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Participatory Design means recognizing that those who will be affected by a future technology should have an active say in its creation. Yet, despite continuous interest in involving people as future users and consumers into designing novel and innovative future technology, participatory approaches in technology design remain relatively underdeveloped in the German HCI community. This article brings together the diversity of voices, domains, perspectives, approaches, and methods that collectively shape Participatory Design in Germany. In the following, we (1) outline our understanding of participatory practice and how it is different from mere user involvement; (2) reflect current issues of participatory and fair technology design within the German Participatory Design community; and (3) discuss tensions relevant to the field, that we expect to arise in the future, and which we derived from our 2021 workshop through a speculative method. We contribute an introduction and an overview of current themes and a speculative outlook on future issues of Participatory Design in Germany. It is meant to inform, provoke, inspire and, ultimately, invite participation within the wider Computer Science community.

The global infrastructure of the Web, designed as an open and transparent system, has a significant impact on our society. However, algorithmic systems of corporate entities that neglect those principles increasingly populated the Web. Typical representatives of these algorithmic systems are recommender systems that influence our society both on a scale of global politics and during mundane shopping decisions. Recently, such recommender systems have come under critique for how they may strengthen existing or even generate new kinds of biases. To this end, designers and engineers are increasingly urged to make the functioning and purpose of recommender systems more transparent. Our research relates to the discourse of algorithm awareness, that reconsiders the role of algorithm visibility in interface design. We conducted online experiments with 105 participants using MTurk for the recommender system Recoin, a gadget for Wikidata. In these experiments, we presented users with one of a set of three different designs of Recoin's user interface, each of them exhibiting a varying degree of explainability and interactivity. Our findings include a positive correlation between comprehension of and trust in an algorithmic system in our interactive redesign. However, our results are not conclusive yet, and suggest that the measures of comprehension, fairness, accuracy and trust are not yet exhaustive for the empirical study of algorithm awareness. Our qualitative insights provide a first indication for further measures. Our study participants, for example, were less concerned with the details of understanding an algorithmic calculation than with who or what is judging the result of the algorithm., 10 pages, 7 figures

In this position paper, I propose that the technical, designerly as well as the ethical dimension of interpretability for machine learning (ML) are irreducibly intertwined, and even commensurate. With ML-driven systems, engineers and designers wield considerable power in shaping the values of the artefacts that govern our access to the world. This statement in itself is neither radical or new, with Winner's article on the politics of technological artefacts a ubiquitous reference, and the post-phenomenological stance of mediation theory gaining ground in the ethical discussions of HCI. Additionally, design methodologies such as participatory (PD) or value-sensitive design (VSD) are well articulated and poised to enter the discourse on interpretability. As a caveat, however, I suggest that any according assessment and design attempts for ML-driven systems ought to consider two co-constitutive factors: distributed hybrid reasoning and emergent values.

Interpretation of machine learning results is a major challenge for non-technical experts, with visualization being a common approach to support this process. For instance, interpretation of clustering results is usually based on scatterplots that provide information about cluster characteristics implicitly through the relative location of objects. However, the locations and distances tend to be distorted because of artifacts stemming from dimensionality reduction. This makes interpretation of clusters difficult and may lead to distrust in the system. Most existing approaches that counter this drawback explain the distances in the scatterplot (e.g., error visualization) to foster the interpretability of implicit information. Instead, we suggest explicit visualization of the uncertainty related to the information needed for interpretation, specifically the uncertain membership of each object to its cluster. In our approach, we place objects on a grid, and add a continuous ''topography'' in the background, expressing the distribution of uncertainty over all clusters. We motivate our approach from a use case in which we visualize research projects, clustered by topics extracted from scientific abstracts. We hypothesize that uncertainty visualization can increase trust in the system, which we specify as an emergent property of interaction with an interpretable system. We present a first prototype and outline possible procedures for evaluating if and how the uncertainty visualization approach affects interpretability and trust., EuroVis Workshop on Trustworthy Visualization (TrustVis), Papers, 1, 5, Christoph Kinkeldey, Tim Korjakow, and Jesse Josua Benjamin, CCS Concepts: Human-centered computing --> Visualization design and evaluation methods; Interface design prototyping; HCI theory, concepts and models

The internal transfer of knowledge is a critical success factor for research organisations. However, little emphasis is placed on the intra-organisational transfer of knowledge due to the focus on knowledge transfer to external stakeholders. We introduce our research in progress project that aims to uncover opportunities for the intra-organisational transfer of knowledge at a research organisation. We propose a sociotechnical system that supports the management of the internal transfer of knowledge. Furthermore, we introduce the organisational role of the Knowledge Transfer Manager, which is a central part of the proposed sociotechnical system.