One and the Same: Ethical Attribution and Distributed Reasoning in ML-driven Systems

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

Examining the Impact of Algorithm Awareness on Wikidata's Recommender System Recoin

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.Comment: 10 pages, 7 figure

QuintEssence: A Probe Study to Explore the Power of Smell on Emotions, Memories, and Body Image in Daily Life

Previous research has shown the influence of smell on emotions, memories, and body image. However, most of this work has taken place in laboratory settings and little is known about the influence of smell in real-world environments. In this paper, we present novel insights gained from a field study investigating the emotional effect of smell on memories and body image. Taking inspiration from the cultural design probes approach, we designed QuintEssence, a probe package that includes three scents and materials to complete three tasks over a period of four weeks. Here, we describe the design of QuintEssence and the main findings based on the outcomes of the three tasks and a final individual interview. The findings show similar results between participants based on the scent. For example, with cinnamon, participants experienced feelings of warmth, coziness, happiness, and relaxation; they recalled blurred memories of past moments about themselves and reported a general feeling of being calm and peaceful towards their bodies. Our findings open up new design spaces for multisensory experiences and inspire future qualitative explorations beyond laboratory boundaries

PreCall: A Visual Interface for Threshold Optimization in ML Model Selection

Machine learning systems are ubiquitous in various kinds of digital applications and have a huge impact on our everyday life. But a lack of explainability and interpretability of such systems hinders meaningful participation by people, especially by those without a technical background. Interactive visual interfaces (e.g., providing means for manipulating parameters in the user interface) can help tackle this challenge. In this paper we present PreCall, an interactive visual interface for ORES, a machine learning-based web service for Wikimedia projects such as Wikipedia. While ORES can be used for a number of settings, it can be challenging to translate requirements from the application domain into formal parameter sets needed to configure the ORES models. Assisting Wikipedia editors in finding damaging edits, for example, can be realized at various stages of automatization, which might impact the precision of the applied model. Our prototype PreCall attempts to close this translation gap by interactively visualizing the relationship between major model metrics (recall, precision, false positive rate) and a parameter (the threshold between valuable and damaging edits). Furthermore, PreCall visualizes the probable results for the current model configuration to improve the human's understanding of the relationship between metrics and outcome when using ORES. We describe PreCall's components and present a use case that highlights the benefits of our approach. Finally, we pose further research questions we would like to discuss during the workshop.Comment: HCML Perspectives Workshop at CHI 2019, May 04, 2019, Glasgo

Ways of seeing design research: A polyphonic speculation

We present six speculative designs that all explore the challenge of representing the broad corpus of Design Research in the form of an interactive data repository. We describe the development of the ideas, identify common themes, and highlight two related challenges: (i) The challenge of reflecting the diversity of Design Research in a repository; (ii) The challenge of capturing context(s) during the Design Research process. We argue that these challenges constitute a ‘causality dilemma’ that is inhibiting the Design Research movement. We offer insights into potential responses to the dilemma, signpost opportunities for future work and reflect on the value of ‘polyphonic speculation’ – dialogue between design researchers speculating through design on a common topic – as a design tool for probing complex challenges

Machine Horizons: Post-Phenomenological AI Studies

In this dissertation, Jesse Josua Benjamin combines philosophical analyses, technical readings and design research to propose post-phenomenological AI studies as a program for investigating how contemporary artificial intelligence (AI) technologies shape the relations between human beings and their worlds. Alongside a number of conceptual innovations refined through historical and contemporary case studies as well as philosophy-in-practice, the titular machine horizons are the key objective for this programmatic proposal: establishing a relation between the appearance of technologies and their structural make-up, and thereby unfolding the synching of human experience to the interplay of data and models of AI technologies

Three Post-Phenomenological Design Projects on and with ML Technologies as Philosophy-in-Practice

In my doctoral research, I am concerned with making machine learning (ML) technologies accessible as a design material using post-phenomenological investigations. I use the latter framework because, on the one hand, it has become particularly widespread in HCI design research given its focus on technological mediation, or the shaping of subjectivity and objectivity, and according perceptions and actions in the world via technology. However, and on the other hand, the relationship between both approaches is reciprocal in my work. While post-phenomenology can inform design research, the former also requires assistance from the latter due to its own conceptual shortcomings regarding ML technologies. In this contribution, I focus on my work fusing post-phenomenology and design research as philosophy-in-practice to explicate conceptual vocabularies and provocative shorthands for approaching ML technologies as a design material