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1. Developer Reputation Estimator (DRE)

   https://doi.org/10.1109/ASE.2019.00107  

   Amreen, Sadika; Karnauch, andrey; Mockus, Audris (November 2019, ASE '19: Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering)

   Evidence shows that developer reputation is extremely important when accepting pull requests or resolving reported issues. It is particularly salient in Free/Libre Open Source Software since the developers are distributed around the world, do not work for the same organization and, in most cases, never meet face to face. The existing solutions to expose developer reputation tend to be forge specific (GitHub), focus on activity instead of impact, do not leverage social or technical networks, and do not correct often misspelled developer identities. We aim to remedy this by amalgamating data from all public Git repositories, measuring the impact of developer work, expose developer's collaborators, and correct notoriously problematic developer identity data. We leverage World of Code (WoC), a collection of an almost complete (and continuously updated) set of Git repositories by first allowing developers to select which of the 34 million(M) Git commit author IDs belong to them and then generating their profiles by treating the selected collection of IDs as that single developer. As a side-effect, these selections serve as a training set for a supervised learning algorithm that merges multiple identity strings belonging to a single individual. As we evaluate the tool and the proposed impact measure, we expect to build on these findings to develop reputation badges that could be associated with pull requests and commits so developers could easier trust and prioritize them. 

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2. QnQ: Quality and Quantity Based Unified Approach for Secure and Trustworthy Mobile Crowdsensing

   Bhattacharjee, Shameek; Ghosh, Nirnay; Shah, Vijay K.; Das, Sajal K. (January 2020, IEEE transactions on mobile computing)

   A major challenge in mobile crowdsensing applications is the generation of false (or spam) contributions resulting from selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue owing to undue incentivization, and also affect the operational reliability of the applications. To counter these problems, we propose an event-trust and user-reputation model, called QnQ, to segregate different user classes such as honest, selfish, or malicious. The resultant user reputation scores, are based on both `quality' (accuracy of contribution) and `quantity' (degree of participation) of their contributions. Specifically, QnQ exploits a rating feedback mechanism for evaluating an event-specific expected truthfulness, which is then transformed into a robust quality of information (QoI) metric to weaken various effects of selfish and malicious user behaviors. Eventually, the QoIs of various events in which a user has participated are aggregated to compute his reputation score, which in turn is used to judiciously disburse user incentives with a goal to reduce the incentive losses of the CS application provider. Subsequently, inspired by cumulative prospect theory (CPT), we propose a risk tolerance and reputation aware trustworthy decision making scheme to determine whether an event should be published or not, thus improving the operational reliability of the application. To evaluate QnQ experimentally, we consider a vehicular crowdsensing application as a proof-of-concept. We compare QoI performance achieved by our model with Jøsang's belief model, reputation scoring with Dempster-Shafer based reputation model, and operational (decision) accuracy with expected utility theory. Experimental results demonstrate that QnQ is able to better capture subtle differences in user behaviors based on both quality and quantity, reduces incentive losses, and significantly improves operational accuracy in presence of rogue contributions 

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3. Reputation Building under Observational Learning

   https://doi.org/10.1093/restud/rdac052  

   Pei, Harry (July 2022, The Review of Economic Studies)

   Abstract A patient seller interacts with a sequence of myopic consumers. Each period, the seller chooses the quality of his product, and a consumer decides whether to trust the seller after she observes the seller’s actions in the last $$K$$ periods (limited memory) and at least one previous consumer’s action (observational learning). However, the consumer cannot observe the seller’s action in the current period. With positive probability, the seller is a commitment type who plays his Stackelberg action in every period. I show that under limited memory and observational learning, consumers are concerned that the seller will not play his Stackelberg action when he has a positive reputation and will play his Stackelberg action after he has lost his reputation. Such a concern leads to equilibria where the seller receives a low payoff from building a reputation. I also show that my reputation failure result hinges on consumers’ observational learning. 

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4. Vistrust: a Multidimensional Framework and Empirical Study of Trust in Data Visualizations

   https://doi.org/10.1109/TVCG.2023.3326579  

   Elhamdadi, Hamza; Stefkovics, Adam; Beyer, Johanna; Moerth, Eric; Pfister, Hanspeter; Bearfield, Cindy Xiong; Nobre, Carolina (January 2024, IEEE Transactions on Visualization and Computer Graphics)

   Trust is an essential aspect of data visualization, as it plays a crucial role in the interpretation and decision-making processes of users. While research in social sciences outlines the multi-dimensional factors that can play a role in trust formation, most data visualization trust researchers employ a single-item scale to measure trust. We address this gap by proposing a comprehensive, multidimensional conceptualization and operationalization of trust in visualization. We do this by applying general theories of trust from social sciences, as well as synthesizing and extending earlier work and factors identified by studies in the visualization field. We apply a two-dimensional approach to trust in visualization, to distinguish between cognitive and affective elements, as well as between visualization and data-specific trust antecedents. We use our framework to design and run a large crowd-sourced study to quantify the role of visual complexity in establishing trust in science visualizations. Our study provides empirical evidence for several aspects of our proposed theoretical framework, most notably the impact of cognition, affective responses, and individual differences when establishing trust in visualizations. 

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5. Powering for Privacy: Improving User Trust in Smart Speaker Microphones with Intentional Powering and Perceptible Assurance

   Youngwook Do and Nivedita Arora, Georgia Institute; Ali Mirzazadeh and Injoo Moon, Georgia Institute; Eryue Xu, Georgia Institute; Zhihan Zhang, Georgia Institute; Gregory D. Abowd, Georgia Institute; Sauvik Das, Georgia Institute (August 2023, Proceedings of the 32nd USENIX Security Symposium)

   Smart speakers come with always-on microphones to facilitate voice-based interaction. To address user privacy concerns, existing devices come with a number of privacy features: e.g., mute buttons and local trigger-word detection modules. But it is difficult for users to trust that these manufacturer-provided privacy features actually work given that there is a misalignment of incentives: Google, Meta, and Amazon benefit from collecting personal data and users know it. What’s needed is perceptible assurance — privacy features that users can, through physical perception, verify actually work. To that end, we introduce, implement, and evaluate the idea of “intentionally-powered” microphones to provide users with perceptible assurance of privacy with smart speakers. We employed an iterative-design process to develop Candid Mic, a battery-free, wireless microphone that can only be powered by harvesting energy from intentional user interactions. Moreover, users can visually inspect the (dis)connection between the energy harvesting module and the microphone. Through a within-subjects experiment, we found that Candid Mic provides users with perceptible assurance about whether the microphone is capturing audio or not, and improves user trust in using smart speakers relative to mute button interfaces. 

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