arXiv subjects

Scott McLachlan

Publications and source records attributed to Scott McLachlan.

14 recordsLinked to original sources

arXiv2024

Explainable AI: Definition and attributes of a good explanation for health AI

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI.

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arXiv2022

The Chaotic State of UK Drone Regulation

In December 2020 the law for drone pilots and unmanned aerial vehicle (UAV) use went into a transition phase in preparation for new EU international UAV regulation. That EU regulation comes into full effect as the transition periods defined in the United Kingdom's Civil Aviation Authority Air Policy CAP722 expire during December 2022 (CAA, 2020). However, international homologation regulation will not address the patchwork of inconsistent drone use regulations that exist in the United Kingdom from the layering of local and subordinate authority byelaws over UK aviation law. We provide an extensive review of local authority regulation of drone use on public open and green spaces, finding that many local authorities are unaware of the issues being created through: (i) inappropriately couched or poorly framed byelaws; (ii) multiple byelaws covering the same area by virtue of overlapping jurisdictions; or (iii) the lack readily identifiable policies for drone use on public land. Overregulation, inconsistent regulation and regulatory disharmony are causing confusion for recreational drone enthusiasts such that it is never clear which public or crown-owned open and green spaces they are allowed to, or prohibited from, flying. While the government and local authorities might like them to, drones are not going away. Therefore, we conclude, the easiest way to ensure citizens stay within the bounds of drone law that is intended to ensure public safety, is to make that law comprehensible, consistent and easy to comply with.

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arXiv2022

How might Driver Licensing and Vehicle Registration evolve if we adopt Autonomous Cars and Digital Identification?

It has been said that fewer or greater numbers of driver's licenses may be issued in future. However, when we extrapolate based on comparisons to other transport domains that have high levels of automation like aviation, the future for driver's licenses may not be so up in the air. Introduction of autonomous systems in aviation frequently leads to pilot retraining, and while modern commercial jets are generally sufficiently capable to take off, fly to their destination and land with little in the way of human intervention, pilots are still required and every pilot must hold a current license with ratings for the types of plane they command. Contemporary in-service training for commercial pilots has shifted focus from extended practicing of general flight skills like navigation and radio use. Pilots now spend many hours each year in simulators training to building knowledge, experience and thus muscle memory for how to respond when the airplane's extensive interconnected collection of semi- and fully-autonomous fly-by-wire systems go awry. While the plane itself is actually flying for much of the time during most flights today, the pilot's license still attests to their ability to assume full control and complete the flight where it becomes necessary. We contend a similar future may exist for driver training and licensure.

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arXiv2022

The Self-Driving Car: Crossroads at the Bleeding Edge of Artificial Intelligence and Law

Artificial intelligence (AI) features are increasingly being embedded in cars and are central to the operation of self-driving cars (SDC). There is little or no effort expended towards understanding and assessing the broad legal and regulatory impact of the decisions made by AI in cars. A comprehensive literature review was conducted to determine the perceived barriers, benefits and facilitating factors of SDC in order to help us understand the suitability and limitations of existing and proposed law and regulation. (1) existing and proposed laws are largely based on claimed benefits of SDV that are still mostly speculative and untested; (2) while publicly presented as issues of assigning blame and identifying who pays where the SDC is involved in an accident, the barriers broadly intersect with almost every area of society, laws and regulations; and (3) new law and regulation are most frequently identified as the primary factor for enabling SDC. Research on assessing the impact of AI in SDC needs to be broadened beyond negligence and liability to encompass barriers, benefits and facilitating factors identified in this paper. Results of this paper are significant in that they point to the need for deeper comprehension of the broad impact of all existing law and regulations on the introduction of SDC technology, with a focus on identifying only those areas truly requiring ongoing legislative attention.

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arXiv2021

Smart Automotive Technology Adherence to the Law: (De)Constructing Road Rules for Autonomous System Development, Verification and Safety

Driving is an intuitive task that requires skills, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users, including wild animals. These requirements are particularly important when approaching intersections, overtaking, giving way, merging, turning and while adhering to the vast body of road rules. Modern motor vehicles now include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. The UK Department of Transport's response to the Safe Use of Automated Lane Keeping System consultation proposes that these systems are tested for compliance with relevant traffic rules. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer's in-depth knowledge of traffic legislation as well. These skills are required to ensure the systems are able to safely perform their tasks while being observant of the law. This paper presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. The approach (de)constructs road rules in legal terminology and specifies them in structured English logic that is expressed as Boolean logic for automation and Lawmaps for visualisation. We demonstrate an example using these tools leading to the construction and validation of a Bayesian Network model. We strongly believe these tools to be approachable by programmers and the general public, and capable of use in developing Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.

