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Human values such as honesty, social responsibility, fairness, privacy, and the like are things considered important by individuals and society. Software systems, including mobile software applications (apps), may ignore or violate such values, leading to negative effects in various ways for individuals and society. While some works have investigated different aspects of human values in software engineering, this mixed-methods study focuses on honesty as a critical human value. In particular, we studied (i) how to detect honesty violations in mobile apps, (ii) the types of honesty violations in mobile apps, and (iii) the perspectives of app developers on these detected honesty violations. We first develop and evaluate 7 machine learning (ML) models to automatically detect violations of the value of honesty in app reviews from an end-user perspective. The most promising was a Deep Neural Network model with F1 score of 0.921. We then conducted a manual analysis of 401 reviews containing honesty violations and characterised honesty violations in mobile apps into 10 categories: unfair cancellation and refund policies; false advertisements; delusive subscriptions; cheating systems; inaccurate information; unfair fees; no service; deletion of reviews; impersonation; and fraudulent-looking apps. A developer survey and interview study with mobile developers then identified 7 key causes behind honesty violations in mobile apps and 8 strategies to avoid or fix such violations. The findings of our developer study also articulate the negative consequences that honesty violations might bring for businesses, developers, and users. Finally, the app developers’ feedback shows that our prototype ML-based models can have promising benefits in practice.

The importance of incorporating human values e g transparency privacy social recognition tradition in the Requirements Engineering RE process is well acknowledged but there is a paucity of empirical research for integrating human values in RE This shortfall becomes more pronounced when designing Artificial Intelligence AI systems due to their significant societal impact Ignoring or violating human values in AI systems can lead to user dissatisfaction negative socio economic repercussions and in some instances societal harm However there is a lack of guidance on addressing human values within the RE process for specific contexts of AI system development In this paper we explore human value requirements from the end users feedback for an AI system We conduct an empirical analysis of the Amazon Alexa app as a case study examining 1003 users reviews to identify relevant human values and assess the extent to which these values are addressed or ignored in the app We identified 34 values of the end users of Amazon Alexa Among them only one value is addressed self discipline and 23 of them are ignored freedom equality obedience in the app The feedback provided mixed experiences both addressed and ignored on the rest of the ten values Through this analysis we have tailored an approach for identifying human values from a specific type of AI system We posit that this approach has the potential for utility across different AI systems and a broad range of contexts providing guidance for developing human value requirements for values based AI systems

The elicitation of end-users& human values — such as freedom, honesty, transparency, etc — is important in the development of software systems. We carried out two preliminary Q-studies to understand (a) the general human value opinion types of eHealth applications (apps) end-users (b) the eHealth domain human value opinion types of eHealth apps end-users (c) whether there are differences between the general and eHealth domain opinion types. Our early results show three value opinion types using generic value instruments: (1) fun-loving, success-driven and independent end-user, (2) security-conscious, socially-concerned, and success-driven end-user, and (3) benevolent, success-driven, and conformist end-user. Our results also show two value opinion types using domain-specific value instruments: (1) security-conscious, reputable, and honest end-user, and (2) success-driven, reputable and pain-avoiding end-user. Given these results, consideration should be given to domain context in the design and application of values elicitation instruments.

The Coronavirus disease 2019 (COVID-19) outbreak quickly spread around the world, resulting in over 240 million infections and 4 million deaths by Oct 2021. While the virus is spreading from person to person silently, fear has also been spreading around the globe. The COVID-19 information from the Australian Government is convincing but not timely or detailed, and there is much information on social networks with both facts and rumors. As software engineers, we have spontaneously and rapidly constructed a COVID-19 information dashboard aggregating reliable information semi-automatically checked from different sources for providing one-stop information sharing site about the latest status in Australia. Inspired by the John Hopkins University COVID-19 Map, our dashboard contains the case statistics, case distribution, government policy, latest news, with interactive visualization. In this paper, we present a participant’s in-person observations in which the authors acted as founders of https://covid-19-au.com/ serving more than 830K users with 14M page views since March 2020. According to our first-hand experience, we summarize 9 lessons for developers, researchers and instructors. These lessons may inspire the development, research and teaching in software engineer aspects for coping with similar public crises in the future.

Human values such as integrity, privacy, curiosity, security, and honesty are guiding principles for what people consider important in life. Such human values may be violated by mobile software applications (apps), and the negative effects of such human value violations can be seen in various ways in society. In this work, we focus on the human value of honesty. We present a model to support the automatic identification of violations of the value of honesty from app reviews from an end-user perspective. Beyond the automatic detection of honesty violations by apps, we also aim to better understand different categories of honesty violations expressed by users in their app reviews. The result of our manual analysis of our honesty violations dataset shows that honesty violations can be characterised into ten categories: unfair cancellation and refund policies; false advertisements; delusive subscriptions; cheating systems; inaccurate information; unfair fees; no service; deletion of reviews; impersonation; and fraudulent-looking apps. Based on these results, we argue for a conscious effort in developing more honest software artefacts including mobile apps, and the promotion of honesty as a key value in software development practices. Furthermore, we discuss the role of app distribution platforms as enforcers of ethical systems supporting human values, and highlight some proposed next steps for human values in software engineering (SE) research.

