publications | marc cheong

🕰 For historical publications (before ca. 2017), please refer to the archive here, as a virtual handout (presented at ASNAC 2016).

🆕 A snapshot of more recent publications are here.

2024

Ethical considerations toward protestware

Marc Cheong, Raula Gaikovina Kula, and Christoph Treude

2024

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This article looks into possible scenarios where developers might consider turning their free and open source software into protestware. Using different frameworks commonly used in artificial intelligence (AI) ethics, we extend the applications of AI ethics to the study of protestware.
Rising caesarean section rates and factors affecting women’s decision-making about mode of birth in Indonesia: a longitudinal qualitative study

Rana Islamiah Zahroh, Alya Hazfiarini, Moya A D Martiningtyas, Fitriana Murriya Ekawati, Ova Emilia, Marc Cheong, Ana Pilar Betran, Caroline S E Homer, and Meghan A Bohren

2024

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IntroductionCaesarean section (CS) rates in Indonesia are increasing rapidly. Understanding women’s preferences about mode of birth is important to help contextualise these rising rates and can help develop interventions to optimise CS. This study aimed to explore Indonesian women’s preferences and decision-making about mode of birth, and how their preferences may change throughout pregnancy and birth.MethodsWe conducted a longitudinal qualitative study using in-depth interviews with 28 women accessing private and public health facilities in Jakarta, the region with the highest CS rates. Interviews were conducted two times: during the woman’s third trimester of pregnancy and in the postpartum period, between October 2022 and March 2023. We used a reflexive thematic approach for analysis.ResultsWe generated three themes: (1) preferences about the mode of birth, (2) decision-making about the mode of birth and (3) regrets about the actual mode of birth. Most women preferred vaginal birth. However, they were influenced by advertisements promoting enhanced recovery after CS (ERACS) as an ‘advanced technique’ of CS, promising a comfortable, painless and faster recovery birth. This messaging influenced women to perceive CS as equivalent or even superior to vaginal birth. Where women’s preferences for mode of birth shifted around the time of birth, this was primarily due to the obstetricians’ discretion. Women felt they did not receive adequate information from obstetricians on the benefits and risks of CS and vaginal birth and felt disappointed when their actual mode of birth was not aligned with their preferences.ConclusionOur study shows that despite rising CS rates, Indonesian women prefer vaginal birth. This highlights the need for better communication strategies and evidence-based information from healthcare providers. Given the rising popularity of ERACS, more work is urgently needed to standardise and regulate its use.
Documenting ethical considerations in open source AI models

Haoyu Gao, Mansooreh Zahedi, Christoph Treude, Sarita Rosenstock, and Marc Cheong

2024

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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.
Now you see me, now you don’t: an exploration of religious exnomination in DALL-E

Mark Alfano, Ehsan Abedin, Ritsaart Reimann, Marinus Ferreira, and Marc Cheong

2024

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Artificial intelligence (AI) systems are increasingly being used not only to classify and analyze but also to generate images and text. As recent work on the content produced by text and image Generative AIs has shown (e.g., Cheong et al., 2024, Acerbi & Stubbersfield, 2023), there is a risk that harms of representation and bias, already documented in prior AI and natural language processing (NLP) algorithms may also be present in generative models. These harms relate to protected categories such as gender, race, age, and religion. There are several kinds of harms of representation to consider in this context, including stereotyping, lack of recognition, denigration, under-representation, and many others (Crawford in Soundings 41:45–55, 2009; in: Barocas et al., SIGCIS Conference, 2017). Whereas the bulk of researchers’ attention thus far has been given to stereotyping and denigration, in this study we examine ‘exnomination’, as conceived by Roland Barthes (1972), of religious groups. Our case study is DALL-E, a tool that generates images from natural language prompts. Using DALL-E mini, we generate images from generic prompts such as “religious person.” We then examine whether the generated images are recognizably members of a nominated group. Thus, we assess whether the generated images normalize some religions while neglecting others. We hypothesize that Christianity will be recognizably represented more frequently than other religious groups. Our results partially support this hypothesis but introduce further complexities, which we then explore.
Moral universals: A machine-reading analysis of 256 societies

Mark Alfano, Marc Cheong, and Oliver Scott Curry

2024

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What is the cross-cultural prevalence of the seven moral values posited by the theory of "morality-as-cooperation"? Previous research, using laborious hand-coding of ethnographic accounts of ethics from 60 societies, found examples of most of the seven morals in most societies, and observed these morals with equal frequency across cultural regions. Here we replicate and extend this analysis by developing a new Morality-as-Cooperation Dictionary (MAC-D) and using Linguistic Inquiry and Word Count (LIWC) to machine-code ethnographic accounts of morality from an additional 196 societies (the entire Human Relations Area Files, or HRAF, corpus). Again, we find evidence of most of the seven morals in most societies, across all cultural regions. The new method allows us to detect minor variations in morals across region and subsistence strategy. And we successfully validate the new machine-coding against the previous hand-coding. In light of these findings, MAC-D emerges as a theoretically-motivated, comprehensive, and validated tool for machine-reading moral corpora. We conclude by discussing the limitations of the current study, as well as prospects for future research.
Investigating gender and racial biases in DALL-E Mini Images

