Artificial Intelligence in Pakistan’s Cyberspace

Artificial Intelligence (AI) is beneficial in many respects, but also has harmful effects that constitute risks for individuals and society. Dealing with AI risks is a future-oriented endeavor that needs to be approached in a forward-looking way. Forward-looking responsibility is about who should do what to remedy or prevent harm. With the ongoing EU policy process on AI development as a point of departure, the purpose of this article is to discuss distribution of forward-looking responsibility for AI development with respect to what the obligations entail in terms of burdens or assets for the responsible agents and for the development of AI. The analysis builds on the documents produced in the course of the EU process, with a particular focus on the early role of the European Parliament, the work of the High-Level Expert Group on AI, and the Commission's proposal for a regulation of AI, and problematises effects of forward-looking responsibility for the agents who are attributed forward-looking responsibility and for the development of AI. Three issues were studied: ethics by design, Artificial General Intelligence (AGI), and competition. Overall, the analysis of the EU policy process on AI shows that competition is the primary value, and that the perspective is technical and focused on short-term concerns. As for ethics by design, the question of which values should be built into the technology and how this should be settled remained an issue after the distribution of responsibility to designers and other technical experts. AGI never really was an issue in this policy process, and it was gradually phased out. Competition within the EU process on AI is a norm that frames how responsibility is approached, and gives rise to potential value conflicts.

We distinguish artificial intelligence (AI) as another automation technology from AI as a new creature posing existential threats. Then, we abstract the existential threats of AI by examining their essential nature, and argue that (i) these threats are best understood as endemic to all intelligences, and (ii) they can be addressed using solutions derived from human (political and cultural) history as well as the evolutionary biological history. We discuss how many of the threats are fundamentally due to a power imbalance between AI and humans. This enables us to transform a large, ambiguous, part of the AI security problem to a familiar political problem since politics is primarily about power. This transformation enables us to address many AI threats by reusing some of the structures and procedures humans developed over time to manage power in human societies. This and other insights lead to proposing principles that must be embedded in AI and imposed on AI developers and operators. The principles belong to four categories: building safe power structures, maintaining operational stability and control, enhancing our security through engineered AI psychology and procedural principles that govern the AI security practice itself. For instance, we argue that all AI entities must be discrete, finite, mortal, supervised, etc., and that their power should be distributed and subject to governance. Moreover, we insist on the general philosophical attitude that all intelligence is inherently unpredictable, dangerous if unchecked, and that the more an AI entity is capable in the physical world, the more it must adhere to human-like and genetic-like limitations and standards of behavior. And finally, we strongly advocate building an AI security and safety practice that involves experts from the humanities and biology as well as technical experts in AI and computing.

The key word in the era of the Fourth Industrial Revolution is definitely AI(Artificial Intelligence). Artificial intelligence's decision based on big data increases the efficiency of work processing by quickly reviewing a large amount of data and reducing time and cost. As a result, artificial intelligence technology has begun to be used in all areas of society, and it is expected to be an anti-corruption policy tool against hiring-related corruption and irregularities that raise doubts about trust and process. However, if there is no proper control over artificial intelligence, artificial intelligence can rather strengthen prejudice and infringe on job seekers' personal information. This is because the data based on artificial intelligence's decisions have been accumulated for a long time, so discrimination and prejudice against race, gender, or certain groups in the past can be revealed or reflected in algorithm design. Moreover, since artificial intelligence's decision-making process is difficult to present procedures and grounds that can be generally understood through self-learning, there is a situation in which humans have to accept machine decisions as they are in a difficult state to refute or correct. Therefore, while maximizing the utility of high-tech technology, it is necessary to find a way to use artificial intelligence without damaging human dignity and value.
 As a way to guarantee the personal rights of artificial intelligence's decision-making, the exclusive decision of artificial intelligence should be limited by referring to the European Personal Information Protection Regulations and the final decision authority should be placed on humans. In addition, it is necessary to set guidelines based on careful analysis by audit agencies, the government, ethics committees, and psychological experts for legal and ethical problems that arise in the hiring process using artificial intelligence. It is more urgent to prepare effective specific procedures to prevent and monitor the risks of technology. As the hiring process is the first step in getting a job, which is a means of self-realization, evaluation according to equality of opportunities and job competency is important. We hope that practical and balanced clear laws will be created through intense discussions by legal, philosophical and technical experts. The most important proposition is that artificial intelligence is a tool that helps humans make decisions, and its ethics always depend on the person who makes the tool.

