Research Talks

22 July 2026

Location: Universität Hamburg, Phil A12006 + Online

We were pleased to welcome Dr Jeremy Fix rom the University of Oxford for a research talk organised in collaboration with the Department of Philosophy at Hamburg University.

𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁:
How does intimacy go beyond morality? How does love go beyond respect? I will consider two respects in which we go beyond morality in intimate relationships. The first often goes under the label ‘reasons to love’; the second often goes under the label ‘reasons of love’. The first topic of this essay is about the basis on which we discriminate. Less grandly, the question is what makes loving someone appropriate. On what basis can I legitimately love you? The second topic of this essay is about how to relate to each other in an intimate relationship such that we do not lose our individuality and yet we do not stand to each other as separate as in a merely moral relationship. As I shall put it, how can we have interdependence and yet remain independent? I shall offer a unified Kantian answer to each of these questions, which differs from extant answers in emphasizing the relational bases of love and the volitional aspects of our interdependence.
 
Dr Jeremy Fix is the Associate Professor of Ethics and Moral Psychology in the Faculty of Philosophy at the University of Oxford and the Tutor in Philosophy at Keble College, Oxford. He works in practical philosophy, focusing on metaphysical questions about agency and ethics, especially as they relate to each other. Further research interests include the history of practical philosophy, practical reasons, and self-knowledge. 

11 May 2026

Location: Hamburg University of Technology, Am Schwarzenberg Campus

We had the pleasure of hosting Fernanda Felix de Oliveira at our institute for an inspiring research talk on the philosophy of disagreement.

Fernanda's ongoing project starts from a striking observation: in increasingly polarised societies, what is missing is often not opposition but genuine disagreement. Drawing on the theory of social acceleration, she argued that algorithmically mediated echo chambers and the constant pressure to be productive foster an "avoidance of disagreement" – a tendency to seek confirmation rather than to engage with perspectives that unsettle our own. Social polarisation, in this view, is not only an ideological phenomenon but an algorithmically mediated condition of experience.

A central thread of the talk was the reframing of dialogue as a genuinely ethical practice. True dialogue, Fernanda argued, presupposes disagreement, openness, and uncontrollability; it resists being instrumentalised towards a predefined outcome. In this context, she conceptualised responsibility as response-ability: the moral capacity to be truly present in uncomfortable situations rather than rushing to resolve them.
The lively discussion explored how acceleration reshapes our ethical relations, why attention and time should themselves be understood as ethical categories, and how philosophy might respond to the challenge of polarisation by reclaiming spaces for attentive and disagreement-rich thinking.

Prof. Dr. Fernanda Felix de Oliveira is Associate Professor of Philosophy at Pontifical Catholic University of Rio Grande do Sul (PUCRS).

22 December 2025

Location: Online

𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁:
I argue that accountability mechanisms are needed in human-AI agent relationships to ensure alignment with user and societal interests. I propose a framework according to which AI agents’ engagement is conditional on appropriate user behaviour. The framework incorporates design-strategies such as distancing, disengaging, and discouraging. I consider how this approach relates to existing sociotechnical safety alignment techniques. 
 
𝗗𝗿. 𝗕𝗲𝗻𝗷𝗮𝗺𝗶𝗻 𝗟𝗮𝗻𝗴𝗲 is a Research Assistant Professor and Junior Research Group Leader in the Ethics of AI & ML at Ludwig-Maximilians University of Munich (LMU) and the Munich Center for Machine Learning (MCML). His research focuses on the ethics of AI and technology, foundational issues in normative ethics, and organizational & business ethics. He is Associate Research Fellow at the Uehiro Oxford Institute and a member of the Zentrum für Ethik und Philosophie in der Praxis (ZEPP) at LMU. Previously he was a Visiting Researcher at Google's Responsible Innovation & AI Ethics team and held a visiting position at Hamburg University.