Marc Cheong, Ehsan Abedin, Marinus Ferreira, Ritsaart Willem Reimann, Shalom Chalson, Pamela Robinson, Joanne Byrne, Leah Ruppanner, Mark Alfano, and Colin Klein

2024

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Generative artificial intelligence systems based on transformers, including both text generators such as GPT-4 and image generators such as DALL-E 3, have recently entered the popular consciousness. These tools, while impressive, are liable to reproduce, exacerbate, and reinforce extant human social biases, such as gender and racial biases. In this article, we systematically review the extent to which DALL-E Mini suffers from this problem. In line with the Model Card published alongside DALL-E Mini by its creators, we find that the images it produces tend to represent dozens of different occupations as populated either solely by men (e.g., pilot, builder, plumber) or solely by women (e.g., hairdresser, receptionist, dietitian). In addition, the images DALL-E Mini produces tend to represent most occupations as populated primarily or solely by White people (e.g., farmer, painter, prison officer, software engineer) and very few by non-White people (e.g., pastor, rapper). These findings suggest that exciting new AI technologies should be critically scrutinized and perhaps regulated before they are unleashed on society.

2023

Decentralised social media: Scoping review and future research directions

Julian Marx, and Marc Cheong

In ACIS 2023 Proceedings, 2023

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Social media platforms such as Facebook, Twitter/X, or TikTok are controlled by and rely on a single service provider. However, repeated controversies around data breaches, algorithmic bias, or misinformation have eroded trust in these centralised platforms. Concurrently, advancements in blockchain technology have opened avenues for decentralised social media that distribute control and data across a network of participants, thereby challenging their centralised counterparts. This study reviews existing literature on the topic and finds that what we know largely focuses on the technological implementation of decentralised social media. Therefore, we surveyed the literature for socio-technical dimensions of the phenomenon and found that control, privacy, operation, security, rewards, adoption, and research are central themes in this regard. We use those themes to derive recommendations for future research in Information Systems (IS) and beyond that aims to unpack individual, organisational, and societal implications of decentralised social media.
Approaches and Models for Teaching Digital Ethics in Information Systems Courses – A Review of the Literature

Minna Paltiel, Marc Cheong, Simon Coghlan, and Reeva Lederman

2023

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The Australasian Journal of Information Systems is a refereed journal that publishes articles contributing to Information Systems theory and practice.
To chat or bot to chat: Ethical issues with using chatbots in mental health

Simon Coghlan, Kobi Leins, Susie Sheldrick, Marc Cheong, Piers Gooding, and Simon D’Alfonso

2023

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This paper presents a critical review of key ethical issues raised by the emergence of mental health chatbots. Chatbots use varying degrees of artificial intelligence and are increasingly deployed in many different domains including mental health. The technology may sometimes be beneficial, such as when it promotes access to mental health information and services. Yet, chatbots raise a variety of ethical concerns that are often magnified in people experiencing mental ill-health. These ethical challenges need to be appreciated and addressed throughout the technology pipeline. After identifying and examining four important ethical issues by means of a recognised ethical framework comprised of five key principles, the paper offers recommendations to guide chatbot designers, purveyers, researchers and mental health practitioners in the ethical creation and deployment of chatbots for mental health.
Existentialism on social media

Marc Cheong

2023

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Social media has become a basis for helping us maintain human contact, especially as our alienation from our phenomenological experiences of ‘being human’ is becoming apparent due to the pandemic. I argue for how existentialist philosophy is crucial, more than ever, to interrogate our social media usage, which is a ‘necessary evil’ in our daily lives. Firstly, Kierkegaard’s critiques of the crowd and of the press are equally applicable to social media, which plays both roles: enabling an anonymous mass of public opinion and doubling-up as an information source, reducing responsibility on the individual. Secondly, social media leads to an intrinsic pressure to objectify one’s self (as portrayed) due to the possibility of an omnipresent Other, based on the technological design of networks. I will link my arguments on social media to other existential ideals and will conclude by suggesting changes that may promote existential ideals in one’s social media portrayal and engagement.
COVID Down Under: where did Australia’s pandemic apps go wrong?

