Workshop on Recommender Systems and Digital Nudging

Idea and Motivation

Recommender systems influence many aspects of our daily life by shaping what information, products, or people we encounter online. At the same time, digital nudging—using recommender system design to steer user behavior—raises important questions about autonomy, responsibility and welfare. This workshop brings together researchers from computer science, economics, and philosophy to explore how recommender systems act as digital nudges and what this means for users and society. The aim is to open space for interdisciplinary exchange and to identify research questions at the intersection of these fields.

Speakers

• Yashar Deldjoo (Polytechnic University of Bari)
• Malte Dold (Pomona College)
• Holly Dykstra (University of Konstanz)
• Caterina Giannetti (University of Pisa)
• Dietmar Jannach (Univ. Klagenfurt)
• Akshath Jitendranath (Paris School of Economics)
• Silvia Milano (Univ. of Exeter/MCMP)
• Ignacio Ojea Quintana (MCMP)
• Francesco Ricci (Free University of Bozen-Bolzano)
• Martijn Willemsen (Eindhoven University of Technology)

Organiser

Dr. Silvia Milano (s.milano@exeter.ac.uk)

Registration

Registration is free but required. Please send an email to s.milano@exeter.ac.uk, specifying your name and affiliation.

Venue

Day 1 / 26 May: MCMP, Ludwigstraße 31, Room 028
Day 2 / 27 May: LMU, Richard-Wagner Straße 10, Room D 018

Programme

Day 1 / 26 May	MCMP, Ludwigstraße 31, Room 028
16:00–16:30	Registration and welcome
16:30–17:15	Silvia Milano: Algorithmic Recommendations: What’s the Problem?
17:15–17:45	Ignacio Ojea: TBD
17:45–18:00	overview of the activities and practical arrangements
18:30	optional dinner

 

Day 2 / 27 May	LMU, Richard-Wagner Straße 10, Room D 018
09:00–09:15	Coffee and welcome
09:15–10:00	Dietmar Jannach: Recommender Systems and Digital Nudging – An Introduction
10:00–10:30	break
10:30–11:00	Caterina Giannetti: TBD
11:00–11:30	Yashar Deldjoo: TBD
11:30–12:00	break
12:00–12:45	Malte Dold: Agentic preferences and nudging
12:45–14:00	lunch
14:00–14:30	Akshat Jitendranath: Hard Choices and the Choice Architecture
14:30–15:00	Holly Dykstra: Ongoing work on nudging in behavioural economics
15:00–15:30	break
15:30-16:00	Martijn Willemsen: TBD
16:00-16:15	break
16:15–17:00	Francesco Ricci: Simulation-Based Trustworthy Recommender Systems Evaluation
17:00-17:15	break
17:15–18:00	panel discussion
19:00	Workshop dinner

Abstracts

Malte Dold: “Algorithms and Autonomy: Liberal Response to the Preference-Shaping Power of Recommender Systems”

Algorithms in the form of recommender systems analyze user data to suggest items (such as products, movies, music, articles, or social connections) that users are likely to find appealing. These systems can enhance user experiences by curating content and personalizing interactions based on past behavior and the behavior of others with similar tastes. However, algorithms also play a critical role in shaping preferences across various domains. They curate decision-making environments through default options, framing effects, and gamification techniques, subtly nudging users toward certain behaviors. On social media, algorithms influence attitudes by prioritizing emotionally engaging or sensational posts. Personalized news feeds and search results further limit exposure to diverse perspectives, reducing opportunities for trial-and-error learning and fostering greater conformity within the system. Importantly, users often remain unaware of how algorithms shape their preferences, contribute to filter bubbles, or reinforce simplistic narratives. These dynamics raise complex ethical questions for liberals, particularly regarding the emphasis they place on individual autonomy.

