title: Enhancing regulatory compliance by using artificial intelligence text mining to identify penalty clauses in legislation

authors: Goltz, Nachshon (Sean)

subjects: Regulatory compliance

abstract: As regulatory compliance (or compliance governance) becomes ever more challenging, attempts to engage IT solutions and especially artificial intelligence (AI) are on the rise. This paper suggest that regulatory compliance can be enhanced by employing an AI model trained to identify penalty clauses in the regulations. The paper provides the theoretical basis of machine learning for text classification and presents a two stage experiment of (1) training multiple models and selecting the best one; and (2) employing a sliding window detection in order to identify penalty clauses in regulation. Results benchmarked using an algorithm based penalties API suggests further development is needed.

text: Enhancing regulatory compliance by using artificial intelligence text mining to identify penalty clauses in legislation As regulatory compliance (or compliance governance) becomes ever more challenging, attempts to engage IT solutions and especially artificial intelligence (AI) are on the rise. This paper suggest that regulatory compliance can be enhanced by employing an AI model trained to identify penalty clauses in the regulations. The paper provides the theoretical basis of machine learning for text classification and presents a two stage experiment of (1) training multiple models and selecting the best one; and (2) employing a sliding window detection in order to identify penalty clauses in regulation. Results benchmarked using an algorithm based penalties API suggests further development is needed.

year: 2017

title: Revisiting deep hybrid models for out-of-distribution detection

authors: Schlumbom, Paul-Ruben

subjects: computer science

abstract: Deep hybrid models (DHMs) for out-of-distribution (OOD) detection, jointly training a deep feature extractor with a classification head and a density estimation head based on a normalising flow, provide a conceptually appealing approach to visual OOD detection. The paper that introduced this approach reported 100% AuROC in experiments on two standard benchmarks, including one based on the CIFAR-10 data. As there are no implementations available, we set out to reproduce the approach by carefully filling in gaps in the description of the algorithm. Although we were unable to attain 100% OOD detection rates, and our results indicate that such performance is impossible on the CIFAR-10 benchmark, we achieved good OOD performance. We provide a detailed analysis of when the architecture fails and argue that it introduces an adversarial relationship between the classification component and the density estimator, rendering it highly sensitive to the balance of these two components and yielding a collapsed feature space without careful fine-tuning. Our implementation of DHMs is publicly available.

text: Revisiting deep hybrid models for out-of-distribution detection Deep hybrid models (DHMs) for out-of-distribution (OOD) detection, jointly training a deep feature extractor with a classification head and a density estimation head based on a normalising flow, provide a conceptually appealing approach to visual OOD detection. The paper that introduced this approach reported 100% AuROC in experiments on two standard benchmarks, including one based on the CIFAR-10 data. As there are no implementations available, we set out to reproduce the approach by carefully filling in gaps in the description of the algorithm. Although we were unable to attain 100% OOD detection rates, and our results indicate that such performance is impossible on the CIFAR-10 benchmark, we achieved good OOD performance. We provide a detailed analysis of when the architecture fails and argue that it introduces an adversarial relationship between the classification component and the density estimator, rendering it highly sensitive to the balance of these two components and yielding a collapsed feature space without careful fine-tuning. Our implementation of DHMs is publicly available.

year: 2025

title: Stemming the colonial environmental tide - Shared Māori governance jurisdiction and ecosystem-based management over the marine and coastal seascape in Aotearoa New Zealand – Possible ways forward

authors: Joseph, Robert

subjects: ecosystem-based management, Maori co-governance, Treaty of Waitangi, Ocean governance

abstract: Ecosystem-based management (EBM) has become an appropriate international response for addressing the alarming global environmental degradation. EBM is designed and executed as an adaptive, learning-based process that applies the following common international principles:

•	the connections and relationships within an ecosystem;
•	the cumulative impacts that affect marine welfare; 
•	focus on maintaining the natural structure and function of ecosystems and their productivity;
•	incorporate human use and values of ecosystems in managing the resources;
•	recognise that ecosystems are dynamic and constantly changing;
•	are based on a shared vision of all key participants; and
•	are based on scientific knowledge, adopted by continual learning and monitoring.

The New Zealand Sustainable Seas National Science Challenge agrees with the above EBM principles and has adopted them but has also adapted them to an Aotearoa New Zealand context that fundamentally acknowledges mātauranga and tikanga Māori law and shared co-governance and concurrent jurisdiction hence the following Aotearoa New Zealand EBM principles:
•	a co-governance and co-design structure that recognises the Māori constitutional relationship and mana whenua at all levels (whānau, hapū, iwi), together with the guiding principles of mauri, whakapapa, kaitiakitanga, mātauranga-a-iwi and mātauranga-a-hapū;
•	is place and time-specific, recognising/understanding the ecosystem as a whole in all its ecological complexities and connectedness and addressing cumulative and multiple stressors;
•	acknowledges humans as ecosystem components with multiple values;
•	views long-term sustainability as a fundamental value, in particular maintaining values and uses for future generations;
•	includes collaborative and participatory management throughout the whole process, considering all values and involving all interested parties from agencies and iwi to industries, whānau, hapū and local communities;
•	has clear goals and objectives based on knowledge; and
•	includes adaptive management, appropriate monitoring and acknowledgement of uncertainty.

This report focuses on analysing EBM through the incorporation of mātauranga and tikanga Māori and shared concurrent jurisdiction through Treaty of Waitangi partnerships over the marine and coastal environment.

text: Stemming the colonial environmental tide - Shared Māori governance jurisdiction and ecosystem-based management over the marine and coastal seascape in Aotearoa New Zealand – Possible ways forward Ecosystem-based management (EBM) has become an appropriate international response for addressing the alarming global environmental degradation. EBM is designed and executed as an adaptive, learning-based process that applies the following common international principles:

•	the connections and relationships within an ecosystem;
•	the cumulative impacts that affect marine welfare; 
•	focus on maintaining the natural structure and function of ecosystems and their productivity;
•	incorporate human use and values of ecosystems in managing the resources;
•	recognise that ecosystems are dynamic and constantly changing;
•	are based on a shared vision of all key participants; and
•	are based on scientific knowledge, adopted by continual learning and monitoring.

The New Zealand Sustainable Seas National Science Challenge agrees with the above EBM principles and has adopted them but has also adapted them to an Aotearoa New Zealand context that fundamentally acknowledges mātauranga and tikanga Māori law and shared co-governance and concurrent jurisdiction hence the following Aotearoa New Zealand EBM principles:
•	a co-governance and co-design structure that recognises the Māori constitutional relationship and mana whenua at all levels (whānau, hapū, iwi), together with the guiding principles of mauri, whakapapa, kaitiakitanga, mātauranga-a-iwi and mātauranga-a-hapū;
•	is place and time-specific, recognising/understanding the ecosystem as a whole in all its ecological complexities and connectedness and addressing cumulative and multiple stressors;
•	acknowledges humans as ecosystem components with multiple values;
•	views long-term sustainability as a fundamental value, in particular maintaining values and uses for future generations;
•	includes collaborative and participatory management throughout the whole process, considering all values and involving all interested parties from agencies and iwi to industries, whānau, hapū and local communities;
•	has clear goals and objectives based on knowledge; and
•	includes adaptive management, appropriate monitoring and acknowledgement of uncertainty.

This report focuses on analysing EBM through the incorporation of mātauranga and tikanga Māori and shared concurrent jurisdiction through Treaty of Waitangi partnerships over the marine and coastal environment.

year: 2020-06-09