Shaanan Cohney, and Marc Cheong

In 2023 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS), 2023

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Governments and businesses worldwide deployed a variety of technological measures to help prevent and track the spread of COVID-19. In Australia, these applications contained usability, accessibility, and security flaws that hindered their effectiveness and adoption. Australia, like most countries, has transitioned to treating COVID as endemic. However it is yet to absorb lessons from the technological issues with its approach to the pandemic. In this short paper we a) provide a systematization of the most notable events; b) identify and review different failure modes of these applications; and c) develop recommendations for developing apps in the face of future crises. Our work focuses on a single country. However, Australia’s issues are particularly instructive as they highlight surprisingly pitfalls that countries should address in the face of a future pandemic.
ETHICS-2023 Session D1 - Open Forum: Building a Technology Ethics Community

Thomas Creely, Marc Cheong, Heather Love, and Ketra Schmitt

In 2023 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS), 2023

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This session was an open forum where audience members were invited to participate in discussions of a number of themes with relevance to ethics and technology and to future conferences in the IEEE ETHICS series. The discussions took place in small groups, with groups reporting back to the full cohort for collaborative brainstorming.
‘Emerging proxies’ in information-rich machine learning: a threat to fairness?

Aidan James McLoughney, Jeannie Marie Paterson, Marc Cheong, and Anthony Wirth

In 2023 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS), 2023

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Anti-discrimination law in many jurisdictions effectively bans the use of race and gender in automated decision-making. For example, this law means that insurance companies should not explicitly ask about legally protected attributes, e.g., race, in order to tailor their premiums to particular customers. In legal terms, indirect discrimination occurs when a generally neutral rule or variable is used, but significantly negatively affects one demographic group. An emerging example of this concern is inclusion of proxy variables in Machine Learning (ML) models, where neutral variables are predictive of protected attributes. For example, postcodes or zip codes are representative of communities, and therefore racial demographics and social-economic class; i.e., a traditional example of ‘redlining’ pre-dating modern automated techniques [1]. The law struggles with proxy variables in machine learning: indirect discrimination cases are difficult to bring to court, particularly because finding substantial evidence that shows the indirect discrimination to be unlawful is difficult [2]. With more complex machine-learning models being developed for automated decision making, e.g., random forests or state-of-the-art deep neural networks, more data points on customers are accumulated [1], from a wide variety of sources. With such rich data, ML models can produce multiple interconnected correlations - such as that found in single neurons in a neural network, or single decision trees in a random forest - which are predictive of protected attributes, akin to traditional uses of discrete proxy variables. In this poster, we introduce the concept of "emerging proxies", that are a combination of several variables, from which the ML model could infer the protected attribute(s) of the individuals in the dataset. This concept differs from the traditional concept of proxies because rather than addressing a single proxy variable, a distribution of interconnected proxies would have to be addressed. Our contribution is to provide evidence for the capacity of complex ML models to identify protected attributes through the correlation of other variables. This correlation is not made explicitly through a discrete one to one relationship between variables, but through a many-to-one relationship. This contribution complements concerns raised in legal analyses of automated decision-making about proxies in ML models leading to indirect discrimination [3]. Our contribution shows that if an ML model contains “emerging proxies” for a protected attribute, the distribution of proxies will be a roadblock when attempting to de-bias the model, limiting the pathways available for addressing potential discrimination caused by the ML model.
"Tell us what’s going on": Exploring the information needs of pregnant and post-partum women in Australia during the pandemic with ’Tweets’, ’Threads’, and women’s views

Cassandra Caddy, Marc Cheong, Megan S C Lim, Robert Power, Joshua P Vogel, Zoe Bradfield, Benjamin Coghlan, Caroline S E Homer, and Alyce N Wilson

2023

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INTRODUCTION: The provision of maternity services in Australia has been significantly disrupted in response to the COVID-19 pandemic. Many changes were initiated quickly, often with rapid dissemination of information to women. The aim of this study was to better understand what information and messages were circulating regarding COVID-19 and pregnancy in Australia and potential information gaps. METHODS: This study adopted a qualitative approach using social media and interviews. A data analytics tool (TIGER-C19) was used to extract data from social media platforms Reddit and Twitter from June to July 2021 (in the middle of the third COVID-19 wave in Australia). A total of 21 individual semi-structured interviews were conducted with those who were, or had been, pregnant in Australia since March 2020. Social media data were analysis via inductive content analysis and interview data were thematically analysed. RESULTS: Social media provided a critical platform for sharing and seeking information, as well as highlighting attitudes of the community towards COVID-19 vaccines in pregnancy. Women interviewed described wanting further information on the risks COVID-19 posed to themselves and their babies, and greater familiarity with the health service during pregnancy, in which they would labour and give birth. Health providers were a trusted source of information. Communication strategies that allowed participants to engage in real-time interactive discussions were preferred. A real or perceived lack of information led participants to turn to informal sources, increasing the potential for exposure to misinformation. CONCLUSION: It is vital that health services communicate effectively with pregnant women, early and often throughout public health crises, such as the COVID-19 pandemic. This was particularly important during periods of increased restrictions on accessing hospital services. Information and communication strategies need to be clear, consistent, timely and accessible to reduce reliance on informal and potentially inaccurate sources.
The wisdom_of_crowds: An Efficient, Philosophically-Validated, Social Epistemological Network Profiling Toolkit

