If one posed a question on how ready is the Republic of Croatia, for use of artificial intelligence in legal and legislative terms in medicine, the answer at this point would be – it is not. The fact is that processes in medicine involve the application of state-of-the-art technologies, as is artificial intelligence, but it is also a controversial fact that the health legislative system does not develop seemingly, in the direction in which it is assumed, according to standards that exist in other developed countries of Europe and the world. Our society awaits many challenges due to the use of ever more ubiquitous applications of artificial intelligence and state-of-the-art, sophisticated technologies and technological processes in treatment, which will need regulation through the prescribed cognitive legal norm. Regulation of the medical treatment process driven by artificial intelligence, must be in place through the norm because the area is too important to be left to technological progress without legal control and adequate legal regulation. In this regard, medicine and all treatment processes, diagnostics and therapies in the health care system must be carried out with one single clear goal, which is the protection and preservation of human health and life.
International audience ; BOX is an artwork that exposes some of the social and political impact of artificial intelligence, computer vision, and automation. The project uses a commercially available computer vision system that predicts the interactor's ethnicity, and locks or unlocks itself depending on this prediction. The artwork showcases a possible use of computer vision, making explicit the fact that every technological implantation crystallises a political worldview.
Artificial intelligence systems are currently deployed in many areas of human activity. Such systems are increasingly assigned tasks that involve taking decisions about people or predicting future behaviours. These decisions are commonly regarded as fairer and more objective than those taken by humans, as AI systems are thought to be resistant to such influences as emotions or subjective beliefs. In reality, using such a system does not guarantee either objectivity or fairness. This article describes the phenomenon of bias in AI systems and the role of humans in creating it. 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Artificial intelligence (AI)1, together with big data, is the driving force behind the ever-accelerating digital revolution. AI has what it takes to call into question our fundamental concepts and processes of political, social, economic etc. order (Macron, 2018; Zuboff, 2018), and the law will not be spared. Therefore, all societal actors (inter alia from politics, the economy, legal practice and academia) must take responsibility for the crucial twin tasks of determining the right, balanced relationship between AI and the law, and even to hybridise them. .
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).
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.
The article of record as published may be found at http://dx.doi.org/10.1109/MC.1985.1663007 ; The progress, goals and techniques being used in the Japanese fifth-generation computer program are assessed. The research is being performed in three phases: tool building, construction of parallel architecture machines, and evaluation and refinement. The first phase is well under way and has yielded designs for two prototype machines: a Personal Sequential Interface (PSI) workstation and the Delta machine (DM), a relational database machine. Kernel Language 0 (KL0), used for the PSI, is being expanded to KL1. The Mandala language is being applied in the DM. Applications have not received a great deal of attention at the government-funded research center, although the techniques developed are already being implemented in industry for machine and computer design and communications systems. 18 references.
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.
Artificial Intelligence (AI) is a branch of computer sciences that uses learning algorithms that calculate probability of outcome using Bayes theorem and other statistical methods given certain input. When the chance of an event occurring is calculated over and over again adding new data or evidence at each step, the probability can reach a level of near certainty for given outcome with certain inputs. Thousands, even millions of data points are incorporated in calculating posterior probability for predictive analytics. The analytics are input neutral as programs predict the future events irrespective of the source of the data. AI has, thus, blurred the boundaries between the physical, digital, and biological worlds. AI is a branch of computer Sciences. This initial learning process is considered training where inputs are given to the program already marked for the expected outcome. This training information can be highly precise or very vague allowing different degree of freedom to the program but also increasing burden of training. Once trained an AI algorithm is able to predict or analyze given input to suggest the required outcome with some certainty, that improves with continued training or feedback. Artificial Intelligence (AI) is ubiquitous, affecting virtually every aspect of life either directly or indirectly. We are aware of some of the Ai applications , others we are not. The term AI was first introduced by McCarthy at Dartmouth college, New Hampshire, in 1956. But recently, AI has become a commonly used modality in science, business, education, aeronautics, arts, defense and government affairs to name a few. In the last several years, there has been exponential growth in AI applications. In 2016, AI added $ 600 million to the global economy, this is projected to grow to $ 15 Trillion by the year 2030. As a comparison, the size of the US economy was 22 Trillion in 2021.
