Towards a Taxonomy of Ethical Considerations in Crowdsourcing

Crowdsourcing is a growing industry, where millions of individuals and businesses have begun tapping into the crowd to perform work. Despite this growth, crowd work and labor contains few regulations. Previous researchers have highlighted examples of ethical challenges organizations and individuals face in crowdsourcing. This paper explores these challenges, using Value Sensitive Design and Transparency literature to identify candidate ethical principles in crowdsourcing. Integrating these principles with ethical dilemmas, crowdsourcing models, and affected stakeholders, this research uses a deductive approach to develop a taxonomic framework of ethical considerations in crowdsourcing. The resulting taxonomy provides practical and theoretical contributions. Organizations choosing to use crowdsourcing can refer to the classification to understand ethical implications, as well as accounting for ethical requirements in the design and governance of projects. Researchers can expand the classification to gain understanding of each element and the interrelationships. Finally, we describe specific directions for future research

Crime applications and social machines: crowdsourcing sensitive data

The authors explore some issues with the United Kingdom (U.K.) crime reporting and recording systems which currently produce Open Crime Data. The availability of Open Crime Data seems to create a potential data ecosystem which would encourage crowdsourcing, or the creation of social machines, in order to counter some of these issues. While such solutions are enticing, we suggest that in fact the theoretical solution brings to light fairly compelling problems, which highlight some limitations of crowdsourcing as a means of addressing Berners-Lee’s “social constraint.” The authors present a thought experiment – a Gendankenexperiment - in order to explore the implications, both good and bad, of a social machine in such a sensitive space and suggest a Web Science perspective to pick apart the ramifications of this thought experiment as a theoretical approach to the characterisation of social machine

GiusBERTo: Italy’s AI-Based Judicial Transformation: A Teaching Case

In an age when open access to law enforcement files and judicial documents can erode individual privacy and confidentiality, miscreants can abuse this open access to personal information for blackmail, misinformation, and even social engineering. Yet, limiting access to law enforcement and court cases is a freedom-of-information violation. To address this tension, this collaborative action-research-based teaching case exemplifies how Italy’s Corte dei Conti (Court of Auditors) used artificial intelligence in the automated deidentification and anonymization of court documents in Italy’s public sector. This teaching case is aimed at undergraduate and graduate students learning about Artificial Intelligence (AI), Large Language Model (LLM) (e.g., ChatGPT) evolution, development, and operations. The case will help students learn the origin and evolution of AI transformer models and architectures, and discusses the GiusBERTo operation and process, highlighting opportunities and challenges. GiusBERTo, Italy’s custom-AI model, offers an innovative approach that walks a tightrope between anonymizing Italy’s judicial court documents without sacrificing context or information loss. The case ends with a series of questions, challenges, and potential for LLMs in data anonymization

Privacy Rarely Considered: Exploring Considerations in the Adoption of Third-Party Services by Websites

Modern websites frequently use and embed third-party services to facilitate web development, connect to social media, or for monetization. This often introduces privacy issues as the inclusion of third-party services on a website can allow the third party to collect personal data about the website’s visitors. While the prevalence and mechanisms of third-party web tracking have been widely studied, little is known about the decision processes that lead to websites using third-party functionality and whether efforts are being made to protect their visitors' privacy. We report results from an online survey with 395 participants involved in the creation and maintenance of websites. For ten common website functionalities we investigated if privacy has played a role in decisions about how the functionality is integrated, if specific efforts for privacy protection have been made during integration, and to what degree people are aware of data collection through third parties. We find that ease of integration drives third-party adoption but visitor privacy is considered if there are legal requirements or respective guidelines. Awareness of data collection and privacy risks is higher if the collection is directly associated with the purpose for which the third-party service is used

An investigation in to different factors related to problematic smartphone use

Smartphone use is increasing, and problematic smartphone use (PSU) has frequently been labelled a public health concern. Building upon the vast number of studies exploring the relationship between different correlates and PSU, many reviews exist exploring how PSU relates to a range of psychological factors. Previously no succinct review existed to help guide researchers/clinicians to make sense of this area or aid theory/intervention development. A meta-review was undertaken that synthesised reviews exploring correlates of PSU between the years of 2019 – 2022. Sixteen reviews were synthesised into five main themes, sleep, emotional and mental health factors, trait factors, ways of coping and physical activity. There was a consistent positive relationship between PSU and increased emotional and mental health difficulties, poorer sleep, trait factors (such as insecure attachment), unhelpful ways of coping and reduced levels of physical activity. However, differentmethodological limitations mean some associations should be interpreted cautiously and not generalised to other samples (physical activity or ways of coping). This meta-review supports the view that different correlates are related to PSU across different themes, countries and, to some extent, populations. Studies sampling older populations that also utilise models used in psychological therapy are recommended for areas of future research

Certification Systems for Machine Learning: Lessons from Sustainability

Concerns around machine learning’s societal impacts have led to proposals to certify some systems. While prominent governance efforts to date center around networking standards bodies such as the Institute of Electrical and Electronics Engineers (IEEE), we argue that machine learning certification should build on structures from the sustainability domain. Policy challenges of machine learning and sustainability share significant structural similarities, including difficult to observe credence properties, such as data collection characteristics or carbon emissions from model training, and value chain concerns, including core-periphery inequalities, networks of labor, and fragmented and modular value creation. While networking-style standards typically draw their adoption and enforcement from functional needs to conform to enable network participation, machine learning, despite its digital nature, does not benefit from this dynamic. We therefore apply research on certification systems in sustainability, particularly of commodities, to generate lessons across both areas, informing emerging proposals such as the EU’s AI Act

Breaking the multi colored box: a study of CAPTCHA

Communication is faster than ever. Innovations in low cost network computing have brought an era in which people can effortlessly and instantaneously view and post opinions collaboratively with others across the world. With such an infrastructure of public message boards, chat rooms and instant messaging systems, there is also a large potential for abuse by people wishing to capitalize on such open services by posting unsolicited advertisements. An entire industry has been constructed around the prevention of unsolicited electronic advertisements (SPAM). This thesis examines various techniques for preventing SPAM, focusing on Completely Automated Public Turing Tests to Tell Computers and Humans Apart (CAPTCHA), a challenge/response technique where an image is displayed with text that is heavily distorted. It also examines the feasibility of breaking CAPTCHA programmatically, alternatives to CAPTCHA based on filtering, improvements to CAPTCHA using photo recognition and avoiding the need for CAPTCHA using naïve approaches