Colin Klein, Marc Cheong, Marinus Ferreira, Emily Sullivan, and Mark Alfano

In Complex Networks and Their Applications XI, 2023

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The epistemic position of an agent often depends on their position in a larger network of other agents who provide them with information. In general, agents are better off if they have diverse and independent sources. Sullivan et al. [19] developed a method for quantitatively characterizing the epistemic position of individuals in a network that takes into account both diversity and independence and presented a proof-of-concept, closed-source implementation on a small graph derived from Twitter data [19]. This paper reports on an open-source re-implementation of their algorithm in Python, optimized to be usable on much larger networks. In addition to the algorithm and package, we also show the ability to scale up our package to large synthetic social network graph profiling, and finally demonstrate its utility in analyzing real-world empirical evidence of ‘echo chambers’ on online social media, as well as interdisciplinary diversity in an academic communications network.
Exploring intellectual humility through the lens of artificial intelligence: Top terms, features and a predictive model

Ehsan Abedin, Marinus Ferreira, Ritsaart Reimann, Marc Cheong, Igor Grossmann, and Mark Alfano

2023

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Intellectual humility (IH) is often conceived as the recognition of, and appropriate response to, your own intellectual limitations. As far as we are aware, only a handful of studies look at interventions to increase IH – e.g. through journalling – and no study so far explores the extent to which having high or low IH can be predicted. This paper uses machine learning and natural language processing techniques to develop a predictive model for IH and identify top terms and features that indicate degrees of IH. We trained our classifier on the dataset from an existing psychological study on IH, where participants were asked to journal their experiences with handling social conflicts over 30 days. We used Logistic Regression (LR) to train a classifier and the Linguistic Inquiry and Word Count (LIWC) dictionaries for feature selection, picking out a range of word categories relevant to interpersonal relationships. Our results show that people who differ on IH do in fact systematically express themselves in different ways, including through expression of emotions (i.e., positive, negative, and specifically anger, anxiety, sadness, as well as the use of swear words), use of pronouns (i.e., first person, second person, and third person) and time orientation (i.e., past, present, and future tenses). We discuss the importance of these findings for IH and the value of using such techniques for similar psychological studies, as well as some ethical concerns and limitations with the use of such semi-automated classifications.
Generative AIs: Epistemic injustice in AI, writ large?

Marc Cheong, and E Abedin

In 2023 Australasian Association of Philosophy Conference, Melbourne, VIC., 2023

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Fricker’s (2007) framework on epistemic injustice has been applied to Artificial Intelligence (AI) systems, to explain the harm to epistemic agents who use and interact with such systems. These AI systems range from chatbots; to algorithmic decision-makers such as automated hiring systems; to social media recommenders. The rapid release, deployment, and use of Generative AI systems (GAIs) – fro text generators (e.g., ChatGPT) to image generators (e.g., DALL-E 2) – are a case in point. Users of GAIs running the risk of harm as knowers, based on the credibility excess afforded to the GAI, despite their propensity to ’hallucinate’ or make up facts. GAIs also inflict hermeneutical injustice, as their data models are not truly representative of society at large: an example is the issue of racial and gender biases in GAIs. Using empirical evidence from the modern crop of GAIs, this talk will illustrate the extent of epistemic injustice in GAIs, drawing upon Fricker (2007) as a framework. We will especially focus on how these injustices can be entrenched in such GAIs, owing to the way the data models are "updated" from version to version, and the ability for these GAIs to be ’cross-pollinated’ with other AI systems.
2023 State of the future of work

Leah Ruppanner, Brendan Churchill, David Bissell, Peter Ghin, Camilla Hydelund, Susan Ainsworth, Alysia Blackham, Jeff Borland, Marc Cheong, Michelle Evans, Lea Frermann, Tania King, and Frank Vetere

2023

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2022

Polarization and trust in the evolution of vaccine discourse on Twitter during COVID-19

Ignacio Ojea Quintana, Ritsaart Reimann, Marc Cheong, Mark Alfano, and Colin Klein

2022

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Trust in vaccination is eroding, and attitudes about vaccination have become more polarized. This is an observational study of Twitter analyzing the impact that COVID-19 had on vaccine discourse. We identify the actors, the language they use, how their language changed, and what can explain this change. First, we find that authors cluster into several large, interpretable groups, and that the discourse was greatly affected by American partisan politics. Over the course of our study, both Republicans and Democrats entered the vaccine conversation in large numbers, forming coalitions with Antivaxxers and public health organizations, respectively. After the pandemic was officially declared, the interactions between these groups increased. Second, we show that the moral and non-moral language used by the various communities converged in interesting and informative ways. Finally, vector autoregression analysis indicates that differential responses to public health measures are likely part of what drove this convergence. Taken together, our results suggest that polarization around vaccination discourse in the context of COVID-19 was ultimately driven by a trust-first dynamic of political engagement.
Professional Presentation and Projected Power: A Case Study of Implicit Gender Information in English CVs

