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IntroductionThere is widespread enthusiasm to improve health through the application of artificial intelligence and machine learning (AI/ML) methods to large population-level health datasets. Achieving this may require successful collaboration between institutions as well as between computer scientists (CS), machine learning researchers (MLR) and health service researchers (HSR). The objective is to describe lessons learned in creating the Health Artificial Data and Analysis Platform (HAIDAP) in Ontario, Canada.
Objectives and ApproachA partnership between a HSR institute (ICES), an AI/ML institute (Vector) and a high-performance computing center (HPC4H) was initiated in 2017 to enable the application of AI/ML methods to population-level health data for the province of Ontario (population 14M). The HAIDAP was launched in 2019. We describe lessons learned (and being learned) following the HAIDAP's launch.
ResultsMajor learnings include: 1)importance of institutional partnerships and alignment with institutional strategies; 2)potential of joint institutional risk-sharing models; 3)need for scientific collaborations bridging disciplines around joint research projects; 4)sensitivity to different scientific cultures (e.g., academic prestige of conference proceedings for MLR vs journal publications for HSR; traditional statistical vs. ML model assumptions); 5)differences in research timeline expectations; 6)different experience with and expectations for access to de-identified routinely collected data (e.g., need for research ethics committee project approvals and privacy impact assessments); 7)developing data access models that enable greater flexibility (e.g. importing code or using open source tools); 8) broadening data access models to allow modern high-dimensional exploratory data analysis; 9)obtaining support of information/privacy regulator; 10)hardware is (relatively) easy part compared to other success factors.
Conclusion / ImplicationsThe HAIDAP has enabled multi-disciplinary collaborations and novel AI/ML research of Ontario's population-level health data. Collectively we have learned that additional effort is required to develop systems and processes enabling more efficient access to data and analytic tools for the analysis of administrative health data.

This interdisciplinary article presents a concept of the 21st century and phenomena that are products of the 4th industrial revolution – big data and Artificial Intelligence technologies – as well as the opportunities of their application in public governance and social policy. This paper examines the advantages and disadvantages of big data, problems of data collection, its reliability and use. Big data can be used for the analysis and modeling of phenomena relevant to public governance and social policy. Big data consist of three main types: a) historical data, b) present data with little delay, c) prognostic data for future forecasting. The following categories of big data can be defined as: a) data from social networks, b) traditional data from business systems, c) machine-generated data, such as water extraction, pollution, satellite information. The article analyzes the advantages and disadvantages of big data. There are big data challenges such as data security, lack of cooperation in civil service and social work, in rare situations – data fragmentation, incompleteness and erroneous issues, as well as ethical issues regarding the analysis of data and its use in social policy and social administration. Big data, covered by Artificial Intelligence, can be used in public governance and social policy by identifying "the hot spots" of various phenomena, by prognosing the meanings of variables in the future on the basis of past time rows, and by calculating the optimal motion of actions in the situations where there are possible various alternatives. The technologies of Artificial Intelligence are used more profoundly in many spheres of public policy, and in the governance of COVID-19 pandemics too. The substantial advantages of the provided big data and Artificial Intelligence are a holistic improvement of public services, possibilities of personalization, the enhancement of citizen satisfaction, the diminishing of the costs of processing expenditure, the targeting of adopted and implemented decisions, more active involvement of citizens, the feedback of the preferences of policy formation and implementation, the observation of social phenomenas in real time, and possibilities for more detailed prognosing. Challenges to security of data, necessary resources and competences, the lack of cooperation in public service, especially rare instances of data fragmentation, roughness, falseness, and ethical questions regarding data analysis and application can be evaluated as the most significant problems of using big data and Artificial Intelligence technologies. Big data and their analytics conducted using Artificial Intelligence technologies can contribute to the adequacy and objectivity of decisions in public governance and social policy, effectively curbing corruption and nepotism by raising the authority and confidence of public sector organizations in governance, which is so lacking in the modern world. ; Šiame tarpdisciplininiame straipsnyje pateikiama XXI amžiaus ketvirtosios pramonės revoliucijos fenomenų – didžiųjų duomenų ir dirbtinio intelekto technologijų – samprata ir aptariamos jų naudojimo viešojo valdymo srityje ir socialinėje politikoje galimybės, nagrinėjami didžiųjų duomenų pranašumai ir trūkumai, jų rinkimo, patikimumo ir naudojimo problemos. Didieji duomenys gali būti naudojami su viešuoju valdymu ir socialine politika susijusių reiškinių analizei ir jiems modeliuoti. Didieji duomenys apima tris duomenų tipus: a) istorinius duomenis, b) dabarties duomenis su mažu pavėlavimu, c) prognostinius duomenis ateičiai prognozuoti. Galima apibrėžti šias didžiųjų duomenų kategorijas: a) duomenis iš socialinių tinklų, b) valdymo sistemų duomenis, c)mašinų generuojamus duomenis, pavyzdžiui, vandens gavybos, užterštumo, palydovų informaciją. Straipsnyje yra analizuojami didžiųjų duomenų pranašumai ir trūkumai. Galimi tokie didžiųjų duomenų iššūkiai, kaip antai: duomenų saugumas, bendradarbiavimo stoka valstybės tarnyboje, labai retai nutinkančios situacijos, duomenų fragmentacija, nebaigtumas ir klaidingumas, etiniai duomenų analizės ir naudojimo viešojo valdymo srityje ir socialinėje politikoje klausimai. Didieji duomenys ir jų analizė naudojant dirbtinio intelekto technologijas gali prisidėti prie viešojo valdymo ir socialinės politikos sprendimų adekvatumo ir objektyvumo,veiksmingai pažaboti korupciją ir nepotizmą didinant viešojosektoriaus organizacijų autoritetą ir pasitikėjimą valdžia, o jo šiuolaikiniame pasaulyje taip trūksta.