In this project, I explore what the history of liberalism can teach us about addressing these challenges. Specifically, I examine three notions of autonomy in the liberal canon: autonomy as negative freedom or opportunities (Nozick/Hayek/Sugden), autonomy as stemming from a well-ordered hierarchy of motivations (Frankfurt/Dworkin), and autonomy as self-authorship or individuality (Mill/Raz). According to the opportunity conception, a person acts autonomously if they are free from coercion or external control, particularly by the state. The hierarchical conception understands autonomy as arising when first-order desires align with reflective higher-order preferences. The individualist conception, by contrast, views autonomy as achievable when the social conditions exist for individuals to engage in experiments of living allowing them to develop their own vision of the good life.

These different notions of autonomy lead to distinct policy responses to the preference-shaping power of algorithms. The opportunity conception cautions against state interventions in algorithms, arguing that such interventions face intricate knowledge problems and might lead to unintended consequences. The hierarchical conception endorses algorithms and recommender systems that help individuals align their desires with their higher-order preferences, while cautioning against those that undermine the reflective process. Finally, the individualist conception highlights the importance of fostering experimentation and ensuring exposure to diverse viewpoints, enabling individuals to craft their own paths. I will argue for the individualistic conception of autonomy, arguing that it provides the most relevant and effective framework for addressing the challenges posed by algorithmic recommender systems.

Holly Dykstra: “Ongoing work on nudging in behavioural economics”

This talk will present work from three projects. In the first project, I present evidence from a series of large-scale lab experiments that show that people display a strong preference for agency, but are also very willing to forego agency in order to avoid making a decision themselves; when presented with a menu of investment options, decision-makers are much more willing to forgo agency if choosing an investment option for themselves requires even a cursory consideration of the investment options. In the second, I present evidence of a “buy-in effect,” where simply increasing the upfront effort during a sign-up process by a small amount increases whether people follow-through on their intended action, including carpooling to work. Finally, in the third project, I present pilot results from an upcoming study about using generative AI to help people apply for unemployment benefits.

Dietmar Jannach: “Recommender Systems and Digital Nudging – An Introduction”

This talk provides a light-weight introduction to recommender systems and a discussion of the relationship between recommender systems and concepts of digital nudging. We start with a review of the value of recommender systems for different stakeholders, briefly discuss algorithmic strategies for making personalized recommendations, and outline how recommender systems can be evaluated. Afterwards we introduce the idea of digital nudging and how recommender systems can be seen as a mechanism to steer user behavior in certain directions.

Literature: https://www.sciencedirect.com/science/article/pii/S245195882030052X

Akshat Jitendranath : “Hard Choices and the Choice Architecture”

This paper challenges the prevailing philosophical accounts of hard choices, which typically characterize them as involving incomparability, inconsistency, vagueness, or parity between alternatives. Observe that these accounts focus exclusively on the ranking of pairs of options. Consequently, they neglect a crucial dimension: the role of the choice menu itself as an argument in rational deliberation. I argue that frameworks for adjudicating justified choices must consider not just the binary relation but the opportunity set from which these options emerge. The first part of this talk demonstrates why existing philosophical models—whether focused on incomparability, vagueness, or parity—fall short by overlooking the menu. Even when some alternatives may be incomparable or vaguely ranked when considered in isolation, there may still exist a clearly justified "best" option when the full menu is considered. The second part shows how this approach yields an important practical implication: rather than forcing individuals to navigate impossible trade-offs, institutions should avoid constructing decision environments that require making hard choices in the first place.

Francesco Ricci: “Simulation-Based Trustworthy Recommender Systems Evaluation”

Building more trustworthy recommender systems (RSs) is a societal issue. This goal has motivated the development of new types of RSs and the introduction of European regulations (Digital Service Act). A key step in the design and audit of such multistakeholder systems is their multidimensional evaluation. In fact, system's effect on users' behaviour is hard to estimate off-line, and it is risky to asses it online. A new line of research is revamping the usage of choice simulation techniques, which have the advantage of enabling offline measurement of the effect of novel recommendations and nudging strategies on users behaviour. The talk will introduce these topics and illustrate the application of simulation-based evaluation methods to promote sustainable tourism.