Jinrui Yang, Sheilla Njoto, Marc Cheong, Leah Ruppanner, and Lea Frermann

In 5th Workshop on Natural Language Processing and Computational Social Science (NLP+CSS), 2022

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Gender discrimination in hiring is a pertinent and persistent bias in society, and a common motivating example for exploring bias in NLP. However, the manifestation of gendered language in application materials has received limited attention. This paper investigates the framing of skills and background in CVs of self-identified men and women. We introduce a data set of 1.8K authentic, English-language, CVs from the US, covering 16 occupations, allowing us to partially control for the confound occupation-specific gender base rates. We find that (1) women use more verbs evoking impressions of low power; and (2) classifiers capture gender signal even after data balancing and removal of pronouns and named entities, and this holds for both transformer-based and linear classifiers.
The Affiliative Use of Emoji and Hashtags in the Black Lives Matter Movement in Twitter

Mark Alfano, Ritsaart Reimann, Ignacio Ojea Quintana, Anastasia Chan, Marc Cheong, and Colin Klein

2022

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Protests and counter-protests seek to draw and direct attention and concern with confronting images and slogans. In recent years, as protests and counter-protests have partially migrated to the digital space, such images and slogans have also gone online. Two main ways in which these images and slogans are translated to the online space is through the use of emoji and hashtags. Despite sustained academic interest in online protests, hashtag activism, and the use of emoji across social media platforms, little is known about the specific functional role that emoji and hashtags play in online social movements. In an effort to fill this gap, the current paper studies both hashtags and emoji in the context of the Twitter discourse around the Black Lives Matter movement.
Attention and counter-framing in the Black Lives Matter movement on Twitter

Colin Klein, Ritsaart Reimann, Ignacio Ojea Quintana, Marc Cheong, Marinus Ferreira, and Mark Alfano

2022

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The social media platform Twitter platform has played a crucial role in the Black Lives Matter (BLM) movement. The immediate, flexible nature of tweets plays a crucial role both in spreading information about the movement’s aims and in organizing individual protests. Twitter has also played an important role in the right-wing reaction to BLM, providing a means to reframe and recontextualize activists’ claims in a more sinister light. The ability to bring about social change depends on the balance of these two forces, and in particular which side can capture and maintain sustained attention. The present study examines 2 years worth of tweets about BLM (about 118 million in total). Timeseries analysis reveals that activists are better at mobilizing rapid attention, whereas right-wing accounts show a pattern of moderate but more sustained activity driven by reaction to political opponents. Topic modeling reveals differences in how different political groups talk about BLM. Most notably, the murder of George Floyd appears to have solidified a right-wing counter-framing of protests as arising from dangerous “terrorist” actors. The study thus sheds light on the complex network and rhetorical effects that drive the struggle for online attention to the BLM movement.
A tragic coalition of the rational and irrational: A threat to collective responses to COVID-19

Marinus Ferreira, Marc Cheong, Colin Klein, and Mark Alfano

2022

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ABSTRACTThere is not as much resistance to COVID-19 mitigation as there seems, but there are structural features that make resistance seem worse than it is. Here, we describe two ways that the problem seeming to be worse than it is can make it worse. First, visible hesitation to implement COVID-19 responses signals to the wider society that mitigation measures may not succeed, which undermines people?s conditional willingness to join in on those efforts. Second, our evaluations of others? willingness to implement these measures are informed by our attempts to mind-read them. Yet attempts to mind-read groups often mislead us, because groups invariably act from diverse motives, whereas mind-reading works best when identifying relatively stable and consistent motivations. This means that a small minority of people refusing to implement measures can have an outsized prominence that prompts mind-reading to diagnose widespread hesitation. These two factors form a feedback loop with each other: we see some people?s hesitation, which prompts us to mind-read other people as being more uncertain about the responses than they actually are, which undermines our confidence in the responses, which in turn encourages others to mind-read this hesitation, which further undermines confidence.
Technological Stewardship and Responsible Innovation: A Mindset, an Ethos, and an Interdisciplinary Undertaking

Brandiff Caron, Marc Cheong, Jin Sol Kim, Jason Lajoie, Heather A Love, and Ketra Schmitt

2022

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In the foreword to Responsible Innovation: Managing the Responsible Emergence of Science and Innovation in Society, the concept of responsible innovation is pitched as a necessary counterpoint to “innovation’s systemic irresponsibility,” an undertaking that aims to “nudge [technologies’] trajectories in various ways toward responsible, desirable futures” [1, p. xii]. Within the leading journal Responsible Innovation, the concept is often treated as synonymous with responsible research and innovation (RRI); indeed, the European Commission suggests an overlapping set of aims in its description of RRI as a “comprehensive approach” to developing research methods and new technologies, one that:
[REVIEW] “AI Assistants.” Roberto Pieraccini, 2021, MIT Press

