Marc Cheong’s research while affiliated with University of Melbourne and other places

Purpose The problem of misinformation is one that has been well-explored in the literature. While researchers often study tertiary student behaviors, they do not distinguish between student groups – such as those who have lived in a country their whole lives versus those who moved to the country. Further, literature tends to focus broadly on misinformation, and malinformation is an understudied area. The study aims to address these gaps. Design/methodology/approach Data was gathered using a survey instrument deployed as part of a larger study. Students were presented with two posts: one containing malinformation and one containing misinformation. They were asked how they would evaluate the posts. There were 193 respondents. Responses were analyzed using general inductive analysis. In completing the analysis, we differentiated between migrant and sedentary student groups. Findings Findings indicate that there are qualitative differences in how different groups evaluate suspect information and qualitative differences in how students approach misinformation and malinformation. Students are more accepting of malinformation than misinformation. Migrant students are less prone to making trust/distrust decisions and instead more prone to ambivalence. They are also more likely to seek out additional information in the face of misinformation compared to sedentary counterparts. Originality/value Findings enhance our understanding of differences in migrant and sedentary students’ experiences with suspect information and provide insights on malinformation experiences which is an underexplored area of research.

With the acceleration of Large Language Model (LLM) use by the public, there is an urgent need to make sure the downstream effects have beneficial impacts on humans and society. Therefore, there is an increasing push for ethical evaluation of LLMs to look for potential bias, toxic behaviour, and misinformation. Thus, crowdsourcing has become a popular practice, such as OpenAI’s 2023 Evals initiative. Firstly, by reviewing the literature from software development and ethics, we wish to highlight several cautions on applying the crowdsourcing model to LLMs: including participant self-selection and non-representativeness; the diffusion of responsibility effect including ethics washing and burden shifting; and requisition of ‘incentives’ vis-a-vis issues faced by gig workers. Using the Evals GitHub repository as a case study, we study the effectiveness of an expert-driven, voluntary, crowdsourced scheme on GitHub to address socioethical issues in LLMs. This is achieved by evaluating the statistics of crowdsourced contributions on ethical and bias considerations, which pales in comparison to other technical contributions. This commentary hopes to highlight the issues of ethics, equity, and justice in LLM crowdsourcing, drawing upon interdisciplinary literature, and presents open considerations on how we can improve the state of play.

Papua New Guinea (PNG) is an emerging tech society with an opportunity to overcome geographic and social boundaries, in order to engage with the global market. However, the current tech landscape, dominated by Big Tech in Silicon Valley and other multinational companies in the Global North, tends to overlook the requirements of emerging economies such as PNG. This is becoming more obvious as issues such as algorithmic bias (in tech product deployments) and the digital divide (as in the case of non-affordable commercial software) are affecting PNG users. The Open Source Software (OSS) movement, based on extant research, is seen as a way to level the playing field in the digitalization and adoption of Information and Communications Technologies (ICTs) in PNG. This perspectives paper documents the outcome of the second International Workshop on BRIdging the Divides with Globally Engineered Software} (BRIDGES2023) in the hopes of proposing ideas for future research into ICT education, uplifting software engineering (SE) capability, and OSS adoption in promoting a more equitable digital future for PNG.

Today, organizations are increasingly relying on automated hiring. The mechanization of the hiring process is assumed to render it more neutral, but a growing literature shows algorithmic decisions are as likely to be biased (Dickson, 2018). In this study, we test two types of biases: (1) gender bias; and (2) parenting bias, (i.e., whether mothers and fathers with an extended gap to care for children are penalized vis‐à‐vis those with uninterrupted employment net of equivalent high‐impact qualifications). We apply a classic counterfactual study sending gender and parenthood manipulated CVs to 211 job advertisements across three occupations (men‐dominated, women‐dominated, and gender‐balanced, to mitigate confounding variables associated with gender composition) in the United States and measure penalty‐premium bias in response rates. Our results identify semi‐automated hiring bias against parents who took leave to care for children relative to those with uninterrupted employment. Importantly, we find fathers who have an extended parental leave were the most severely penalized, followed by mothers with an extended parental leave and women and men without parental leave respectively. Ultimately, we identify gender and parenting bias in algorithmic and human hiring decisions.

Large Language Models (LLMs) have taken the world by storm, demonstrating their ability not only to automate tedious tasks, but also to show some degree of proficiency in completing software engineering tasks. A key concern with LLMs is their "black-box" nature, which obscures their internal workings and could lead to societal biases in their outputs. In the software engineering context, in this early results paper, we empirically explore how well LLMs can automate recruitment tasks for a geographically diverse software team. We use OpenAI's ChatGPT to conduct an initial set of experiments using GitHub User Profiles from four regions to recruit a six-person software development team, analyzing a total of 3,657 profiles over a five-year period (2019-2023). Results indicate that ChatGPT shows preference for some regions over others, even when swapping the location strings of two profiles (counterfactuals). Furthermore, ChatGPT was more likely to assign certain developer roles to users from a specific country, revealing an implicit bias. Overall, this study reveals insights into the inner workings of LLMs and has implications for mitigating such societal biases in these models.

Background: The development of AI-enabled software heavily depends on AI model documentation, such as model cards, due to different domain expertise between software engineers and model developers. From an ethical standpoint, AI model documentation conveys critical information on ethical considerations along with mitigation strategies for downstream developers to ensure the delivery of ethically compliant software. However, knowledge on such documentation practice remains scarce. Aims: The objective of our study is to investigate how developers document ethical aspects of open source AI models in practice, aiming at providing recommendations for future documentation endeavours. Method: We selected three sources of documentation on GitHub and Hugging Face, and developed a keyword set to identify ethics-related documents systematically. After filtering an initial set of 2,347 documents, we identified 265 relevant ones and performed thematic analysis to derive the themes of ethical considerations. Results: Six themes emerge, with the three largest ones being model behavioural risks, model use cases, and model risk mitigation. Conclusions: Our findings reveal that open source AI model documentation focuses on articulating ethical problem statements and use case restrictions. We further provide suggestions to various stakeholders for improving documentation practice regarding ethical considerations.