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ABSTRACT
The rapid developments in Artificial Intelligence (AI) and the intensification in the adoption of AI in domains such as autonomous vehicles, lethal weapon systems, robotics and alike pose serious challenges to governments as they must manage the scale and speed of socio-technical transitions occurring. While there is considerable literature emerging on various aspects of AI, governance of AI is a significantly underdeveloped area. The new applications of AI offer opportunities for increasing economic efficiency and quality of life, but they also generate unexpected and unintended consequences and pose new forms of risks that need to be addressed. To enhance the benefits from AI while minimising the adverse risks, governments worldwide need to understand better the scope and depth of the risks posed and develop regulatory and governance processes and structures to address these challenges. This introductory article unpacks AI and describes why the Governance of AI should be gaining far more attention given the myriad of challenges it presents. It then summarises the special issue articles and highlights their key contributions. This special issue introduces the multifaceted challenges of governance of AI, including emerging governance approaches to AI, policy capacity building, exploring legal and regulatory challenges of AI and Robotics, and outstanding issues and gaps that need attention. The special issue showcases the state-of-the-art in the governance of AI, aiming to enable researchers and practitioners to appreciate the challenges and complexities of AI governance and highlight future avenues for exploration.

How can we realize the grand dreams of Artificial Intelligence, without making our worst fears come true? Bart Verheij argues that we need to build machines that can participate in a constructive critical discussion, that tried-and-tested tool for good science, good politics and good family life. By developing such argumentation machines we can arrive at an artificial intelligence that provides good answers to our questions, has good reasons for its actions and makes good choices. In this text (presented in the original Dutch and in English translation), Bart Verheij leads us along the right mathematical foundations, Hitchcock's film `To Catch A Thief' and different traditions of legal reasoning. Bart Verheij predicts that by 2025 argumentation systems will have finally closed the long-standing gap between knowledge-based and data-driven artificial intelligence. The text is the transcription of Bart Verheij's inaugural lecture, read upon accepting the chair of Artificial Intelligence and Argumentation at the University of Groningen. He currently is head of the department of Artificial Intelligence in the Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence and serves as the president of the International Assocation of Artificial Intelligence and Law. The lecture was delivered on September 12, 2017, in the Aula of the Academiegebouw of the University of Groningen (Broerstraat 5, Groningen).

Intro -- Preface -- Contents -- Artificial Intelligence in Sustainability Agricultures -- Optimization of Drip Irrigation Systems Using Artificial Intelligence Methods for Sustainable Agriculture and Environment -- 1 Introduction -- 2 Mathematical Model -- 3 Algorithm -- 4 Simulation -- 5 Conclusion -- References -- Artificial Intelligent System for Grape Leaf Diseases Classification -- 1 Introduction -- 2 Materials and Methods -- 2.1 K-Means Algorithm for Fragmentation -- 2.2 Multiclass Support Vector Machine Classifier -- 3 The Proposed Artificial Intelligent Based Grape Leaf Diseases -- 3.1 Dataset Characteristic -- 3.2 Image Processing Phase -- 3.3 Image Segmentation Phase -- 3.4 Feature Extraction Phase -- 3.5 Classification Phase -- 4 Results and Discussion -- 5 Conclusions -- References -- Robust Deep Transfer Models for Fruit and Vegetable Classification: A Step Towards a Sustainable Dietary -- 1 Introduction -- 2 Related Works -- 3 Dataset Characteristics -- 4 Proposed Methodology -- 4.1 Data Augmentation Techniques -- 5 Experimental Results -- 6 Conclusion and Future Works -- References -- The Role of Artificial Neuron Networks in Intelligent Agriculture (Case Study: Greenhouse) -- 1 Introduction -- 2 Overview of AI -- 3 Agriculture and Greenhouse -- 4 Intelligent Control Systems (SISO and MIMO) -- 4.1 Particular Aspects of Information Technology on Greenhouse Cultivation -- 4.2 Greenhouse Climate Control Techniques -- 5 Modern Optimization Techniques -- 5.1 Genetic Algorithms -- 5.2 Main Attractions of GAs -- 5.3 Strong and Weak Points of FL and Neural Networks -- 6 Fuzzy Identification -- 7 Conclusion -- References -- Artificial Intelligence in Smart Health Care -- Artificial Intelligence Based Multinational Corporate Model for EHR Interoperability on an E-Health Platform -- 1 Introduction.