Marc Cheong

2022

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Using public data to measure diversity in computer science research communities: A critical data governance perspective

Rachelle Bosua, Marc Cheong, Karin Clark, Damian Clifford, Simon Coghlan, Chris Culnane, Kobi Leins, and Megan Richardson

2022

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Encouraging and supporting diversity and inclusion in computer science research communities is a critical issue for many reasons, including the ethical and robust design, delivery and publication of research that addresses real-world situations ranging from the use of digital tools in health to predictive policing to workplace hiring practices, just to name a few. One way to measure diversity is to apply analytical research methods to data sourced from the public domain for use in research. However, attempts to measure diversity using public data may themselves raise legal and ethical questions about the provenance of the data, research methods adopted, and treatment of diversity in the publication of results. This article interrogates the challenges of measuring diversity using public data, examining an illustrative case study framed around an academic research project at an Australian university using a public data set to identify gender representation in computer science communities. Employing a critical data governance perspective, we point to a range of ethical and legal concerns and recommend greater regulatory guardrails to better balance public interests in research and the privacy, data protection and other ethical interests of research subjects.
Ethical Dilemmas for @Celebrities: Promoting #Intimacy, Facing #Inauthenticity, and Defusing #Invectiveness

Marc Cheong

2022

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The rise of social media mediated celebrity culture raises several philosophical concerns. It is not uncommon to see, for example, Hollywood actors being placed in the same bracket as YouTube artists and Instagram influencers. The increased perceived ‘connectivity’ afforded by social media allows online celebrities to reach more fans and increases the perceived engagement or intimacy in the fan-celebrity relationship. In the present article I argue that this online relationship, which is beneficial to celebrities (for brand development) and social media companies (in profiting from the high engagement), has the potential to cause harm to both the celebrities as well as their fanbases (and sometimes innocent third parties). Firstly, I argue that celebrities’ attempts to be more intimate or authentic with their fanbase can turn out to be – more often than not – acts of Sartrean bad faith. Celebrities who tend to project a certain self-styled ‘online authenticity’, given the context of social media’s attention economy, can run the risk of treating a fan as a means-to-an-end, rather than an autonomous for-itself. Secondly, I argue that this phenomenon can catalyse real-world harms, by drawing upon network theory and social psychology. I argue that the connectedness of social media, ‘blending into the crowd’, and social media technologies’ ethos of optimising-for-engagement, leads to real harms to human wellbeing when fans do not engage critically and mindfully with online celebrities. Examples include Trump-esque celebrity politicians trying to drum up populist influence; celebrities feuding online encouraging fans’ conduct of harmful trolling and harassment; and vilification of those who do not subscribe to the same perceived worldview of a celebrity’s fandom. Following my arguments within, I shall outline an online celebrity’s ethical duties in their social media engagement, with emphasis on the duties that celebrity status comes with, particularly in preventing harms that transcend the online into the offline.
Book review: Luca Possati (2021): “The algorithmic unconscious: how psychoanalysis helps in understanding AI” (Routledge)

Marc Cheong

2022

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Gender Bias in AI Recruitment Systems: A Sociological- and Data Science-based Case Study

Sheilla Njoto, Marc Cheong, Reeva Lederman, Aidan McLoughney, Leah Ruppanner, and Anthony Wirth

In Proceedings of the 2022 IEEE International Symposium on Technology and Society (ISTAS), 2022

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Teaching Digital Ethics in Information Systems

Minna Paltiel, Marc Cheong, Simon Coghlan, and Reeva Lederman

In ACIS 2022 Proceedings, 2022

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This paper reviews and discusses the little-explored topic of digital ethics education in Information Systems and related fields. The importance of teaching digital ethics to students studying information and communication technologies (ICT) is increasingly recognised. However, it is unclear how ethics should best be taught to these future ICT practitioners. Using a narrative literature review methodology to explore existing scholarly work, this paper identifies five pedagogical theories related to digital ethics education. Additionally, the paper outlines approaches that deploy standalone ethics units, integrated ethics teaching, and hybrid teaching approaches, and identifies the employment of and emphasis given to various moral theories in digital ethics education. The paper then discusses how these three sets of findings—namely, different pedagogical approaches, degree of integration of ethics teaching, and use of moral theories—are related to each other. It provides educators with information and reflections to consider when designing digital ethics teaching.
A Tale of Two Anti-Heroes

Marc Cheong

2022

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Social Media Harms as a Trilemma: Asymmetry, Algorithms, and Audacious Design Choices

Marc Cheong

In Proceedings of the 2022 IEEE International Symposium on Technology and Society (ISTAS), 2022