23 June 2025

Location: Hamburg University of Technology, Am Schwarzenberg Campus + Online

Abstract:
The rapid deployment of generative AI systems is redrawing the map of human epistemic agency – how we gather information, weigh reasons and revise our beliefs. I chart the pathways through which current AI technologies influence individual and collective inquiry. I begin by showing how recommendation algorithms, retrieval-augmented chatbots and “reflection prompts” affect different aspects of human epistemic agency. Drawing on recent experimental work with AI-mediated self-reflection, I argue that these tools can reduce unwarranted certainty – but sometimes at the cost of undermining well-founded beliefs. I then propose a framework for evaluating such trade-offs. The goal is to move the debate beyond the language of autonomy and manipulation toward a richer account of human epistemic agency.  

Dr. Dr. Marco Meyer is serving as interim professor in Political Philosophy at the University of Hamburg in summer semester 2025 and leads the research group "Culpable Ignorance – Moral Knowledge in Organizations" funded by the VolkswagenStiftung. In his research, he combines philosophy, economics, psychology, and data science to investigate ethical and epistemological questions, particularly how organizations responsibly generate and use knowledge.

14 April 2025

Location: Hamburg University of Technology, Am Schwarzenberg Campus + Online

We were excited to welcome Kate Vredenburgh from the The London School of Economics and Political Science (LSE) for a thought-provoking talk on explainable AI and discrimination.

𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁:
Proponents of algorithmic decision-making have argued that the use of algorithms can reduce discrimination, against the baseline of human decision-making. One reason is the greater explainability of the models, or the ability to provide information so that people can understand how inputs influence outputs. This talk examines the relationship between discrimination and explainability. I will argue that people are at least as explainable as AI for the purposes of detecting discrimination, and that explanations of particular algorithmic decisions are of limited use in providing legal proof to combat discrimination. These two claims should lead us to think that algorithmic decision-making is not preferable to human decision-making on the grounds that discrimination can be made more transparent and provable.

𝗞𝗮𝘁𝗲 𝗩𝗿𝗲𝗱𝗲𝗻𝗯𝘂𝗿𝗴𝗵 is an Associate Professor in the Department of Philosophy, Logic and Scientific Method at the London School of Economics. Her work spans the philosophy of social science, political philosophy, and the philosophy of AI. Her current research focuses on AI, worker autonomy, and the future of work.

23 January 2025

𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁:
AI is right now vehemently entering the field of art: Apps can create paintings of various styles and art movements with just a click. AI composes symphonies and songs, chatbots write poems. The central question of this talk will be whether AI can truly create art and what the indispensable "human factor" in the production of art is, if there is any. Against the background of the new technological possibilities that AI provides, key aesthetic concepts.

𝗣𝗿𝗼𝗳. 𝗗𝗿. 𝗖𝗮𝘁𝗿𝗶𝗻 𝗠𝗶𝘀𝘀𝗲𝗹𝗵𝗼𝗿𝗻 is Professor of Philosophy at the University of Göttingen. In 2024, she was elected a full member of the Lower Saxony Academy of Sciences and Humanities in Göttingen. From 2012-2019 she held the chair of Philosophy of Science and Technology and was permanent director of the Institute of Philosophy at the University of Stuttgart. Previously, she taught at the University of Zurich, the Humboldt University Berlin and the University of Tübingen and was as a Feodor Lynen Research Fellow at the Center of Affective Sciences in Genevea, the Collège de France and the Institut Jean Nicod for Cognitive Sciences in Paris.  She is working on the philosophy of AI, robot and machine ethics.

07 December 2024

Location: Hamburg University of Technology, Am Schwarzenberg Campus

Abstract:
This talk explores the transformative concept of Ubuntu and its pivotal role in shaping our approach to the climate and moral crises facing our world. In a world intricately woven with diverse narratives, Africa's rich heritage presents the profound concept of Ubuntu - a philosophy emphasising our interconnected humanity. Ubuntu, a term that resonates beyond mere words, is encapsulated in the African ethos as "I am because we are." This concept highlights the interconnectedness of all life and the belief that our individual and collective well-being are inextricably linked.

