title: Terrorism and religious fundamentalism: Prospects for a predictive paradigm

authors: Pratt, Douglas

subjects: religion

abstract: Although fundamentalism is neither new nor limited to one culture, globalized communications and recent political events have highlighted Christian and Muslim forms. Since links are often perceived between fundamentalism, extremism and terrorism, most commonly with respect to Islamic fundamentalism, it is desirable to delineate a model of fundamentalism which permits some degree of prediction as to its possible extension into political action. The term ‘religious fundamentalism’, it is argued here, can denote a type of worldview that can in fact be found in various religions of the world of today. Specifically, it may denote a shift in mentality from the relative harmlessness of an otherwise quaint, ultra-religious belief system to the readiness to pursue aggressively impositional, and in some cases terrorist activity. The article seeks to work out the details of these potential connections paradigmatically.

text: Terrorism and religious fundamentalism: Prospects for a predictive paradigm Although fundamentalism is neither new nor limited to one culture, globalized communications and recent political events have highlighted Christian and Muslim forms. Since links are often perceived between fundamentalism, extremism and terrorism, most commonly with respect to Islamic fundamentalism, it is desirable to delineate a model of fundamentalism which permits some degree of prediction as to its possible extension into political action. The term ‘religious fundamentalism’, it is argued here, can denote a type of worldview that can in fact be found in various religions of the world of today. Specifically, it may denote a shift in mentality from the relative harmlessness of an otherwise quaint, ultra-religious belief system to the readiness to pursue aggressively impositional, and in some cases terrorist activity. The article seeks to work out the details of these potential connections paradigmatically.

year: 2006-06

title: IP round-up: Recent decisions from the courts (May 2008)

authors: Kingsbury, Anna

subjects: law

abstract: This was a trade mark case in which the plaintiff, Portacom New Zealand Ltd, sought an interim injunction and orders requiring that the defendants remove "Port A Room" from all marketing materials and that any such branded materials be delivered up.

text: IP round-up: Recent decisions from the courts (May 2008) This was a trade mark case in which the plaintiff, Portacom New Zealand Ltd, sought an interim injunction and orders requiring that the defendants remove "Port A Room" from all marketing materials and that any such branded materials be delivered up.

year: 2008-05

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