Resources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algorithm development has been addressed in past studies, little is known about the knowledge and perception of bias among AI developers. This study's objective was to survey AI specialists in health care to investigate developers' perceptions of bias in AI algorithms for health care applications and their awareness and use of preventative measures. A web-based survey was provided in both German and English language, comprising a maximum of 41 questions using branching logic within the REDCap web application. Only the results of participants with experience in the field of medical AI applications and complete questionnaires were included for analysis. Demographic data, technical expertise, and perceptions of fairness, as well as knowledge of biases in AI, were analyzed, and variations among gender, age, and work environment were assessed. A total of 151 AI specialists completed the web-based survey. The median age was 30 (IQR 26-39) years, and 67% (101/151) of respondents were male. One-third rated their AI development projects as fair (47/151, 31%) or moderately fair (51/151, 34%), 12% (18/151) reported their AI to be barely fair, and 1% (2/151) not fair at all. One participant identifying as diverse rated AI developments as barely fair, and among the 2 undefined gender participants, AI developments were rated as barely fair or moderately fair, respectively. Reasons for biases selected by respondents were lack of fair data (90/132, 68%), guidelines or recommendations (65/132, 49%), or knowledge (60/132, 45%). Half of the respondents worked with image data (83/151, 55%) from 1 center only (76/151, 50%), and 35% (53/151) worked with national data exclusively. This study shows that the perception of biases in AI overall is moderately fair. Gender minorities did not once rate their AI development as fair or very fair. Therefore, further studies need to focus on minorities and women and their perceptions of AI. The results highlight the need to strengthen knowledge about bias in AI and provide guidelines on preventing biases in AI health care applications.

This year the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) will be running a workshop at the WebSci ’20 Conference in Southampton, UK. Artificial and Augmented Intelligence systems have the potential to make a real difference in the scientific discovery domain however this brings a new wealth of ethical and societal implications to consider with regards to this research (e.g. human enhancement, algorithmic biases, risk of detriment). This workshop looks to explore the ethical and societal issues centered around using intelligent technologies (Artificial Intelligence, Augmented Intelligence, Machine Learning, and in general Semantic Web Knowledge Technologies) to further scientific discovery, with a strong consideration of data ethics and algorithmic accountability. Advances in technology and software are rarely inherently bad in themselves, however that unfortunately does not preclude them from being subverted to ill intent by others; furthermore, as demonstrated by the examples above, even an unintentional lack of care towards ethical codes and algorithmic accountability can lead to societal and ethical implications of scientific discovery. It is our responsibility as researchers to consider these issues in our research; are we conducting studies ethically? What ethical codes can we put in place for scientific discovery research to mitigate against ethical and societal issues. These are really important issues, and they require an interdisciplinary focus between scientists, social scientists and technical experts in order to be comprehensively addressed. AI4Good is a day long workshop including five keynotes, discussion sessions and an interactive activity. The first keynote is from Dr Will McNeill, from the University of Southampton. Will is a lecturer in Philosophy, and he will speak about Ethical Frameworks and Ethical Judgements. The second keynote will be given by Dr Cian O’Donnovan, a Researcher at UCL. Cian’s research is based on understanding how the benefits of emerging technologies can best contribute to a flourishing world. Cian’s talk will be on AI Ethics from the Ground Up: Cultivating Capabilities for Care. The third keynote will be given by Jacqui Ayling, a PhD Student at the University of Southampton. Jacqui’s PhD is on the topic of researching data protection and innovation in smart cities. She will be talking about Data Ethics for AI & Algorithmic Accountability. The fourth keynote will be given by Dr Peter Craigon from the University of Nottingham. Peter is a Research Fellow specialising in Ethics, and his talk will focus on the Moral IT Cards that he has developed. The final keynote will be given by Dr Samantha Kanza, an Enterprise Fellow at the University of Southampton. Samantha coordinates the AI for Scientific Discovery Network and developed a keen interest in the ethical and societal issues of technology whilst completing her PhD in Web Science. She will be presenting on Ethics for AI for Scientific Discovery.