Popularised by Archbishop Desmond Tutu in post-apartheid South Africa, Ubuntu serves as a unifying cry across various African cultures. Its essence lies in the ethical principles of survival, solidarity, compassion, respect, dignity, and the pivotal concept of reciprocity. Reciprocity, or treating all life as we wish to be treated, is a universal principle found in numerous disciplines and indigenous worldviews.

Hoffman delves into the three levels of inner development under Ubuntu: Independence, Interdependence, and Interconnectedness. These stages guide us toward a deeper understanding of our role in the natural world and emphasise the need for a new moral framework in addressing the current climate and inner climate crises.

This talk was not just a presentation of an idea; it was an invitation to embrace a new operating system, a logic of the heart, that aligns with the shared principles of Ubuntu and reciprocity.

Wakanyi Hoffman is a Research Fellow at The New Institute Hamburg.

07 November 2024

Location: Hamburg University of Technology, Am Schwarzenberg Campus + Online

Abstract: 
Seit der Veröffentlichung von ChatGPT im November 2022 ist auf großen Sprachmodellen (large language models, LLM) basierende künstliche Intelligenz für jede:n über einen Internetbrowser zugänglich. An Universitäten gilt diese Entwicklung primär als problematisch, da ein Ansteigen von Täuschungsversuchen befürchtet wird. Die Möglichkeiten, die in Forschung und Lehre mit LLMs einhergehen, wurden und werden hingegen außerhalb der informatischen Fachcommunity kaum untersucht. Hier setze ich mit meinem Vortrag an und beleuchte die Einsetzbarkeit von KI-Tools in den Geisteswissenschaften anhand von LLMs. Dafür stelle ich zuerst meine Erfahrungen mit dem aktiven Einsatz von Chatbots in der geisteswissenschaftlichen Lehre dar. Anschließend widme ich mich der Frage, ob man sich auch als Forscherin bei komplexen Aufgaben wie der Textanalyse und -interpretation von künstlicher Intelligenz unterstützen lassen kann – und soll. Neben Überlegungen zur Bewertung des Outputs von LLMs zeige ich den Einsatz bestimmter Promptingstrategien, wie etwa dem Rollenprompting („Stell Dir vor, Du bist eine Literaturwissenschaftlerin“), und diskutiere die Ergebnisse von Experimenten, bei denen wir große Sprachmodelle direkter – über eine API – trainiert haben.

Evelyn Gius ist Professorin für Digitale Philologie und neuere deutsche Literaturwissenschaft an der Technischen Universität Darmstadt. Sie leitet dort das fortext lab, welches zur Anwendung und Methodik der computationellen Textanalyse forscht und u.a. das Annotationstool CATMA (https://catma.de/) zur Verfügung stellt.  Weitere Informationen unter https://evelyngius.de

08 May 2024

Location: Hamburg University of Technology, Am Schwarzenberg Campus + Online

Abstract:
Critiques of opaque machine learning models, used to guide consequential decisions, are getting traction in moral philosophy. According to the received view, the legitimacy of algorithmic decisions is threatened on the grounds that they undermine the rights of decision-subjects to informed self-advocacy (Vredenburgh, 2022). The appropriate mitigation strategy, in turn, is to grant decision-subjects a right to explanation (via explanation of the model output). This paper challenges the received view. More precisely, we have two objectives. The first is a critical one: we argue that existing accounts of the right to explanation prove unsatisfactory to ameliorate concerns about the moral illegitimacy of algorithmic decision-making. This is in particular due to their individualist framing, overburdening decision-subjects in a two-fold way: (i) the relevant explanations are likely to be epistemically over-demanding since their correct interpretation will require a combination of domain-knowledge and statistical proficiency that cannot be presumed for laypersons; and (ii) shifting the task to detect inadequacies to decision-subjects makes scrutinizing explanations very costly for them. Weakening the epistemic requirements of the right to explanation also lays the ground for our positive contribution. If providing explanations to decision-subjects turns out to be an inadequate amelioration strategy for opaque algorithmic decision-making, alternative moral guardrails are necessary. We outline the basic features of our proposal by discussing literature on model auditing in machine learning.

