In today’s world which is marked by a confluence of geopolitical turbulence and stunning advances in digital technologies spearheaded by AI , digital humanism can provide a guide both for future technological research and development as well as the choices we are called to make regarding the applications of technology as individuals and as members of society. What kind of digital world can we envision in which democracy, the rule of law and basic human rights, including the pursuit of happiness, are supported by technology rather than being threatened by it? How can we work backwards from such a vision and identify the steps to be taken by academia, industry and governments required to enable it as well as identify the traps to be avoided along the way? Is the slide towards “electoral authoritarianism” irreversible or can your generation re-invent democracy and create a present that the future will be…nostalgic of. Utopia may be unattainable, but dystopia is not the only other choice!

Reinforcement learning from human feedback has emerged as one of the primary mechanisms for aligning large language models. In this framework, (human) workers report their preferences over possible responses to a query, and their reports are used to fine-tune the model. This process collects feedback from multiple raters, and therefore implicitly aggregates the raters' preferences. In this tutorial, we will discuss voting methods that underpin popular RLHF approaches, and investigate whether tools of social choice can be used to design better fine-tuning procedures.

Excursion to the Austrian Parliament.

Groups of 4-5 students select one of several use cases.

Planned reception at the City Hall of Vienna.

Abstract forthcoming

The collapse of artists’ revenues and the rise of AI are certainly not coincidental: they represent two gears within the same instrument. Streaming’s economic model rewards infinite supply at near-zero unit cost. While human-created music is finite and expensive, AI music is infinite and cheap. For a platform whose margins improve as payout obligations shrink, the logical endgame is obvious: keep the streams, remove the artists. My lecture is grounded on my experience as a professional musician, union activist, and a member of European Commission expert groups focused on Music in the Digital Age. I will attempt to peel part of this very large onion to answer such questions as:

1. How have streaming platforms changed the economics of the music business?
2. How can we protect the rights of creators when AI is trained to compete with human creation?
3. How can we build sustainable and equitable compensation models in the streaming market?
4. What role can/should legislation play to secure ethical AI to promote a fairness-oriented digital market without posing a threat to AI-related innovations?
5. What can we expect from recent EU regulatory mechanisms with a focus on the EU AI Act?
6. How can musicians/other creatives rally to gain a fair market share of streaming revenues?
7. Should unions retreat from an animosity towards AI “AI will become the worker vs. AI supports the work”? If so, what strategies for future success could be promoted?

Time permitting, we will take a look at several recent, groundbreaking cases. Interestingly, most ‘wins’ have been booked in protecting creatives from piracy lags behind.

Political communication is how political actors (such as political parties and governments), media/journalists, and citizens create and exchange messages about politics. AI is rapidly transforming this process through data-driven, emotionally targeted communication, offering new opportunities for engagement while raising risks of manipulation, polarization, and fragmented public discourse. This lecture connects empirical insights from political and communication research with the principles of Digital Humanism, asking how democratic politics can effectively communicate in the age of AI.

Alessandra Russo - combined ESSAI Keynote @ TU Vienna: Learning with Logic: Neuro-Symbolic AI for Transparent and Robust Decision-Making AI systems have achieved remarkable capabilities across a wide range of tasks, yet their adoption in high-stakes domains remains limited by fundamental concerns around interpretability, robustness, and reliable generalisation across diverse tasks and environments. Addressing these challenges requires methods that integrate data-driven learning with explicit representations of knowledge, structure, and reasoning. In the first part of this talk, I will present a neuro-symbolic perspective on these challenges, grounded in work on learning from answer sets — a form of symbolic machine learning that produces interpretable models from noisy data in a way that is data-efficient, scalable, and robust. I will explore how symbolic representations and logical semantics can be learned from raw and noisy data, integrated with neural architectures, and used to support interpretable inference and reasoning across a range of settings. The research challenges I address include learning symbolic abstractions from perception, learning robust task representations for reinforcement learning, integrating symbolic reasoning with foundation models, and controlling the semantic behaviour of generative models. In the second part, I will illustrate these ideas through applications, including clinical decision-making.
Location EI 7

One of the central challenges of AI and its impact on society is the assessment or audits of developed AI models for their alignment with ethical principles and values. Often, ethical concerns with AI models are uncovered only after their launch, making fixes particularly ad hoc. Fortunately, a legion of auditing methods is being developed. In this tutorial, I discuss the AI alignment problem and review various types of auditing methods, discussing the central obstacles to the practical realization of AI Ethics Audits. Guided by the question “how can we empower (small) developers of AI and lower the threshold for embedding ethical reflection throughout an AI’s life-cycle?,” I will briefly present a workshop method intended to be used by AI developers for ethical self-assessment. The tutorial is based on joint work with Tobias Christoph and Katta Spiel.