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arXiv2020

Visualisation of Law and Legal Process: An Opportunity Missed

Visual representation of the law and legal process can aid in recall and discussion of complicated legal concepts, yet is a skill rarely taught in law schools. This work investigates the use of flowcharts and similar process-oriented diagrams in contemporary legal literature through a literature review and concept-based content analysis. Information visualisations (infovis) identified in the literature are classified into eleven described archetypal diagram types, and the results describe their usage quantitatively by type, year, publication venue and legal domain. We found that the use of infovis in legal literature is extremely rare, identifying not more than ten articles in each calendar year. We also identified that the concept flow diagram is most commonly used, and that Unified Modelling Language (UML) is the most frequently applied representational approach. This work posits a number of serious questions for legal educators and practicing lawyers regarding how infovis in legal education and practice may improve access to justice, legal education and lay comprehension of complex legal frameworks and processes. It concludes by asking how we can expect communities to understand and adhere to laws that have become so complex and verbose as to be incomprehensible even to many of those who are learned in the law?

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arXiv2020

Lawmaps: Enabling Legal AI development through Visualisation of the Implicit Structure of Legislation and Lawyerly Process

Modelling that exploits visual elements and information visualisation are important areas that have contributed immensely to understanding and the computerisation advancements in many domains and yet remain unexplored for the benefit of the law and legal practice. This paper investigates the challenge of modelling and expressing structures and processes in legislation and the law by using visual modelling and information visualisation (InfoVis) to assist accessibility of legal knowledge, practice and knowledge formalisation as a basis for legal AI. The paper uses a subset of the well-defined Unified Modelling Language (UML) to visually express the structure and process of the legislation and the law to create visual flow diagrams called lawmaps, which form the basis of further formalisation. A lawmap development methodology is presented and evaluated by creating a set of lawmaps for the practice of conveyancing and the Landlords and Tenants Act 1954 of the United Kingdom. This paper is the first of a new breed of preliminary solutions capable of application across all aspects, from legislation to practice; and capable of accelerating development of legal AI.

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arXiv2020

Medical idioms for clinical Bayesian network development

Bayesian Networks (BNs) are graphical probabilistic models that have proven popular in medical applications. While numerous medical BNs have been published, most are presented fait accompli without explanation of how the network structure was developed or justification of why it represents the correct structure for the given medical application. This means that the process of building medical BNs from experts is typically ad hoc and offers little opportunity for methodological improvement. This paper proposes generally applicable and reusable medical reasoning patterns to aid those developing medical BNs. The proposed method complements and extends the idiom-based approach introduced by Neil, Fenton, and Nielsen in 2000. We propose instances of their generic idioms that are specific to medical BNs. We refer to the proposed medical reasoning patterns as medical idioms. In addition, we extend the use of idioms to represent interventional and counterfactual reasoning. We believe that the proposed medical idioms are logical reasoning patterns that can be combined, reused and applied generically to help develop medical BNs. All proposed medical idioms have been illustrated using medical examples on coronary artery disease. The method has also been applied to other ongoing BNs being developed with medical experts. Finally, we show that applying the proposed medical idioms to published BN models results in models with a clearer structure.

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arXiv2020

Bluetooth Smartphone Apps: Are they the most private and effective solution for COVID-19 contact tracing?

Many digital solutions mainly involving Bluetooth technology are being proposed for Contact Tracing Apps (CTA) to reduce the spread of COVID-19. Concerns have been raised regarding privacy, consent, uptake required in a given population, and the degree to which use of CTAs can impact individual behaviours. However, very few groups have taken a holistic approach and presented a combined solution. None has presented their CTA in such a way as to ensure that even the most suggestible member of our community does not become complacent and assume that CTA operates as an invisible shield, making us and our families impenetrable or immune to the disease. We propose to build on some of the digital solutions already under development that, with addition of a Bayesian model that predicts likelihood for infection supplemented by traditional symptom and contact tracing, that can enable us to reach 90% of a population. When combined with an effective communication strategy and social distancing, we believe solutions like the one proposed here can have a very beneficial effect on containing the spread of this pandemic.

