This synopsis is an invitation to see New Zealand’s history through an A.I. lens, unclouded by judgement, aiming to respect all perspectives. From the arrival of the Polynesians to the colonial era and beyond, this book delves into the rich heritage, significant events and key figures that have shaped the nation. Through an objective lens, readers gain insight into the land’s Mori origins, European settlement, the Treaty of Waitangi, socio-economic developments and contemporary challenges. You are invited to join in this exploratory journey, armed with curiosity and an open mind as we navigate through the annals of New Zealand history; to re-examine historical accounts, providing a narrative both expansive and inclusive, avoiding political and cultural bias. It has been inspired especially for New Zealand history students of all ages, travellers and aficionados.
This synopsis is an invitation to see New Zealand's history through an A.I. lens, unclouded by judgement, aiming to respect all perspectives. From the arrival of the Polynesians to the colonial era and beyond, this book delves into the rich heritage, significant events and key figures that have shaped the nation. Through an objective lens, readers gain insight into the land's Mori origins, European settlement, the Treaty of Waitangi, socio-economic developments and contemporary challenges. You are invited to join in this exploratory journey, armed with curiosity and an open mind as we navigate through the annals of New Zealand history; to re-examine historical accounts, providing a narrative both expansive and inclusive, avoiding political and cultural bias. It has been inspired especially for New Zealand history students of all ages, travellers and aficionados.
"This is the first major report from the Artificial Intelligence and the Law Project. The overall focus of the report is on the regulatory issues surrounding uses of artificial intelligence (AI) in New Zealand. There are many types of AI systems, and many spheres within which AI systems are used (in New Zealand and beyond). Phase 1 of the project focuses on regulatory issues surrounding the use of predictive AI models in New Zealand government departments. As discussed in the report, while there are many types of AI model, the concept of a “predictive model” picks out a reasonably well-defined class of models that share certain commonalities and are fairly well characterisable as a regulatory target. The report specifically focuses on the use of predictive models in the public sector because the researchers want to begin by discussing regulatory options in a sphere where the New Zealand Government can readily take action. New Zealand’s Government can relatively easily effect changes in the way its own departments and public institutions operate. The report identifies and discusses a number of primary concerns: Accuracy, Human control, Transparency and a right to reasons/explanations, Bias, fairness and discrimination, Privacy. Individual rights are vital for any democracy but exclusive reliance should not be placed on individual rights models that depend on affected parties holding predictive algorithms to account. Often, individuals will lack the resources to do so. Furthermore, individual rights models might offer limited efficacy in monitoring group harms. With regard to oversight and regulation, one of the key recommendations of the report is that Government should consider the establishment of a regulatory/oversight agency. Several possible models for the new regulatory agency are proposed in the report. The new regulator could serve a range of other functions, including: Producing best practice guidelines; Maintaining a register of algorithms used in government; Producing an annual public report on such uses; Conducting ongoing monitoring on the effects of these tools. The report indicates preference for a relatively “hard-edged” regulatory agency, with the authority to demand information and answers, and to deny permission for certain proposals. However, even a light-touch regulatory agency could serve an important function. The researchers stress the need for consultation with a wide range of stakeholders across New Zealand society, especially with populations likely to be affected by algorithmic decisions, and with those likely to be under-represented in construction and training. This is likely to include those in lower socio-economic classes, and Māori and Pacific Island populations. Quite simply, they are likely to have insights, concerns and perspectives that will not be available to even the most well-intentioned of outside observers."--Publisher's website.
This thoroughly revised second edition Handbook examines the latest knowledge and perspectives on digital politics. Leading scholars explore the expansion of digital technologies, channels and styles as it shapes political dynamics.
This study critically examines inequality within New Zealand's indigenous Māori population. Specifically it asks whether strong ties to Māori identity incur higher socio-economic costs. Historical expository analysis is undertaken in concert with statistical analyses of data from the New Zealand Census of Population and Dwellings (1996, 2001, 2006), and a longitudinal study of Māori households. I find strong evidence of ethnic and socio-economic segmentation within the Māori population. In each census, individuals identified exclusively as Māori by ethnicity are the most disadvantaged across a wide range of socio-economic indicators. Those identified as Māori solely by ancestry are the least disadvantaged. Pronounced differences in Māori language ability and intra-Māori partnering are also evident, indicating that the association between Māori identification and disadvantage may be partially explained by ties to Māori identity. Regression analyses of multi-wave survey data reveal a complex set of relationships. Changing patterns of identification suggest self-designation as a Māori is best conceived as a fluid, contingent process rather than a stable, individual trait. Māori identification is generally a less salient predictor of disadvantage than specific ties to Māori identity, expressed through network ties, language, and practices. However, while some ties to Māori identity appear to incur high socio-economic costs, other ties are inconsequential, or advantageous. Taken together, the analyses contribute new insights into patterns of inequality between Māori, and highlight the need for more careful theorizing and interpretation of ethnicity variables in empirical analysis.
In Australia and New Zealand, many public projects, programs and services perform well. But these cases are consistently underexposed and understudied. We cannot properly ‘see’—let alone recognise and explain—variations in government performance when media, political and academic discourses are saturated with accounts of their shortcomings and failures, but are next to silent on their achievements. Successful Public Policy: Lessons from Australia and New Zealand helps to turn that tide. It aims to reset the agenda for teaching, research and dialogue on public policy performance. This is done through a series of close-up, in-depth and carefully chosen case study accounts of the genesis and evolution of stand-out public policy achievements, across a range of sectors within Australia and New Zealand. Through these accounts, written by experts from both countries, we engage with the conceptual, methodological and theoretical challenges that have plagued extant research seeking to evaluate, explain and design successful public policy. Studies of public policy successes are rare—not just in Australia and New Zealand, but the world over. This book is embedded in a broader project exploring policy successes globally; its companion volume, Great Policy Successes (edited by Paul ‘t Hart and Mallory Compton), is published by Oxford University Press (2019).
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
