Risk reporting is essential for documenting AI models, yet only 14% of model cards mention risks, out of which 96% copying content from a small set of cards, leading to a lack of actionable insights. Existing proposals for improving model cards do not resolve these issues. To address this, we introduce RiskRAG, a Retrieval Augmented Generation based risk reporting solution guided by five design requirements we identified from literature, and co-design with 16 developers: identifying diverse model-specific risks, clearly presenting and prioritizing them, contextualizing for real-world uses, and offering actionable mitigation strategies. Drawing from 450K model cards and 600 real-world incidents, RiskRAG pre-populates contextualized risk reports. A preliminary study with 50 developers showed that they preferred RiskRAG over standard model cards, as it better met all the design requirements. A final study with 38 developers, 40 designers, and 37 media professionals showed that RiskRAG improved their way of selecting the AI model for a specific application, encouraging a more careful and deliberative decision-making. The RiskRAG project page is accessible at: https://social-dynamics.net/ai-risks/card.

Rising unemployment has led to many discouraged job seekers. While the impact of job seekers’ motivation on interview performance is acknowledged in previous research, little attention has been given to understanding the effect of training on interview motivation and performance. We present InterviewApp, an online interview training tool aiming to support interview motivation through autonomy, relatedness and competence needs derived from Self-Determination Theory and, in turn, performance. Through a four-month study (N=135), we assess its effectiveness in supporting job seekers’ interview motivation and performance. Our results demonstrate the role of autonomy in mediating the effect of training on performance. We found that the intervention significantly affected the job seekers’ perceived autonomy. Furthermore, engagement with the recording and feedback features of the tool positively impacted performance. Overall, job seekers found InterviewApp helpful for online interview training and valued the provided expert feedback. These findings have implications for the design of online interview training tools and for behaviour change interventions to support employment.

In recent years, various initiatives from within and outside the HCI field have encouraged researchers to improve research ethics, openness, and transparency in their empirical research. We quantify how the CHI literature might have changed in these three aspects by analyzing samples of 118 CHI 2017 and 127 CHI 2022 papers—randomly drawn and stratified across conference sessions. We operationalized research ethics, openness, and transparency into 45 criteria and manually annotated the sampled papers. The results show that the CHI 2022 sample was better in 18 criteria, but in the rest of the criteria, it has no improvement. The most noticeable improvements were related to research transparency (10 out of 17 criteria). We also explored the possibility of assisting the verification process by developing a proof-of-concept screening system. We tested this tool with eight criteria. Six of them achieved high accuracy and F1 score. We discuss the implications for future research practices and education.

Effective communication is an important social skill that facilitates us to interpret and connect with people around us and is of utmost importance in employment based interviews. This paper presents a methodical study and automatic measurement of communication skill of candidates in different modes of behavioural interviews. It demonstrates a comparative analysis of non-conventional methods of employment interviews namely 1) Interface-based asynchronous video interviews and 2) Written interviews (including a short essay). In order to achieve this, we have collected a dataset of 100 structured interviews from participants. These interviews are evaluated independently by two human expert annotators on rubrics specific to each of the settings. We, then propose a predictive model using automatically extracted multimodal features like audio, visual and lexical, applying classical machine learning algorithms. Our best model performs with an accuracy of 75% for a binary classification task in all the three contexts. We also study the differences between the expert perception and the automatic prediction across the settings.

Communication skill is an important social variable in em- ployment interviews. As recent trends point to, increasingly asynchronous or interface-based video interviews are becom- ing popular. Also getting increasing interest is automatic hiring analysis, of which automatic communication skill pre- diction is one such task. In this context, a research gap that exists and which our paper addresses is â€oeAre there any differences in perception of communication skill and the accuracy of automatic prediction of say classes of communicators (e.g. those below average) when we compare interface-based and face-to-face interviewsâ€❝. To this end, we have collected a set of 106 interview videos from graduate students in both the settings i.e., interface-based and face-to-face. We observe that perception of behavior of participants in interface-based (when no person is involved) vs. face-to-face (when inter- viewer is involved) according to the external naive observers is slightly different. In this paper, we present an automatic system to predict the communication skill of a person in interface-based and face-to-face interviews by automatically extracting several low level features based on audio, visual and lexical behavior of the participants and using Machine Learning algorithms like Linear Regression, Support Vec- tor Machine (SVM) and Logistic Regression. We also make an extensive study of the verbal behavior of the participant when the spoken response is obtained from manual tran- scriptions and Automatic Speech Recognition (ASR) tool. Our best automatic prediction results achieve an accuracy of 80% in interface-based and 83% in face-to-face setting.