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arXiv2020

Predicted by Orwell: A discourse on the gradual shift in electronic surveillance law

At some point in the history of most nations, one or more events of illegal electronic surveillance by those in power or law enforcement has occurred that has the effect of setting State against Citizen. The media sensationalise these incidents for profit, however they more often correctly express the concern felt by the general public. At these times politicians rise, either into fame or infamy, by proposing new legislation which the public is told will protect them by from future incidents of illegal and unwarranted invasion by officers of the state. Two things have occurred since these protective laws were enacted; technological advancement that is claimed has frustrated legitimate investigation, and changes in the law that are ostensibly presented as intending to facilitate the prosecution or prevention of a publicly decried offence, like child pornography, but which in context deliver expanded powers to the State, effectively weakening the protections previously enacted. This report looks at human rights legislation in three jurisdictions, starting from a position of comparing and contrasting the protections that are available from illegal search and seizure. By identifying legislative changes related to several forms of electronic surveillance and technology, and the situations that led to them, we can locate the effective peak of protection and discuss the processes that have led to a gradual yet pervasive weakening of those laws in all three nations. We are regularly diverted by those in power towards disregarding the paranoia of the outliers who have been warning us with their purple prose that big brother is watching. But if we focus on the effect of recent legislative changes in the area of electronic surveillance we can clearly see that the Orwellian dystopia is already here, and we are living it.

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arXiv2020

Health Information Standardisation as a basis for Learning Health Systems

Standardisation of healthcare has been the focus of hospital management and clinicians since the 1990's. Electronic health records were already intended to provide clinicians with real-time access to clinical knowledge and care plans while also recording and storing vast amounts of patient data. It took more than three decades for electronic health records to start to become ubiquitous in all aspects of healthcare. Learning health systems are the next stage in health information systems whose potential benefits have been promoted for more than a decade - yet few are seen in clinical practice. Clinical care process specifications are a primary form of clinical documentation used in all aspects of healthcare, but they lack standardisation. This thesis contends that this lack of standardisation was inherited by electronic health records and that this is a significant issue holding back the development and adoption of learning health systems. Standardisation of clinical documents is used to mitigate issues in electronic health records as a basis for enabling learning health systems. One type of clinical document, the caremap, is standardised in order to achieve an effective approach to containing resources and ensuring consistency and quality. This led not only to improved clinicians' comprehension and acceptance of the clinical document, but also to reduced time expended in developing complicated learning health systems built using the input of clinical experts.

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arXiv2020

A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. The review shows that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exists in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption of BNs; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review empowers researchers and clinicians with an analytical framework and findings that will enable understanding of the need to address the problems of restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.

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arXiv2020

Bayesian Networks in Healthcare: Distribution by Medical Condition

Bayesian networks (BNs) have received increasing research attention that is not matched by adoption in practice and yet have potential to significantly benefit healthcare. Hitherto, research works have not investigated the types of medical conditions being modelled with BNs, nor whether any differences exist in how and why they are applied to different conditions. This research seeks to identify and quantify the range of medical conditions for which healthcare-related BN models have been proposed, and the differences in approach between the most common medical conditions to which they have been applied. We found that almost two-thirds of all healthcare BNs are focused on four conditions: cardiac, cancer, psychological and lung disorders. We believe that a lack of understanding regarding how BNs work and what they are capable of exists, and that it is only with greater understanding and promotion that we may ever realise the full potential of BNs to effect positive change in daily healthcare practice.

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arXiv2020

Public Authorities as Defendants: Using Bayesian Networks to determine the Likelihood of Success for Negligence claims in the wake of Oakden

Several countries are currently investigating issues of neglect, poor quality care and abuse in the aged care sector. In most cases it is the State who license and monitor aged care providers, which frequently introduces a serious conflict of interest because the State also operate many of the facilities where our most vulnerable peoples are cared for. Where issues are raised with the standard of care being provided, the State are seen by many as a deep-pockets defendant and become the target of high-value lawsuits. This paper draws on cases and circumstances from one jurisdiction based on the English legal tradition, Australia, and proposes a Bayesian solution capable of determining probability for success for citizen plaintiffs who bring negligence claims against a public authority defendant. Use of a Bayesian network trained on case audit data shows that even when the plaintiff case meets all requirements for a successful negligence litigation, success is not often assured. Only in around one-fifth of these cases does the plaintiff succeed against a public authority as defendant.

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