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2021

Timely Integration of User Generated Responses to C19: TIGER C19 - Report 6, September 2021

Robert Power, Ben Coghlan, Marc Cheong, Simon Coghlan, and Cassie Caddy

2021

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The Optimise Study: Vaccination knowledge, attitudes and beliefs

Freya Saich, Edwin Kwong, Katherine Heath, Aimée Altermatt, Stephanie Munari, Bronwen Merner, Sophie Hill, Mark Stoové, Daniel Griffiths, Alex Collie, Megan Lim, Sarah Eddy, Niamh Meagher, Colin McDougall, Katitza Marinkovic Chavez, Lauren Carpenter, Stephanie Fletcher-Lartey, David Price, Lisa Gibbs, Freya Shearer, Robert Power, Marc Cheong, Cassandra Caddy, Alisa Pedrana, Katherine Gibney, and Margaret Hellard

2021

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Timely Integration of User Generated Responses to C19: TIGER C19 - Report 5, August 2021

Robert Power, Ben Coghlan, Marc Cheong, Simon Coghlan, and Cassie Caddy

2021

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The Optimise Study: Impact of government restrictions on risk mitigation strategies

Freya Saich, Edwin Kwong, Katherine Heath, Aimée Altermatt, Stephanie Munari, Bronwen Merner, Sophie Hill, Robert Power, Marc Cheong, Cassandra Caddy, Daniel Griffiths, Alex Collie, Alisa Pedrana, Katherine Gibney, and Margaret Hellard

2021

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Timely Integration of User Generated Responses to C19: TIGER C19 - Report 4, June 2021

Robert Power, Ben Coghlan, Marc Cheong, Simon Coghlan, and Cassie Caddy

2021

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Timely Integration of User Generated Responses to C19: TIGER C19 - Report 3, April-May 2021

Robert Power, Ben Coghlan, Marc Cheong, Simon Coghlan, and Cassie Caddy

2021

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LEx: A Framework for Operationalising Layers of Machine Learning Explanations

Ronal Singh, Upol Ehsan, Marc Cheong, Mark O Riedl, and Tim Miller

In ACM CHI 2023 Workshop on Human-Centered Explainable AI (HCXAI), 2021

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Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally. In this position paper, we define a framework called the \textitlayers of explanation (LEx), a lens through which we can assess the appropriateness of different types of explanations. The framework uses the notions of \textitsensitivity (emotional responsiveness) of features and the level of \textitstakes (decision’s consequence) in a domain to determine whether different types of explanations are \textitappropriate in a given context. We demonstrate how to use the framework to assess the appropriateness of different types of explanations in different domains.
Timely Integration of User Generated Responses to C19: TIGER C19 - Report 2, April 2021

Robert Power, Ben Coghlan, Marc Cheong, Simon Coghlan, and Cassie Caddy

2021

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Computer Science Communities: Who is Speaking, and Who is Listening to the Women? Using an Ethics of Care to Promote Diverse Voices

Marc Cheong, Kobi Leins, and Simon Coghlan

In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, New York, NY, USA, 2021

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Those working on policy, digital ethics and governance often refer to issues in ’computer science’, that includes, but is not limited to, common subfields such as Artificial Intelligence (AI), Computer Science (CS) Computer Security (InfoSec), Computer Vision (CV), Human Computer Interaction (HCI), Information Systems, (IS), Machine Learning (ML), Natural Language Processing (NLP) and Systems Architecture. Within this framework, this paper is a preliminary exploration of two hypotheses, namely 1) Each community has differing inclusion of minoritised groups (using women as our test case, by identifying female-sounding names); and 2) Even where women exist in a community, they are not published representatively. Using data from 20,000 research records, totalling 503,318 names, preliminary data supported our hypothesis. We argue that ACM has an ethical duty of care to its community to increase these ratios, and to hold individual computing communities to account in order to do so, by providing incentives and a regular reporting system, in order to uphold its own Code.
Timely Integration of User Generated Responses to C19: TIGER C19 - Report 1, March 2021

Robert Power, Ben Coghlan, Marc Cheong, and Simon Coghlan

2021

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Who Oversees the Government’s Automated Decision-Making? Modernising Regulation and Review of Australian Automated Administrative Decision-Making

Marc Cheong, and Kobi Leins

2021
To Honor our Heroes: Analysis of the Obituaries of Australians Killed in Action in WWI and WWII

Marc Cheong, and Mark Alfano

In 2020 25th International Conference on Pattern Recognition (ICPR), 2021

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Obituaries represent a prominent way of expressing the human universal of grief. According to philosophers, obituaries are a ritualized way of evaluating both individuals who have passed away and the communities that helped to shape them. The basic idea is that you can tell what it takes to count as a good person of a particular type in a particular community by seeing how persons of that type are described and celebrated in their obituaries. Obituaries of those killed in conflict, in particular, are rich repositories of communal values, as they reflect the values and virtues that are admired and respected in individuals who are considered to be heroes in their communities. In this paper, we use natural language processing techniques to map the patterns of values and virtues attributed to Australian military personnel who were killed in action during World War I and World War II. Doing so reveals several clusters of values and virtues that tend to be attributed together. In addition, we use named entity recognition and geotagging the track the movements of these soldiers to various theatres of the wars, including North Africa, Europe, and the Pacific.
The Hidden Harms of Targeted Advertising by Algorithms and Interventions from the Consumer Protection Toolkit