My talk will introduce the concept of democratic trustworthiness of AI, shifting the focus from thinking about AI users to protecting citizens; and from output expectations to protected interests within AI systems. Most contemporary debates on Trustworthy AI (TAI) emphasise technical and output-oriented criteria such as accuracy, robustness, safety, and lawfulness. While necessary and valid, this talk argues that these markers are insufficient from a strictly democratic perspective on citizen interests. Drawing on Roger Hardin’s concept of trustworthiness as “encapsulated interest,” I reframe trustworthiness in AI as a relational and political question: whose interests are structurally taken into account by AI systems? Can everyone equally afford to trust AI, independent of its reliability? The central claim is that dominant TAI frameworks tend to adopt a perspective in which the trustworthiness of AI is more about accurate information and less about power and representation. While I argue that AI should only be considered trustworthy if it demonstrably encapsulates the interests of those affected by it, especially those of vulnerable groups.

Location: Kuppelsaal
To start the discussion, Sennay Ghebreab will talk about the difference between "AI for the people" and "AI by the people" and will reflect on the progress that the Civic AI Lab in Amsterdam made towards their vision of participatory AI development. Subsequently, the panelists will discuss different approaches towards and vision for AI for and AI by the people. Panelists: Edith Elkind (Northwestern University) Elisabeth Lex (TU Graz) Josef Zehetner (Innovation in Politics Institute)

Abstract forthcoming

The ongoing process of digitalization makes the realization of a fully direct democracy—one in which all political decisions are made directly by the people—appear increasingly feasible, even in large and populous states. This might suggest that majority decisions, determined through general, direct, free, equal, and secret elections and referenda, constitute the sole defining feature of democratic decision-making. However, democracy is more than majority rule. This talk explores what democracy presupposes beyond voting procedures. Democracy entails human rights, the separation of powers, and the role of expert consultation, which must not be undermined by digital participation tools. It also requires cooperation: it cannot function if a minority defeated in a vote obstructs the implementation of the decision made by the majority or if citizens act solely as self-interest-maximizing agents. Moreover, in a democracy, political debates cannot be reduced to the assertion of the interests of various lobby and interest groups, but also involve the articulation of political positions put forward with a claim to truth (while remaining open to the possibility of error). Finally, from a philosophical perspective, the freedom and equality of citizens constitute normative preconditions of democracy.

The lecture looks at the ongoing convergence of artificial intelligence and modern warfare. On battlefields such as those in Ukraine, Gaza or Iran, the use of quasi autonomous systems and drones is already a reality. A powerful military-digital complex is taking shape, dominated largely by the U.S. and China. This automation carries enormous risks of escalation, opaque decision-making processes, and ethical dual-use issues.

Machine learning (ML), particularly deep learning, is being used everywhere. However, not always is used well, ethically and scientifically. In this talk we first do a deep dive in the limitations of supervised ML and data, its key component. We cover small data, datification, bias, predictive optimization issues, evaluating success instead of harm, and pseudoscience, among other problems. The second part is about our own limitations using ML, including different types of human incompetence: cognitive biases, unethical applications, no administrative competence, misinformation, and the impact on mental health. In the final part we discuss regulation on the use of AI and responsible AI principles, that can mitigate the problems outlined above.

A common critique of AI systems is their reduced transparency (“black boxes"), which often raises a critical challenge for ensuring that they function in a principled and fair way while complying with legal regulations. To address this issue we work on representing the internal structure of AI systems in detail. Additionally, we develop methods and tools for Auditing AI systems for several criteria, including some of those listed as requirements by the EU AI Act. An important question remains how input from social science (e.g., philosophy) can be infused in our work as part of the broader area of Digital Humanism

AI systems increasingly affect society, yet questions of democratic control remain largely unresolved. At present, many consequential choices in the development of AI systems — from data collection to deployment — are made by a small number of decision-makers, with little public input or oversight. Current debates on democratic AI governance tend to focus on institutional and regulatory frameworks at the macro level. In this talk, I complement this perspective with an approach grounded in social choice theory, the mathematical study of how conflicting preferences can be aggregated into collective decisions. I argue that opportunities for meaningful collective input arise at many stages of the machine learning development pipeline, yet are rarely recognised as such. Social choice theory provides both a modelling language to make these decision points explicit and principled criteria - drawing on notions such as fair representation, participation, and efficiency - to evaluate mechanisms for incorporating collective input. The talk aims to provide an accessible introduction to social choice theory in the context of AI governance, requiring no prior background in either area.

Project Presentations