Camera glasses create fundamental privacy tensions between wearers seeking recording functionality and bystanders concerned about unauthorized surveillance. We present a systematic multi-stakeholder evaluation of privacy mechanisms through surveys (N=525) and paired interviews (N=20) in China. Study 1 quantifies expectation-willingness gaps: bystanders consistently demand stronger information transparency and protective measures than wearers will provide, with disparities intensifying in sensitive contexts where 65–90% of bystanders would take defensive action. Study 2 evaluates twelve privacy-enhancing technologies, revealing four fundamental trade-offs that undermine current approaches: visibility versus disruption, empowerment versus burden, protection versus agency, and accountability versus exposure. These gaps reflect structural incompatibilities rather than inadequate goodwill, with context emerging as the primary determinant of privacy acceptability. We propose context-adaptive pathways that dynamically adjust protection strategies: minimal-friction visibility in public spaces, structured negotiation in semi-public environments, and automatic protection in sensitive contexts. Our findings contribute a diagnostic framework for evaluating privacy mechanisms and implications for context-aware design in ubiquitous sensing.

Private conversations in social Virtual Reality (VR) environments lack the nuanced cues of physical interactions, potentially diminishing the sense of privacy and social presence. This paper introduces Whisper, a novel multisensory interaction technique designed to enhance private conversations for social VR applications. We first conducted a formative study (N=20) to understand private conversation demands, limitations of existing methods, and user expectations in social VR. Informed by these insights, Whisper incorporates visual (avatar proximity, gestures and illumination), auditory (voice conversation), and tactile (simulated airflow) elements to simulate the act of whispering, providing users with an intuitive and immersive method of private communication. The technique also features a contextual record to maintain conversation continuity. We evaluated Whisper through a comparative user study (N=24) in party and classroom scenarios. Results demonstrate that Whisper significantly outperforms existing methods in sense of privacy, mode distinguishability, intimacy, perceptual realism, and social presence.

Context: Machine learning software can generate models that inappropriately discriminate against specific protected social groups (e.g., groups based on gender, ethnicity, etc.). Motivated by those results, software engineering researchers have proposed many methods for mitigating those discriminatory effects. While those methods are effective in mitigating bias, few of them can provide explanations on what is the root cause of bias.Objective: We aim to better detect and mitigate algorithmic discrimination in machine learning software problems.Method: Here we propose${{\sf FairMask}}$, amodel-basedextrapolation method that is capable of both mitigating bias and explaining the cause. In our${{\sf FairMask}}$approach, protected attributes are represented by models learned from the other independent variables (and these models offer extrapolations over the space between existing examples). We then use the extrapolation models to relabel protected attributes later seen in testing data or deployment time. Our approach aims to offset the biased predictions of the classification model by rebalancing the distribution of protected attributes.Results: The experiments of this paper show that, without compromising (original) model performance,${{\sf FairMask}}$can achieve significantly better group and individual fairness (as measured in different metrics) than benchmark methods. Moreover, compared to another instance-based rebalancing method, our model-based approach shows faster runtime and thus better scalability.Conclusion: Algorithmic decision bias can be removed via extrapolation that corrects the misleading latent correlation between the protected attributes and other non-protected ones. As evidence for this, our proposed${{\sf FairMask}}$is not only performance-wise better (measured by fairness and performance metrics) than two state-of-the-art fairness algorithms.Reproduction Package: In order to better support open science, all scripts and data used in this study are available online athttps://github.com/anonymous12138/biasmitigation.

Software comes in releases. An implausible change to software is something that has never been changed in prior releases. When planning how to reduce defects, it is better to use plausible changes, i.e., changes with some precedence in the prior releases. To demonstrate these points, this paper compares several defect reduction planning tools. LIME is a local sensitivity analysis tool that can report the fewest changes needed to alter the classification of some code module (e.g., from “defective” to “non-defective”). TimeLIME is a new tool, introduced in this paper, that improves LIME by restricting its plans to just those attributes which change the most within a project. In this study, we compared the performance of LIME and TimeLIME and several other defect reduction planning algorithms. The generated plans were assessed via (a) the similarity scores between the proposed code changes and the real code changes made by developers; and (b) the improvement scores seen within projects that followed the plans. For nine project trails, we found that TimeLIME outperformed all other algorithms (in 8 out of 9 trials). Hence, we strongly recommend using past releases as a source of knowledge for computing fixes for new releases (using TimeLIME). Apart from these specific results, the other lesson from this paper is that our community might be more careful about using off-the-shelf AI tools, without first applying SE knowledge (e.g., that past releases are a good source of knowledge for planning defect reductions). As shown here, once that SE knowledge is applied, this can result in dramatically better reasoning.

We report here the following example of reuse. The original paper is a prior work about features in product lines by Berger et al. The paper "Dimensions of software configuration: on the configuration context in modern software development" by Siegmund et al. reused definitions and theories about configuration features in the original paper.

We report here the following example of reuse. LIME is a local instance-based explanation generation framework that was originally proposed by Ribeiro et al. in their paper "'Why Should I Trust You?': Explaining the Predictions of Any Classifier". The framework was reused by Peng et al. in their paper "Defect Reduction Planning (using TimeLIME)". The paper used the original implementation of LIME as one of the core components in the proposed framework.

Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice) having huge social impact. But sometimes the behavior of this software is biased and it shows discrimination based on some sensitive attributes such as sex, race etc. Prior works concentrated on finding and mitigating bias in ML models. A recent trend is using instance-based model-agnostic explanation methods such as LIME[36] to find out bias in the model prediction. Our work concentrates on finding shortcomings of current bias measures and explanation methods. We show how our proposed method based on K nearest neighbors can overcome those shortcomings and find the underlying bias of black box models. Our results are more trustworthy and helpful for the practitioners. Finally, We describe our future framework combining explanation and planning to build fair software.