The EmpathiCH Workshop aims to blend a diverse set of expertise to expand upon the nascent field of Empathy-Centric Design. Building on the discussions in previous editions of the workshop, this iteration invites contributions which scrutinize the use of empathy as a design principle in digital interfaces. We encourage inquiry in a number of research dimensions: examining the multifaceted nature of empathy; establishing both the requirements and shortcomings of empathy in design research; discussing key post-human stakeholders in digital interfaces (social groups, causes, digital avatars, artificial agents etc.); and expanding the scope of empathy research beyond preliminary perspective-taking. The workshop, structured as a combination of author panels, expert discussion, and interactive activities, provides the ideal venue to foster a critical discussion on the nature of the suitability of empathy in digital design, especially in the rapidly approaching context of its role in post-humanist HCI.@en

Hate speech moderation remains a challenging task for social media platforms. Human-AI collaborative systems offer the potential to combine the strengths of humans' reliability and the scalability of machine learning to tackle this issue effectively. While methods for task handover in human-AI collaboration exist that consider the costs of incorrect predictions, insufficient attention has been paid to accurately estimating these costs. In this work, we propose a value-sensitive rejection mechanism that automatically rejects machine decisions for human moderation based on users' value perceptions regarding machine decisions. We conduct a crowdsourced survey study with 160 participants to evaluate their perception of correct and incorrect machine decisions in the domain of hate speech detection, as well as occurrences where the system rejects making a prediction. Here, we introduce Magnitude Estimation, an unbounded scale, as the preferred method for measuring user (dis)agreement with machine decisions. Our results show that Magnitude Estimation can provide a reliable measurement of participants' perception of machine decisions. By integrating user-perceived value into human-AI collaboration, we further show that it can guide us in 1) determining when to accept or reject machine decisions to obtain the optimal total value a model can deliver and 2) selecting better classification models as compared to the more widely used target of model accuracy.

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