The digital transformation of the energy sector enhances organizational performance, yet a significant skills gap can hinder an organization's digital aspirations. The industry faces fierce competition for artificial intelligence (AI) skilled professionals, as demand outstrips supply. Concurrently, petrotechnical experts are reassessing their roles due to concerns about skill relevance in the AI era. The achievements of one company's talent development and upskilling initiative illustrate one approach to preparing its workforce for the future. Domain expertise is crucial to understand and solve any industry problem. Our talent acquisition strategy emphasizes internal development over external recruitment. We train petrotechnical experts in AI, addressing both challenges effectively. The approach consists of two steps. The first is upskilling petrotechnical experts in our digital division to become data science practitioners, proficient in low-code and no-code AI solutions. The second step is participation in the newly developed domain data scientist program, in which selected experts are equipped with advanced data science and software development skills, followed by hands-on experience on a real-world use case. This intensive 6-month program, managed by technical experts, human resources, and line management, bridges the gap between domain-specific challenges and data science solutions, incorporating machine-learning operations practices. The program has yielded significant business outcomes across the industry's three pillars: people, technology, and performance. It has democratized AI expertise within the organization, resulting in a 50% increase in the data science workforce and reduced attrition among petrotechnical experts. This has led to substantial savings in recruitment costs. The program stands as an innovative model for scaling AI competency in the industry. Throughout its various stages, the program has facilitated more than 60 AI-powered innovation projects across the exploration and production life cycle and engaged with more than 300 stakeholders. The program has also fostered collaboration through external learning partnerships, addressing sustainability challenges such as emissions, and providing AI solutions for social issues. Following successful campaign phases, the adoption of data science learning has surged, involving more than 1,300 certified data science practitioners and more than 2,500 employees upgrading their skills. This comprehensive approach demonstrates the program's effectiveness in driving AI innovation, enhancing workforce skills, and achieving sustainable and social impact across the company. Since the inception of our AI democratization and upskilling program, our workforce, particularly domain experts, have been motivated to learn and apply data science and AI concepts to business use cases, overcoming previous barriers. AI has transformed from a perceived threat to an opportunity for improvement. This upskilling initiative accelerates AI adoption both internally and externally, promising substantial benefits for the industry.

This article examines artificial intelligence (AI) co-regulation in the EU AI Act and the critical role of standards under this regulatory strategy. It engages with the foundation of democratic legitimacy in EU standardization, emphasizing the need for reform to keep pace with the rapid evolution of AI capabilities, as recently suggested by the European Parliament. The article highlights the challenges posed by interdisciplinarity and the lack of civil society expertise in standard-setting. It critiques the inadequate representation of societal stakeholders in the development of AI standards, posing pressing questions about the potential risks this entails to the protection of fundamental rights, given the lack of democratic oversight and the global composition of standard-developing organizations. The article scrutinizes how under the AI Act technical standards will define AI risks and mitigation measures and questions whether technical experts are adequately equipped to standardize thresholds of acceptable residual risks in different high-risk contexts. More specifically, the article examines the complexities of regulating AI, drawing attention to the multi-dimensional nature of identifying risks in AI systems and the value-laden nature of the task. It questions the potential creation of a typology of AI risks and highlights the need for a nuanced, inclusive, and context-specific approach to risk identification and mitigation. Consequently, in the article we underscore the imperative for continuous stakeholder involvement in developing, monitoring, and refining the technical rules and standards for high-risk AI applications. We also emphasize the need for rigorous training, certification, and surveillance measures to ensure the enforcement of fundamental rights in the face of AI developments. Finally, we recommend greater transparency and inclusivity in risk identification methodologies, urging for approaches that involve stakeholders and require a diverse skill set for risk assessment. At the same time, we also draw attention to the diversity within the European Union and the consequent need for localized risk assessments that consider national contexts, languages, institutions, and culture. In conclusion, the article argues that co-regulation under the AI Act necessitates a thorough re-examination and reform of standard-setting processes, to ensure a democratically legitimate, interdisciplinary, stakeholder-inclusive, and responsive approach to AI regulation, which can safeguard fundamental rights and anticipate, identify, and mitigate a broad spectrum of AI risks.

