AI and ethics最新文献

The digital divide remains an ongoing societal concern, with digital exclusion shown to have a significantly detrimental impact on people’s quality of life. Artificial intelligence (AI), the latest wave of digitalisation, is being integrated into the fabric of society at an accelerated rate, the speed of which has prompted ethical concerns. Without addressing the digital divide, the AI revolution risks exacerbating the existing consequences of digital exclusion and limiting the potential for all people to reap the benefits provided by AI. To understand the factors that might contribute to experiences of AI, and how these might be related to digital exclusion, we surveyed a diverse online community sample (N = 303). We created a novel measure of digital confidence capturing individual levels of awareness, familiarity, and sense of competence with digital technology. Results indicated that measures of digital confidence were predicted by structural, behavioural, and psychological differences, such that women, older people, those on lower salaries, people with less digital access, and those with lower digital well-being, reported significantly less digital confidence. Furthermore, digital confidence significantly moderated the relationship between people’s experiences with everyday AI technologies and their general attitudes towards AI. This understanding of the spill-over effects of digital exclusion onto experiences of AI is fundamental to the articulation and delivery of inclusive AI.

Gender Tapestry is a multi-stage interactive AI art project, challenging traditional gender classification systems. This project diverges from binary approaches by recognizing the individuality of the gender experience and expression. This paper draws parallels to the ways color perception differs amongst people and how gender is also experienced in different ways due to lived experiences. Gender Tapestry uses a multi-label classification system, with predictions extending across six gender categories, with a custom RGB color generated based on the outcomes. Participants receive binary images of their face in their personalized colors and styles, while their uploaded photos contribute to a live Generative Adversarial Network training process. This work was created in response to the very binary representations of gender in AI and the lack of representation for genders outside of the binary. The culmination is an evolving mosaic artwork, incorporating all users' custom colors, illustrating gender as a fluid construct. The mosaic gains in complexity as more images are added and more colors enter the mix, creating a community artwork on gender as a 3D color spectrum. This work contributes to the discourse on diversity and inclusion in AI, emphasizing the fluidity of gender and fostering unconventional artistic representations.

Communication poses a challenge for the deaf and hearing loss community. This difficulty is even more pronounced in the families of Children of Deaf Adults (CODAs) and Parents of Deaf Children (PODCs). To help these families overcome this challenge, we design an AI-empowered interactive bi-directional Australian Sign Language (i.e., Auslan) dictionary application to facilitate communication within a household. Technically, our APP can not only look up sign gestures for the given English words but also translate isolated Auslan gestures into English. Through an inviting user interface and experience design, we can further improve engagement within the CODA and PODC families while enabling Auslan education at home. The positive user experience underscores the success of our APP not only in leveraging AI to revolutionise Auslan education but also in promoting cross-generational language acquisition and communication.

Chatbots have emerged as a potent artificial intelligence (AI) tool for expediting expert knowledge, including evidence used for conservation research and practices. While digital technologies can support the curation and analysis of vast amounts of conservation datasets to inform best practices, AI-driven solutions raise ethical concerns around what source of evidence is used or not. This paper examines the ethical issues around sources, biases, and representation of conservation evidence formulated by chatbots. We interviewed two versions of ChatGPT, GPT-3.5-turbo and GPT-4, regarding knowledge available for ecological restoration and analysed 40,000 answers. Our results show that these chatbot developments are expanding the inclusion of diverse data sources and improving the accuracy of the responses. However, these technical developments do not necessarily imply ethical considerations in terms of fair representation and unbiased inclusion of diverse knowledge offered by different sources of expertise. While the updated model expands the descriptions ofgeographical locations and organizations, there remain limitations regarding equitable representation of different expertise and stakeholders. The updated version of GPT still relies heavily on evidence from high-income countries (88%), North American expertise (67%), and male academics (46%) with limited contributions from minority groups, such as Indigenous organizations (10%) and low-income countries (2%). In conclusion, the ethical implications within generative AI reveal the crucial requirement of human-centered negotiations to consider how knowledge practices are legitimized and embedded in the development and use of chatbots.