and cognitive psychology, and communication sciences, this course delves into the origins, manifestations, and consequences of trust and distrust. Students will gain insights into the psychological, sociological, and media-related factors influencing trust formation.

Additionally, the course emphasizes the exploration of trust-building strategies through the analysis of real-world case studies. Students will investigate the implications of issues such as misinformation, conspiracy theories, and deception through the lens of frameworks such as the Truth-Default Theory or Game Theory.

Lecturer: 

• Dr. Inge van de Ven (TSHD-DCU)
• Dr.Frank Bosman (TSHD-TST)

Course content

The last decades, a whole range of unreliable narrators and characters have populated not just literary fiction, but also film (Gone Girl, American Beauty, Memento, Fight Club) and videogames (The Wolf Among Us, Spec-Op: The Line; Dear Esther; Telling Lies).  Confronted with stories, how do we decide when to adopt a trusting, and when a vigilant reading attitude and strategy? Can we use this knowledge to mitigate the risks of misinformation?

This course focuses on unreliability in stories through the disciplinary frame of narratology, the study of narrative. We look at both the textual and narrative elements and cognitive processes involved.  We analyze examples of unreliable narration in (digital) literature, film, picturebooks and videogames, and discuss how we can use narratology’s toolkit for detecting and interpreting unreliability in the context of prevalent challenges of misinformation today.

* This optional online module consists of video lectures and assignments and is meant for Honours students taking a semester abroad.

Lecturers:

• Dr. Bennett Kleinberg & postdoc (TSB-MS)
• Guest lectures by Dr. Cristian Dobre (TiSEM)

Course content

Artificial intelligence (AI) systems have increased in sophistication in the past few years. Most noteworthy are advances with various large language models (e.g., ChatGPT, FLAN-T5, Sparrow) that have set new benchmarks for the sophistication of AI models. While this brings an exciting opportunity to harness these models, introducing large language models to the general public also creates dilemmas around fairness, transparency, and robustness. Such challenges are exacerbated by the models' size, complexity and opaque nature, making it unfeasible to explain in detail analytically how they arrived at a specific output.

This course will adopt a new perspective on studying such complex computational models by resorting to methods from social and behavioural research that allow us to make inferences about the (inner) processes and, ultimately, the behaviour of these models. Students will learn the basic principles of large language models, how to interact with them, and how a machine behaviour approach can be used to assess trust in these powerful models. This course does not require prior knowledge of machine learning, AI systems or programming.

Lecturers:

• Dr. Roos Slegers
• Dr. Barend de Rooij (TSHD-DFI)

Course content

Since we are asked to place a significant degree of trust in the data-driven algorithms that regulate our lives, it is important to critically assess the conditions under which that trust is warranted. This course provides an interdisciplinary perspective and combines insights from virtue epistemology, economics and (popular) culture to examine fundamental questions about the nature of trust and trustworthiness, epistemic justification, and the epistemic virtues we should cultivate to use information technologies in responsible ways.

Drawing on critical and feminist theory, we also explore how our epistemic practices (of e.g. trust and vigilance) are mediated by power structures and can be a vector of racial, gender, and class-based injustice. This raises crucial questions about the Epistemology of (Big) Data under conditions of inequality, algorithmic bias, and (surveillance) capitalism, including barriers to trust for disadvantaged communities. To address these questions and highlight the multifaceted challenges facing epistemology in the Data Age, the course integrates philosophical theory with literature, film, (business) case studies and economics.