Often justified in the name of religion, Islamist violence against women in Iraq is a prevalent and omnipresent concern. This thesis explores the escalating issues with regard to both physical and psychological or legally-sanctioned violence against women in Iraq, exploring the reasons for its occurrence and working to deconstruct its religious rationalizations. The thesis also offers an analysis of the Islamic sources -- the Qur'an, Sunna, and hadith -- that relies on strategies such as linguistic and historical contextualization to demonstrate that Islam's intended view of women was actually quite progressive. Many more conservative Islamic interpretations or arguments in favor of female subservience are reliant on carefully chosen Qur'anic verses for legitimacy. Devoid of context and a thorough explanation of each Arabic term's array of potential meanings, these arguments fail to adequately portray Islam's progressive potential with regard to gender relations. Ultimately, the ensuing work seeks to make the policy recommendation that, at least with respect to Iraqi women, the key to liberation in the post-Saddam era is not through secular strategies or solutions, but instead, through non-secular arguments and interpretations, indeed through Islam itself.

"H.A.S.C. no. 111-180." ; Shipping list no.: 2011-0160-P. ; Mode of access: Internet.

In AI and the Future of the Public Sector: The Creation of Public Sector 4.0, renowned executive and consultant Tony Boobier delivers a comprehensive reference of the most relevant and central issues regarding the adoption and implementation of AI in the public sector. In the book, you'll find out why data and analytics are the solution to significant and ongoing problems in the public service relating to its ability to effectively provide services in an environment of reduced funding. You'll also discover the likely impact of future technological developments, like 5G and quantum computing, as well as explore the future of healthcare and the effective digitalization of the healthcare industry. The book also offers: discussions of policing 4.0 and how data and analytics will transform public safety; explorations of the future of education and how AI can dramatically enhance educational standards while reducing costs; and treatments of the internationalization of public services and its impact on agencies and departments everywhere. A can't-miss resource for public sector employees at the managerial and professional levels, AI and the Future of the Public Sector is an insightful and timely blueprint to the effective use of artificial intelligence that belongs in the bookshelves of policy makers, academics, and public servants around the world.

Makela talks about Arab Spring's impact on Egypt's securitocracy. Security was lacking at the street level and beyond. Once the Arab world's most powerful country, Egypt's economy has since been shattered, and its security situation badly deteriorated. Securitocracy refers to a system of security elites (intelligence and security services, military and police forces), that, at the executive level, use either direct or indirect political power and influence in matters related to a state's foreign and security policy, internal security, and even in the finance and economic sectors. Adapted from the source document.

During the nation's first years of existence, Pres George Washington sought funding for secret services that he felt were essential to US security. The allocations for those spying activities consumed roughly 10% of the US budget -- a grand sum measuring roughly $1 million. In 2014, the reported budget for US intelligence activities was approximately $68 billion, down from its 2010 high of $80 billion. Despite its deep roots and the resources that have been poured into it, today's intelligence community stands at a watershed. The most modern information systems are designed to serve consumers of information. The intelligence apparatus is perversely oriented to the needs and concerns of information producers. It is centralized when most systems are distributed, and it is hard to search when most focus on making that easy. The intelligence community needs fewer agencies, fewer bosses, less redundancy, lower costs, and more orientation toward focusing on the right missions in the right way. Adapted from the source document.