Background: The rapid advancement of artificial intelligence (AI) in various sectors has revolutionized problem-solving approaches, particularly in healthcare. Developed countries have invested significantly in AI research and applications in healthcare, while low-income countries such as Somalia lag due to various challenges. This study aimed to assess the knowledge, attitudes, and practices (KAP) of AI among healthcare professionals in Somalia and explore their familiarity with AI technologies and practices. Methods: A cross-sectional study was conducted from January 1, 2024, to March 15, 2024, among 441 healthcare professionals in Somalia, using an online questionnaire. The questionnaire assessed the participants' sociodemographic information, knowledge of AI applications in healthcare, attitudes towards AI capabilities, and practical experience with AI in healthcare. Results: Most participants demonstrated good knowledge of AI (67.6%) and a positive attitude towards its potential in healthcare (80.5%). However, a significant gap was observed in the practical application of AI, with 79.1% of the respondents reporting poor practice. The study also found that sociodemographic factors such as age, gender, and income level did not significantly influence knowledge or attitudes towards AI but did affect its practical use. Professionals in certain fields such as midwifery and public health are more likely to use AI in their work. Knowledge and attitude scores were also significant predictors of practice scores. Conclusion: Healthcare professionals in Somalia demonstrate a good understanding and positive attitudes towards AI but encounter challenges in its practical application. This study emphasizes the necessity of an enhanced infrastructure, technical expertise, and data access to fully utilize AI's potential in healthcare. It also highlights the significance of addressing ethical considerations and implementing regulations to ensure responsible use of AI in healthcare. Efforts are needed to translate awareness and receptiveness into effective practice, which could result in a better healthcare system.

This paper explores the transformative potential of artificial intelligence (AI) in reshaping taxation education through a "technology-ethics-institution" trinity framework. It identifies critical challenges in current tax education, including curriculum obsolescence, risks of technological rationality, institutional fragmentation, and faculty capacity gaps. To address these, three-dimensional strategies are proposed: (1) **Curriculum Reconstruction** introduces modular "AI+" courses such as Tax Big Data Analytics and AI Tax Planning, emphasizing Python programming and SQL database skills; (2) **Pedagogical Innovation** leverages deep reinforcement learning for intelligent case analysis, transforming instructors into cognitive coaches while balancing technology reliance and critical thinking; (3) **Institutional Optimization** establishes national AI education standards, a smart tax education certification center, and a hybrid assessment model combining process-oriented evaluation with AI-based competency diagnostics. The study highlights the need to balance technical proficiency with humanistic values through a three-dimensional competency matrix integrating AI literacy, tax expertise, and innovative thinking. Future research directions include long-term ethical impacts of AI education, metaverse applications in tax training, and cross-national comparisons with initiatives like the EU’s Digital Tax Talent Program.

Health leaders are increasingly embracing Artificial Intelligence (AI) to enhance patient care, streamline operations, and support healthcare providers. But are they truly leading an AI-capable organization-one that can harness AI's full potential and long-term value while mitigating its risks? An AI-capable organization possesses the necessary infrastructure, governance, technical expertise, and cultural mindset to effectively develop, deploy, and manage AI systems. It ensures the safe, ethical, and strategic use of AI across its operations, aligning AI adoption with organizational priorities. This article outlines the essential components of an AI-capable organization, provides a framework for assessing AI maturity, and introduces a risk-proportionate approach to building an AI tool pipeline for healthcare delivery. We explore key leadership considerations, including the decision to build or buy AI solutions, and conclude with special considerations, including the rise of Bring Your Own AI and its implications for governance and oversight.

Digital technology plays a vital role in various aspects of human life in the era of digitalization. Electronic devices such as the Internet and phones have changed how we interact, learn, work, and do business. In the current era of computers and the Internet, there are many impacts on society, both positive and negative. Rapid development must be halted in today's technological era. (Amalia et al., 2023). Adaptability is crucial for any country, especially as we enter the 5.0 era or the Super Smart Society. In this concept, modern society is expected to rely on modern technology for daily needs the application of AI evidences this. Artificial Intelligence (AI), a subfield of computer science, enables computers to perform tasks similar to human intelligence (Jaya et al., 2018). Artificial Intelligence (AI) is a technology that enables computers and machines to simulate human learning, understanding, problem-solving, decision-making, creativity, and autonomy. (IBM.com). AI can analyze large amounts of data quickly and accurately. AI can be used in sales to understand consumer behavior patterns, predict market trends, and optimize budget efficiency. For example, AI can help business units reach the right consumer segments so that the costs incurred in promotions do not exceed the planned budget. AI can also be used to perform several tasks in sales, such as answering customer questions using AI in the form of chatbots, which can improve operational efficiency and budget efficiency. Yayasan Darul Anwar Banten's business unit must still utilize AI technology to develop its business. Many factors, such as a lack of knowledge abouneed for moreicial intelligence, technical expertise, and access to the necessary resources for implementation, can cause this