This paper discusses the argument that the adoption of artificial intelligence (AI) technologies benefits the powerful few, focussing on their own existential concerns. The paper will narrow down the analysis of the argument to jurisprudence (i.e. the philosophy of law), considering also the historical context. The paper will discuss the construction of the legal system through the lens of political involvement of what one may want to consider to be powerful elites. Before discussing these aspects the paper will clarify the notion of "powerful elites". In doing so the paper will be demonstrating that it is difficult to prove that the adoption of AI technologies is undertaken in a way which mainly serves a powerful class in society. Nevertheless, analysing the culture around AI technologies with regard to the nature of law with a philosophical and sociological focus demonstrates a utilitarian and authoritarian trend in the adoption of AI technologies. The paper will conclude by proposing an alternative, some might say practically unattainable, approach to the current legal system by looking into restorative justice for AI crimes, and how the ethics of care could be applied to AI technologies. ; peer-reviewed
Artificial intelligence (AI), particularly various methods of machine learning (ML), has achieved landmark advances over the past few years in applications as diverse as playing complex games, language processing, speech recognition and synthesis, image identification, and facial recognition. These breakthroughs have brought a surge of popular, journalistic, and policy attention to the field, including both excitement about anticipated advances and the benefits they promise, and concern about societal impacts and risks – potentially arising through whatever combination of accident, malicious or reckless use, or just social and political disruption from the scale and rapidity of change.
In this, OxCAIGG's second major research output of the year, author Thomas Vogl explores the use of artificial intelligence by UK local authorities. While there have been a number of successful projects related to back-office automation, predictive analytics for decision support, or the use of chatbots for interactions with resident, Vogl finds that governments face important practical challenges in successfully adopting AI. This briefing note identifies three key challenges and three key enablers that local authorities face when working with AI.
This paper discusses the argument that the adoption of artificial intelligence (AI) technologies benefits the powerful few, focussing on their own existential concerns. The paper will narrow down the analysis of the argument to jurisprudence (i.e. the philosophy of law), considering also the historical context. We will discuss the construction of the legal system through the lens of political involvement of what one may want to consider to be powerful elites. Before discussing these aspects we will clarify our notion of "powerful elites". In doing so we will be demonstrating that it is difficult to prove that the adoption of AI technologies is undertaken in a way which mainly serves a powerful class in society. Nevertheless, analysing the culture around AI technologies with regard to the nature of law with a philosophical and sociological focus enables us to demonstrate a utilitarian and authoritarian trend in the adoption of AI technologies. The paper will conclude by proposing an alternative, some might say practically unattainable, approach to the current legal system by looking into restorative justice for AI crimes, and how the ethics of care could be applied to AI technologies. ; peer-reviewed
This article studies the security politics of Artificial Intelligence (AI) in China. Using securitization as an analytical framework, it examines the official Chinese AI discourse and how AI is becoming a security matter. The article argues that the Chinese central government is securitizing AI to mobilize local states, market actors, intellectuals, and the general public. China's historical anxieties about its technology and regime security needs are conducive to the rise of a security discourse in China's AI politics, a trend also fuelled by tensions arising from great power competition. Although helpful in convincing domestic actors, this securitization trend could undermine Chinese key AI objectives by heading in an inward-looking, techno-nationalistic direction that may be seriously detrimental to China's AI industry and leadership ambitions.
Artificial Intelligence has grown throughout recent years to become a major part of popular culture and products used by people around the world. However, these systems are not perfect and can in fact contain multiple different biases in their underlying algorithms. In this paper, we provide an overview of the sources of algorithmic bias, a discussion of real-world case studies and their impacts, and a general summary of past attempts to address biases in artificial intelligence such as the General Data Protection Regulation (GDPR), corporate and governmental ethical guidelines, and New York City's Automated Decision System (ADS) Task Force. Specifically, we discuss the COMPAS algorithm used for pretrial assessments, the Facebook ad-delivery algorithm used on its online advertising platform, and a healthcare algorithm used for high-risk care management in the United States. We conclude that algorithmic bias will only be exacerbated as more systems become automated through artificial intelligence. However, recognizing and calling for the alleviation of biases in current systems as well as approaching the design of automated systems holistically have led to reduced biases. More empirical research is required to fully understand what ways algorithmic bias can consistently be reduced. ; Young, William D. ; Computer Sciences