AbstractThroughout our history, we, Homo sapiens, have used technologies to better satisfy ourneeds. The relation betweenneedsandtechnologyis so fundamental that the US National Research Council defines the distinguishing characteristic of technology as its goal "to make modifications in the world [in order] to meet human needs" [1]. Artificial intelligence (AI) is one of the most promising emerging technologies of our time. Similar to other technologies, AI is expected by many "to meet [human] needs". In this article, we reflect on the relationship betweenneedsand AI, and call for the realization ofneeds-awareAI systems. We argue that re-thinkingneedsfor,through,by, andwithAI can be a very useful means towards the development of realistic approaches for sustainableHuman-aware,Accountable,Lawful, andEthical (HALE) AI systems. We discuss some of the most critical gaps, barriers, enablers, and drivers of co-creating future AI-based sociotechnical systems in which [human]needsare well considered and met. Finally, we provide an overview of potential challenges and considerations that should be carefully taken into account; and call for joint, immediate, and interdisciplinary efforts and collaborations to start on the path toneeds-awareAI.

Preface: Leading edge of science and technology and be synchronized with The Times——artificial intelligence helps judicial reform and judicial modernization -- The Theory:Artificial intelligence and judicature ——the inevitable course to realize judicature modernization -- The Practice: Artificial intelligence helps judicial reform——reform of the litigation system centered on trial -- The Expectation: Opportunities and challenges of artificial intelligence ——constructing the future rule of law of artificial intelligence

We are in an important technological moment in history, where experts in academia, research institutes, and non-governmental organizations posit that developments in artificial intelligence (ai) will lead to widespread disruptions in the labour market. This article addresses this claim by asking if organized labour sees ai as an equally imminent threat. Moreover, it asks how labour is preparing to challenge the power of capital as employers leverage automation in an age of neoliberal precarity. Online materials published by unions affiliated with the Canadian Labour Congress are reviewed here through discursive analysis. Our findings indicate that while no union has expressed opposition to technological change, many have questioned how employers leverage it in the workplace and its wider geopolitical and societal effects that affect their members and communities. We find that discussion around technological change emphasizes that technology makes work better and safer in a human-centred work environment. Overall, organized labour in Canada is attentive to issues within the political-economic context of automation, precarious work, community impacts, the role of government and regulation, skills and retraining, and job loss, among others. Given the view of technology held by organized labour, we challenge perspectives of both techno-pessimism and techno-optimism and highlight instead that labour unions are in a unique position to both respond and adapt to the evolution of work. Expanded strategic interventions around automation are needed to combat precarious work and the erosion of working conditions at present and in the coming decade(s), and we point to some notable efforts that are underway.

This book analyses the implications of the technical, legal, ethical and privacy challenges as well as challenges for human rights and civil liberties regarding Artificial Intelligence (AI) and National Security. It also offers solutions that can be adopted to mitigate or eradicate these challenges wherever possible. As a general-purpose, dual-use technology, AI can be deployed for both good and evil. The use of AI is increasingly becoming of paramount importance to the governments mission to keep their nations safe. However, the design, development and use of AI for national security poses a wide range of legal, ethical, moral and privacy challenges. This book explores national security uses for Artificial Intelligence (AI) in Western Democracies and its malicious use. This book also investigates the legal, political, ethical, moral, privacy and human rights implications of the national security uses of AI in the aforementioned democracies. It illustrates how AI for national security purposes could threaten most individual fundamental rights, and how the use of AI in digital policing could undermine user human rights and privacy. In relation to its examination of the adversarial uses of AI, this book discusses how certain countries utilize AI to launch disinformation attacks by automating the creation of false or misleading information to subvert public discourse. With regards to the potential of AI for national security purposes, this book investigates how AI could be utilized in content moderation to counter violent extremism on social media platforms. It also discusses the current practices in using AI in managing Big Data Analytics demands. This book provides a reference point for researchers and advanced-level students studying or working in the fields of Cyber Security, Artificial Intelligence, Social Sciences, Network Security as well as Law and Criminology. Professionals working within these related fields and law enforcement employees will also find this book valuable as a reference.

