Wednesday July 2

Research on algorithmic fairness promotes a view on algorithmic systems as black boxes that need to be “opened” and “unpacked” to make their inner workings accessible. Understanding the black box as a mode of inquiry and knowledge making practice (rather than a thing), I will explore what exactly researchers and practitioners aim to unpack when they examine algorithmic black boxes, what they consider to be constitutive elements of these black boxes, and what is othered or perceived as “monstrous”.  Following scholarship in the social studies of science and technology (STS), the notion of the monster captures what is considered irrelevant for the constitution and inner workings of an algorithmic black box. It is what is excluded and escapes analysis. I will argue, however, that attention to the outer limits of algorithmic black boxes allows us to explore how social actors, temporalities, places, imaginaries, practices, and values matter for knowledge making about algorithmic fairness. In my talk, I will review three modes of assembling black boxes of machine learning (ML)-based systems: (1) the black box of ML data, (2) the black box of ML algorithms and trained models and (3) the black box of ML-based systems in practice. In reassembling these three distinct ML black boxes, I demonstrate how generative engagements with algorithmic black boxes and their monsters are for the critical inquiry of algorithmic fairness

1. Equality insights in the development of fairer high-risk AI systems and the control of its discriminatory impacts
   Anna Capellà i Ricart

2. Optimizing Social Network Interventions via Hypergradient-Based Recommender System Design
   Giulia De Pasquale

3. When and Why are Algorithmic Generalizations Morally Wrong? AI, Negligence and (Dis)Respect for Persons
   Cossette-Lefebvre, Hugo

4. It will be what we want it to be: sociotechnical and contested systemic risk at the core of the EU’s regulation of platforms’ AI systems
   Mateus Correia de Carvalho

5. Auditing recommender systems under the Digital Services Act: emerging evidence and paths forward
   Matteo Fabbri

6. Toward developing a Social Impact Assessment for AI in the public sector (SIA4AI) framework: key design considerations
   Vanessa Dirksen

7. Fares on Fairness: Using a Total Error Framework to Examine the Role of Measurement and Representation in Training Data on Model Fairness and Bias
   Christoph Kern

8. Why am I Still Seeing This: Measuring the Effectiveness Of Ad Controls and Explanations in AI-Mediated Ad Targeting Systems
   Jane Castleman

9. Uncertainty as a Primary Barrier for Trustworthy AI Under the EU AI Act: German SME Perspectives
   Simon Jarvers & Chiara Ullstein

1. Optimizing Social Network Interventions via Hypergradient-Based Recommender System Design
   Giulia De Pasquale

2. How is the Socio-Demographic Background of Researchers in AI & ML Related to the Values reflected in their Research?
   Paula Nauta

3. When Algorithms Play Favorites: Lookism in the Generation and Perception of Faces
   Miriam Doh

4. Re-evaluating the role of refugee integration factors for building more equitable allocation algorithms
   Clara Strasser Ceballos

5. Fairness-Regulated Dense Subgraph Discovery
   Emmanouil Kariotakis

6. Situating and Understanding Machine Unlearning, Ethically
   Iqra Aslam

7. Why am I Still Seeing This: Measuring the Effectiveness Of Ad Controls and Explanations in AI-Mediated Ad Targeting Systems
   Jane Castleman

8. Algorithmic Fairness over the Years - A Scoping Review of Research in Computer Science and Law
   Anne Oloo & Daphne Lenders

9. From Implicit to Explicit Assumptions: Why There is No Fairness Without Bias-Awareness
   Marco Favier

10. The Competing Interests Shaping Article 40
    Tim Booker

11. Uncertainty as a Primary Barrier for Trustworthy AI Under the EU AI Act: German SME Perspectives
    Simon Jarvers & Chiara Ullstein

12. The Disability Gap: Examining Representational Shortcomings in Bias Benchmarking
    Arjita Mital

13. Unmasking Style Sensitivity: A Causal Analysis of Bias Evaluation Instability in Large Language Models
    Jiaxu Zhao

14. Neurosymbolic Models for Trustworthy ADM
    Leonhard Kestel

15. QueerGen: How LLMs Reflect Societal Norms on Gender and Sexuality in Sentence Completion Task
    Mae Sosto

16. Advancing Equal Opportunity Fairness and Group Robustness through Group-Level Cost-Sensitive Deep Learning
    Modar Sulaiman

17. Reliability of the Equal Opportunity Fairness Evaluation under Selective Labeling Bias
    Niels Scholten

18. Towards Demographically Diverse Audio DeepFake Detection
    Isabella Manolaki-Sempagios