title: Exploring the Possibility of an Intergenerational Right to Return to Earth

authors: Brennan, Anna

abstract: If human civilisation realises its grand ambitions to establish extraterrestrial human settlements on Mars in the not-so-distant future, how will human rights apply and be enforced? Rather than States, it is thought that space companies such as SpaceX, Blue Origin and the Sierra Nevada Corporation will be the first to establish extraterrestrial settlements. Rapid technological developments over the last decade mean that these ambitions are no longer science fiction. This article considers the scope of the right to freedom of movement in outer space, especially the right to return to Earth. Due to the perilous nature of the journey from Earth to Mars and the harsh environment in which the first settlements will be established, this article considers whether there are any legitimate and, indeed,
permissible circumstances where the right to return to Earth will be restricted. The final part of this article will explore the possibility that future descendants of the first wave of settlers to Mars will have an intergenerational right to return to Earth
despite having been born on the Red Planet.

text: Exploring the Possibility of an Intergenerational Right to Return to Earth If human civilisation realises its grand ambitions to establish extraterrestrial human settlements on Mars in the not-so-distant future, how will human rights apply and be enforced? Rather than States, it is thought that space companies such as SpaceX, Blue Origin and the Sierra Nevada Corporation will be the first to establish extraterrestrial settlements. Rapid technological developments over the last decade mean that these ambitions are no longer science fiction. This article considers the scope of the right to freedom of movement in outer space, especially the right to return to Earth. Due to the perilous nature of the journey from Earth to Mars and the harsh environment in which the first settlements will be established, this article considers whether there are any legitimate and, indeed,
permissible circumstances where the right to return to Earth will be restricted. The final part of this article will explore the possibility that future descendants of the first wave of settlers to Mars will have an intergenerational right to return to Earth
despite having been born on the Red Planet.

year: 2024

title: Context-awareness in the wild: An investigation into the existing uses of context in everyday life

authors: Pascoe, Jason

subjects: computer science

abstract: It is common for literature on context-awareness to focus on specific application domains or on the development of models and frameworks to facilitate context-awareness. In this study, however, we take a step back from such work in order to investigate how regular people may already be employing, or trying to employ, context-awareness in their everyday lives using existing mobile tools (such as mobile phones, paper notepads, etc.). We believe that an understanding of these existing real-world uses and needs of context-awareness will help to better inform and direct research efforts in this domain. Towards this aim we present the findings of a user study in which twelve randomly selected individuals recorded a diary on their use of mobile tools over the period of two days. The findings clearly demonstrate that people do indeed currently employ a wide variety of contexts and context-aware behaviours, albeit in a manner that is often imperfect and at a sub-conscious level.

text: Context-awareness in the wild: An investigation into the existing uses of context in everyday life It is common for literature on context-awareness to focus on specific application domains or on the development of models and frameworks to facilitate context-awareness. In this study, however, we take a step back from such work in order to investigate how regular people may already be employing, or trying to employ, context-awareness in their everyday lives using existing mobile tools (such as mobile phones, paper notepads, etc.). We believe that an understanding of these existing real-world uses and needs of context-awareness will help to better inform and direct research efforts in this domain. Towards this aim we present the findings of a user study in which twelve randomly selected individuals recorded a diary on their use of mobile tools over the period of two days. The findings clearly demonstrate that people do indeed currently employ a wide variety of contexts and context-aware behaviours, albeit in a manner that is often imperfect and at a sub-conscious level.

year: 2007

title: Data and model bias in artificial intelligence for healthcare applications in New Zealand

authors: Yogarajan, Vithya

abstract: Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

text: Data and model bias in artificial intelligence for healthcare applications in New Zealand Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

year: 2022-12-22