Ubiquitous technologies such as mobile software applications (mobile apps) have a tremendous influence on the evolution of the social, cultural, economic, and political facets of life in society. Mobile apps fulfil many practical purposes for users including entertainment, transportation, financial management, etc. Given the ubiquity of mobile apps in the lives of individuals and the consequent effect of these technologies on society, it is essential to consider the relationship between human values and the development and deployment of mobile apps. The many negative consequences of violating human values such as privacy, fairness or social justice by technology have been documented in recent times. If we can detect these violations in a timely manner, developers can look to better address them. To understand the violation of human values in a range of common mobile apps, we analysed 22,119 app reviews from Google Play Store using natural language processing techniques. We base our values violation detection approach on a widely accepted model of human values; the Schwartz theory of basic human values. The results of our analysis show that 26.5% of the reviews contained text indicating user perceived violations of human values. We found that benevolence and self-direction were the most violated value categories, and conformity and tradition were the least violated categories. Our results also highlight the need for a proactive approach to the alignment of values amongst stakeholders and the use of app reviews as a valuable additional source for mining values requirements.

Abstract BACKGROUND Grade 2 and 3 (G2/3) gliomas are the second largest group of brain tumors in adults. Although the prognosis for G2/3 gliomas at the time of relapse mirror those of glioblastoma, there are few trials in this space. METHODS LUMOS was a national multi-center pilot study for patients with relapsed G2/3 gliomas designed to match contemporaneous tissue obtained at the time of disease progression with subsequent targeted therapies. The objective was to establish the feasibility of a precision oncology, umbrella approach to obtain and type tissue within a useful timeframe. As a key feature of LUMOS, a multidisciplinary Molecular Tumor Advisory Panel (MTAP) with subspecialty neuro-oncology expertise was formed to interpret the complex genomic information and provide a simplified recommendation to the treating physician. RESULTS Ten patients (median age 42: range 32-62; four G2 astrocytoma, one G3 astrocytoma, three G2 oligoendroglioma, one G3 oligodendroglioma, one mixed tumor) were enrolled in the study. Eight patients had biopsies within 6 months of study entry whilst two underwent a biopsy during the study. All patients had potentially targetable alterations (10 IDH, 3 FGFR, 2 PIK3K, CCND3, NRAS, CDK4, PRPRZ1-MET fusion and MET amplification). Matched therapies were delivered for two patients via compassionate access outside the study. The median turnaround time (TAT) of MTAP reports was 6.2 weeks (range 4.2-9.7 weeks) but 4.6 weeks when lag time for shipping was removed. CONCLUSION LUMOS confirmed that this design was feasible with good turnaround times. The MTAP facilitated education and support for treating physicians. Thes findings support moving to a larger study using contemporaneous and

This chapter explores co-production with community groups of innovative digital technologies designed to address challenging social issues. It presents lessons learned from the Catalyst project (http://www.catalystproject.org.uk), which carried out 13 such co-production projects over a three year period developing digital solutions in areas as diverse as homelessness, anxiety management, behaviour change, and renewable energy. The approach taken was to form meaningful partnerships of multidisciplinary academics and external partners from community groups. The chapter offers guidelines for how to make such partnerships effective based on the Catalyst experience. These guidelines cover a range of different areas: working in the community, research innovation, working across disciplines, and practicalities. They are illustrated, where appropriate, by reference to a range of research partnerships set up as part of the Catalyst project.

This chapter explores co-production with community groups of innovative digital technologies designed to address challenging social issues. It presents lessons learned from the Catalyst project (http://www.catalystproject.org.uk), which carried out 13 such co-production projects over a three year period developing digital solutions in areas as diverse as homelessness, anxiety management, behaviour change, and renewable energy. The approach taken was to form meaningful partnerships of multidisciplinary academics and external partners from community groups. The chapter offers guidelines for how to make such partnerships effective based on the Catalyst experience. These guidelines cover a range of different areas: working in the community, research innovation, working across disciplines, and practicalities. They are illustrated, where appropriate, by reference to a range of research partnerships set up as part of the Catalyst project.

Studies of visit-to-visit offce blood pressure (BP) variability (OBPV) as a predictor of cardiovascular events and death in high-risk patients treated to lower BP targets are lacking. We conducted a post hoc analysis of SPRINT (Systolic Blood Pressure Intervention Trial), a well-characterized cohort of participants randomized to intensive (<120 mmHg) or standard (<140 mmHg) systolic BP targets. We defned OBPV as the coeffcient of variation of the systolic BP using measurements taken during the 3-,6-, 9-, and 12-month study visits. In our cohort of 7879 participants, older age, female sex, black race, current smoking, chronic kidney disease, and coronary disease were independent determinants of higher OBPV. Use of thiazide-type diuretics or dihydropyridine calcium channel blockers was associated with lower OBPV whereas angiotensin-converting enzyme inhibitors or angiotensin receptor blocker use was associated with higher OBPV. There was no difference in OBPV in participants randomized to standard or intensive treatment groups. We found that OBPV had no signifcant associations with the composite end point of fatal and nonfatal cardiovascular events (n=324 primary end points; adjusted hazard ratio, 1.20; 95% confdence interval, 0.85-1.69, highest versus lowest quintile) nor with heart failure or stroke. The highest quintile of OBPV (versus lowest) was associated with all-cause mortality (adjusted hazard ratio, 1.92; confdence interval, 1.22-3.03) although the association of OBPV overall with all-cause mortality was marginal (P=0.07). Our results suggest that clinicians should continue to focus on offce BP control rather than on OBPV unless defnitive benefts of reducing OBPV are shown in prospective trials.