Intro -- Foreword -- Preface -- Contents -- About the Editors -- Chapter 1: Introduction to Artificial Intelligence in COVID-19 -- Pandemics -- History of Pandemics -- The COVID-19 Pandemic -- Origins of the COVID-19 Pandemic -- Continuous Fight for Science and Reason -- Modern Tools for Pandemic Control -- A Brief Chronology of the Chapters of This Book -- Power of Science -- References -- Chapter 2: AI for Pooled Testing of COVID-19 Samples -- Introduction -- System Model -- The PCR Process -- Mathematical Model -- Pooled COVID-19 Tests -- Recovery from Pooled Tests -- Group Testing Methods for COVID-19 -- Adaptive GT Methods -- Non-Adaptive GT Methods -- Pooling Matrix -- Noiseless Linear Non-Adaptive Recovery -- Noisy Non-Linear Non-Adaptive Recovery -- Summary -- Compressed Sensing for Pooled Testing for COVID-19 -- Compressed Sensing Forward Model for Pooled RT-PCR -- CS Algorithms for Recovery -- Details of Algorithms -- Assessment of Algorithm Performance and Experimental Protocols -- Choice of Pooling Matrices -- Choice of Number of Pools -- Use of Side Information in Pooled Inference -- Comparative Discussion and Summary -- References -- Chapter 3: AI for Drug Repurposing in the Pandemic Response -- Introduction -- Desirable Features of AI for Drug Repurposing in Pandemic Response -- Technical Flexibility and Efficiency -- Clinical Applicability and Acceptability -- Major AI Applications for Drug Repurposing in Response to COVID-19 -- Knowledge Mining -- Network-Based Analysis -- In Silico Modelling -- IDentif.AI Platform for Rapid Identification of Drug Combinations -- Project IDentif.AI -- IDentif.AI for Drug Optimization Against SARS-CoV-2 -- IDentif.AI 2.0 Platform in an Evolving Pandemic -- IDentif.AI as a Pandemic Preparedness Platform -- Use of Real-World Data to Identify Potential Targets for Drug Repurposing.

Artificial Intelligence (AI), also referred to as the new electricity, is the emerging focus area in India. AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making. Most of the AI systems rely on historical large datasets for predicting future trends and outcomes at a pace which humans would not be able to match. The development of AI in India is in the initial stages and there is no regulatory body focused solely on AI. However, recently, Government of India has taken various initiatives related to AI such as establishment of Artificial Intelligence Task Force, formulation of NITI Aayog's National Strategy for Artificial Intelligence #AIFORALL, setting up of four Committees for AI under Ministry of Electronics and Information technology etc. Some of India's state governments have also taken few initiatives, such as establishment of Centre of Excellence for Data Science and Artificial Intelligence (CoE-DS&AI) by Karnataka, Safe and Ethical Artificial Intelligence Policy 2020 and Face Recognition Attendance System by Tamil Nadu, AI-Powered System for monitoring driving behaviour by West Bengal, AI System to fight agricultural risks by Maharashtra etc. As with any other technology, AI brings with it a span of opportunities and challenges. In healthcare, AI could be beneficial in mining medical records; designing treatment plans; forecasting health events; assisting repetitive jobs; doing online consultations; assisting in clinical decision making; medication management; drug creation; making healthier choices and decisions; and solving public health problems etc. AI could be very helpful in areas where there is scarcity of human resources, such as rural and remote areas. AI technology has been helpful in dealing with COVID-19 in India. It has helped in preliminary screening of COVID-19 cases, containment of coronavirus, contact tracing, enforcing quarantine and social distancing, tracking of suspects, tracking the pandemic, treatment and remote monitoring of COVID-19 patients, vaccine and drug development etc. The path for adoption of AI driven healthcare in India is filled with a lot of challenges. The unstructured data sets, interoperability issues, lack of open sets of medical data, inadequate analytics solutions which could work with big data, limited funds, inadequate infrastructure, lack of manpower skilled in AI, regulatory weaknesses, inadequate framework and issues related to data protection are some of the key challenges for AI-driven healthcare. It is recommended that government should support companies to invest in AI; encourage public private partnerships in the domain of AI and Health; enact and effectively enforce laws and legislation related to AI and Health; frame policies addressing issues related to confidentiality and privacy in the AI-driven healthcare; and establish a certification system for AI-based healthcare solutions. To adopt AI-based healthcare, it is important to train workforce in AI so that they can carefully handle sensitive health information, protect data against theft and use AI systems effectively. It is also crucial that healthcare decisions based on AI solutions should have a rationale and are explainable.