Jeannie Marie Paterson, Shanton Chang, Marc Cheong, Chris Culnane, Suelette Dreyfus, and Dana McKay

2021

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Developments in pervasive data collection and predictive data analytics are allowing firms to target consumers with increasingly precise personalised, behavioural and contextual advertising. These techniques give rise to new risks of harm in the attention economy by unduly influencing or manipulating consumers’ decision-making, and also by narrowing the product options visible and available to them. In many countries, the legal response to concerns about targeted advertising by algorithm has been focused on privacy protection and data rights. These are important initiatives. However, consent-based data rights are unlikely to provide a comprehensive or even adequate response to the risks of harm to consumers occasioned by the kinds of algorithmically targeted advertising that are now possible. This paper suggests that a suite of responses from the consumer protection toolkit are required to respond to the different manifestations of algorithmically targeted advertising. These include bans and warnings as well as making use of standard safety-net prohibitions on misleading and unconscionable/unfair conduct already in place in many jurisdictions.

2020

Ethical Implications of AI Bias as a Result of Workforce Gender Imbalance

Marc Cheong, Reeva Lederman, Aidan McLoughney, Sheilla Njoto, Leah Ruppanner, and Anthony Wirth

2020

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Tracking, tracing, trust: contemplating mitigating the impact of COVID-19 through technological interventions

Simon Coghlan, Marc Cheong, and Benjamin Coghlan

2020

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2019

Guest Editors’ Introduction: Examining Moral Emotions in Nietzsche with the Semantic Web Exploration Tool: Nietzsche

Mark Alfano, and Marc Cheong

2019

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Asymmetry in Online Social Networks

Marc Cheong

In 2019 Australasian Association of Philosophy Conference, Wollongong, 2019

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The concept of ’authenticity’ is highly valued on social media sites (SMSes), despite its ambiguous nature and definition. One interpretation of ’authenticity’ by media scholars is a human’s congruence with online ...

2018

Technological Seduction and Self-Radicalization

Mark Alfano, J Adam Carter, and Marc Cheong

2018

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Many scholars agree that the Internet plays a pivotal role in self-radicalization, which can lead to behaviors ranging from lone-wolf terrorism to participation in white nationalist rallies to mundane bigotry and voting for extremist candidates. However, the mechanisms by which the Internet facilitates self-radicalization are disputed; some fault the individuals who end up self-radicalized, while others lay the blame on the technology itself. In this paper, we explore the role played by technological design decisions in online self-radicalization in its myriad guises, encompassing extreme as well as more mundane forms. We begin by characterizing the phenomenon of technological seduction. Next, we distinguish between top-down seduction and bottom-up seduction. We then situate both forms of technological seduction within the theoretical model of dynamical systems theory. We conclude by articulating strategies for combating online self-radicalization.
“Power to the People!”: Social Media Discourse on Regional Energy Issues in Australia

Kerri Morgan, Marc Cheong, and Susan Bedingfield

2018
Experimental investigations of #authenticity online

Marc Cheong

In 2018 Australasian Association of Philosophy/New Zealand Association of Philosophy Conference, Wellington, 2018

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The concept of ’authenticity’ is highly valued on social media sites (SMSes), despite its ambiguous nature and definition. One interpretation of ’authenticity’ by media scholars is a human’s congruence with online ...

2017

The Algorithm as Human: a cross-disciplinary discussion of anthropology in an increasingly data-driven world

J Byrne, and M Cheong

In AAS/ASA/ASAANZ 2017 Shifting States Conference, 2017
Existentialism and the ‘IT Crowd’

Marc Cheong

In 2017 Australasian Association of Philosophy Conference, Adelaide, 2017
From Sartre to #nofilter: On Authenticity: dialogues between existentialism & online technology

Marc Cheong

In 2017 Australasian Society for Continental Philosophy Conference, Hobart, 2017

2016

An investigation of how the Australian brewing industry influence consumers on Twitter

Torgeir Aleti, Paul Harrigan, Marc Cheong, and Will Turner

2016

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Twitter, Alcohol and Wasted War Stories: Potted Lessons in Social Media-Based Methodologies

Marc Cheong, Torgeir Aleti, and Will Turner

2016

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2013

Inferring Social Behavior and Interaction on Twitter by Combining Metadata about Users & Messages

Marc Cheong

2013

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