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There is a growing consensus in HCI and AI research that the design of AI systems needs to engage and empower stakeholders who will be affected by AI. However, the manner in which stakeholders should participate in AI design is unclear.…
Fairness in machine learning is often conceptualized narrowly in comparative, distributional terms. In studying stakeholders' concepts of fairness, we find that this framing is insufficient to capture the full range of issues raised. As an…
There has been evidence that least-commitment planners can efficiently handle planning problems that involve difficult goal interactions. This evidence has led to the common belief that delayed-commitment is the "best" possible planning…
In human-AI collaboration, a central challenge is deciding whether the AI should handle a task, be deferred to a human expert, or be addressed through collaborative effort. Existing Learning to Defer approaches typically make binary choices…
AI is now embedded in healthcare, finance, policy, and many other domains, yet genuine human-AI synergy - combined performance that exceeds what either party achieves alone - is uncommon. Meta-analyses show that AI assistance tends to…
Understanding citizens' values in participatory systems is crucial for citizen-centric policy-making. We envision a hybrid participatory system where participants make choices and provide motivations for those choices, and AI agents…
This study investigates the challenges in designing research infrastructure software for automated software publication in multi-stakeholder environments, focusing specifically on the HERMES system. Through two quantitative surveys of…
Budgetary constraints force organizations to pursue only a subset of possible innovation projects. Identifying which subset is most promising is an error-prone exercise, and involving multiple decision makers may be prudent. This raises the…
Onboarding newcomers is vital for the sustainability of open-source software (OSS) projects. To lower barriers and increase engagement, OSS projects have dedicated experts who provide guidance for newcomers. However, timely responses are…
Value-based approaches such as Value Sensitive Design (VSD) enable technology designers to engage with and integrate human values in technology through a tripartite methodology of conceptual, empirical, and technical investigations.…
Divisiveness appears to be increasing in much of the world, leading to concern about political violence and a decreasing capacity to collaboratively address large-scale societal challenges. In this working paper we aim to articulate an…
We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i.e., not problematic) aspects of data generating process,…
This dissertation presents three years of academic inquiry into the question of what role materials play in interaction design and participatory design processes. The dissertation aims at developing conceptual tools, based on Deweys…
We consider the joint design and control of discrete-time stochastic dynamical systems over a finite time horizon. We formulate the problem as a multi-step optimization problem under uncertainty seeking to identify a system design and a…
Benchmarking involves designing, running and disseminating rigorous performance assessments of methods, most often for data analysis and software tools, but the process can also be applied to experimental systems. Ideally, a benchmarking…
Closed-loop decision-making systems (e.g., lending, screening, or recidivism risk assessment) often operate under fairness and service constraints while inducing feedback effects: decisions change who appears in the future, yielding…
Fairness is commonly seen as a property of the global outcome of a system and assumes centralisation and complete knowledge. However, in real decentralised applications, agents only have partial observation capabilities. Under limited…
In this paper, we consider two paradigms that are developed to account for uncertainty in optimization models: robust optimization (RO) and joint estimation-optimization (JEO). We examine recent developments on efficient and scalable…
The digital realm has witnessed the rise of various search modalities, among which the Image-Based Conversational Search System stands out. This research delves into the design, implementation, and evaluation of this specific system,…
With the push for contact- and proximity-tracing solutions as a means to manage the spread of the pandemic, there is a distrust between the citizens and authorities that are deploying these solutions. The efficacy of the solutions relies on…