Artificial intelligence (AI) in agriculture is transforming the sector by improving resources efficiency, sustainability and productivity. Our study examined a number of AI-related applications such as pest control, crop monitoring, precision farming and soil health evaluation. AI powered devices enables automated fertilization, harvesting and irrigation, and therefore, cutting down the labor expenses and resource waste. Predictive analytics in AI helps with crop yield and weather forecasts which ultimately improves the planning and risk management. The paper also discusses the challenges and limitations of AI adoption in agriculture, such as the need for reliable data, technical expertise and infrastructure investment. Ultimately, the findings highlights the AI can have positive transformative potential in creating resilient agricultural practices that can meet the demands of a growing global population while minimizing environmental impact. However, one of the biggest uses of AI is precision farming, which uses the technology to optimize inputs like water, fertilizer and pesticides by adjusting them to the unique requirements of the crop and the field. AI techniques also make it possible to detect the pests and diseases through picture recognition and predictive analytics, which ultimately minimizes the crop loss and allows for prompt interventions. Widespread use may be hampered by issues with data quality, model interpretability, expensive prices and system integration. Furthermore, issues with labor impact, regulatory frameworks and scalability complicate its adoption. In order to fully utilize AI in agriculture, researchers, farmers and policymakers must work together to overcome these challenges and develop workable and accessible solutions that are suited to a variety of agricultural environments. Present review also highlighted how AI involvement has the ability to revolutionize agricultural sector by developing resilient methods that can both minimize environmental effects and meet the needs of an expanding global population. The agriculture industry can set the path for a sustainable future by adopting AI advances and guaranteeing the environmental stewardship and goals of food safety and security.

Artificial intelligence technologies have become a ubiquitous part of human life. This prompts us to ask, ‘how should we live well with artificial intelligence?’ Currently, the most prominent candidate answers to this question are principlist. According to these approaches, if you teach people some finite set of principles or convince them to adopt the right rules, people will be able to live and act well with artificial intelligence, even in an evolving and opaque moral world. We find the dominant principlist approaches to be ill-suited to providing forward-looking moral guidance regarding living well with artificial intelligence. We analyze some of the proposed principles to show that they oscillate between being too vague and too specific. We also argue that such rules are unlikely to be flexible enough to adapt to rapidly changing circumstances. By contrast, we argue for an Aristotelian virtue ethics approach to artificial intelligence ethics. Aristotelian virtue ethics provides a concrete and actionable guidance that is also flexible; thus, it is uniquely well placed to deal with the forward-looking and rapidly changing landscape of life with artificial intelligence. However, virtue ethics is agent-based rather than action-based. Using virtue ethics as a basis for living well with artificial intelligence requires ensuring that at least some virtuous agents also possess the relevant scientific and technical expertise. Since virtue ethics does not prescribe a set of rules, it requires exemplars who can serve as a model for those learning to be virtuous. Cultivating virtue is challenging, especially in the absence of moral sages. Despite this difficulty, we think the best option is to attempt what virtue ethics requires, even though no system of training can guarantee the production of virtuous agents. We end with two alternative visions – one from each of the two authors – about the practicality of such an approach.

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.

We present AI Pro, an open-source framework for data processing with Artificial Intelligence (AI) models. Our framework empowers its users with immense capability to transform raw data into meaningful information with a simple configuration file. AI Pro's configuration file generates a data pipeline from start to finish with as many data transformations as desired. AI Pro supports major deep learning frameworks and Open Neural Network Exchange (ONNX), which allows users to choose models from any AI frameworks supported by ONNX. Its wide range of features and user friendly web interface grants everyone the opportunity to broaden their AI application horizons, irrespective of the user's technical expertise. AI Pro has all the quintessential features to perform end-to-end data processing, which we demonstrate using two real world scenarios.