Thomas Grote is a research fellow at the Cluster of Excellence: “Machine Learning: New Perspectives for Science” at the University of Tübingen. He is also Co-PI in a project on certification and safety of ML models in healthcare, funded by the Carl-Zeiss Stiftung.

17 April 2024

Location: Audimax II, Hamburg University of Technology, Am Schwarzenberg Campus

The "Future Lecture" series featured the inaugural lecture by Prof. Dr. Maximilian Kiener on "Ethics in Technology and the Future of Morality". This event included a contribution from Prof. Dominic Wilkinson (University of Oxford) and a moderated discussion led by Dr. Andrew Graham (University of Oxford).

10 April 2024

Location: Hamburg University of Technology, Am Schwarzenberg Campus + Online

Abstract: 
Human oversight is currently discussed as a potential safeguard to counter some of the negative aspects of high-risk AI applications. This prompts a critical examination of the role and conditions necessary for what is prominently termed effective or meaningful human philosophical, and technical domains. Based on the claim that the main objective of human oversight is risk mitigation, we propose a viable understanding of effectiveness in human oversight: for human oversight to be effective, the human overseer has to have (a) sufficient causal power with regards to the system and its effects, (b) suitable epistemic access to relevant aspects of the situation, (c) self-control over their own actions, and (d) fitting intentions for their role. Furthermore, we argue that this is equivalent to saying that a human overseer is effective if and only if they are morally responsible and have fitting intentions. Against this backdrop, we suggest facilitators and inhibitors of effectiveness in human oversight when striving for practical applicability and scrutinize the upcoming AI Act of the European Union – in particular Article 14 on Human Oversight – as an exemplary regulatory framework in which we study the practicality of our understanding of effective human oversight.

Kevin Baum is a philosopher and computer scientist. He is currently head of the Center for European Research in Trusted AI (CERTAIN) at the German Research Center for Artificial Intelligence (DFKI), one of the six German competence centers for AI. He is part of the NGO Algoright e.V., a think tank for good digitalization and interdisciplinary science communication. In his talk, Kevin presented current interdisciplinary work from the Center for Perspicuous Computing (CPEC), to which he is associated.

28 February 2024

Location: Online

Abstract: 
Do Large Language Models (LLMs) have credences or degrees of belief? This question matters because a growing body of empirical research aims at quantifying LLM confidence in propositions with downstream implications for calibrating user trust in LLM assertions and combatting LLM-generated misinformation. Here an important question is whether techniques for quantifying confidence in LLMs are measuring degrees of belief on the part of the LLM; and if not, what is being measured and how it relates to credences. These questions are especially significant in relation to empirical studies which compare LLM confidence scores with human degrees of belief. In this paper we argue against the view that LLMs have credences. We consider three plausible accounts of what makes it the case that an LLM has a credence in a proposition: the reported confidence view, the output probabilities view, and the logits view. We argue that each account fails to adequately capture what it means to have a credence. The upshot is to clarify the interpretation of quantitative metrics for LLM confidence by providing a philosophical basis for denying that LLMs have credences. In doing so we not only put question to empirical comparisons between measurements of LLM confidence and reported degrees of belief in humans, but also orient discourse on confidence measurement in LLMs towards a non-mentalistic interpretation of confidence measures.

Geoff Keeling is a senior research scientist at Google, specialising in machine learning ethics. Prior to this role, Geoff served as a bioethicist at Google Health. His academic background includes a postdoctoral position at Stanford University, where he was part of the Institute for Human-Centered AI and the McCoy Family Center for Ethics in Society, and at the Leverhulme Centre for the Future of Intelligence at the University of Cambridge.