Collaborative Artificial Intelligence Research Articles

Leveraging Virtual Containers for High-Powered, Collaborative AI Research in Radiology

Abstract Numerous obstacles confront radiologists interested in the use of artificial intelligence (AI) models within the field of radiology. For example, discrepancies between the radiologist's and an AI developer's hardware and software specifications pose a substantial hindrance to using AI models. Additionally, accessing and using GPU computers can lead to compatibility issues and add to these challenges. Finally, the dissemination of AI models and the ability to download preexisting AI models are not simple tasks due to the size and complexity of most programs. Virtual containers offer a solution to such compatibility issues and provide a simplified way for radiologists to use AI models. Virtual containers are software tools that bundle code, required programs, and necessary software packages to ensure that a program runs identically for all users, regardless of their computing environment. This article outlines the features of virtual containers (compatibility, versatility, and portability) and highlights an applied use case for virtual containers in the development of an AI model.

Fossil Fuel Prospects in the Energy of the Future (Energy 5.0): A Review

Achieving the energy and climate goals of sustainable development, declared by the UN as imperative and relevant for the upcoming Society 5.0 with its human-centricity of technological development, requires ensuring a “seamless” Fourth Energy Transition, preserving but at the same time modifying the role of fossil fuels in economic development. In this regard, the purpose of this review is to analyze the structure of publications in the field of technological platforms for the energy of the future (Energy 5.0), with digital human-centric modernization and investment in fossil fuel extraction in the context of the Fourth Energy Transition. To achieve this goal, this review presents a comprehensive overview of research in the field of determining the prospects of fossil fuels within Energy 5.0, characterized not only by the dominance of renewable energy sources and the imperative of zero CO2 emissions, but also by the introduction of human-centric technologies of Industry 5.0 (the Industrial Internet of Everything, collaborative artificial intelligence, digital triplets). It was concluded that further research in such areas of Energy 5.0 development as the human-centric vector of modernization of fossil fuel extraction and investment, achieving energy and climate goals for sustainable development, reducing CO2 emissions in the mineral extractive sector itself, and developing CO2 capture and utilization technologies is important and promising for a “seamless” Fourth Energy Transition.

Privacy Preserving Technology using Federated Learning and Blockchain in protecting against Adversarial Attacks for Retinal Imaging

PurposeCollaboration provides valuable data for robust artificial intelligence (AI) model development. Federated learning (FL) is a privacy enhancing technology that allows collaboration while respecting privacy via the development of models without raw data transfer. However state-of-the-art FL models still face challenges in non-independent and identically distributed (non-i.i.d.) health care settings and remain susceptible to privacy breaches. We propose a novel FL framework coupled with blockchain technology to address these challenges. DesignRetrospective multicohort study Subjects and Methods27,145 images from Singapore, China and Taiwan were used to design a novel FL aggregation method for the detection of myopic macular degeneration (MMD) from fundus photographs and macular disease from optical coherence tomography (OCT) scans in feature distribution skew and label distribution imbalance scenarios. We further performed adversarial attacks (label flipping and clean label). As proof of concept, blockchain was incorporated into FL to demonstrate secure transfer of model updates across collaborating sites. Main Outcome MeasuresWe evaluated our FL model performance in MMD and OCT classification and compared our model against state-of the-art FL and centralized models. ResultsOur FL model showed robust performance with areas under the receiving operating characteristic curves (AUC) of 0.868±0.009 for MMD detection and 0.970±0.012 for OCT macular disease classification. In label flipping attack, our FL model had an AUC of 0.861±0.019, similar to the centralized model (AUC 0.856± 0.015) and higher than other FL models (AUC 0.578–0.819) In clean label attack, our FL model had an AUC of 0.878±0.006 which was comparable to the centralized model (AUC 0.878±0.001) and superior to other state-of-the-art FL models with AUC of 0.529-0.838. Simulation showed that the additional time with blockchain in one global epoch was around 5 seconds. The addition of blockchain to the FL framework was feasible with a minimal impact on model development time. ConclusionsOur proposed FL algorithm overcomes the shortcoming of the traditional FL in non i.i.d. situations and remains robust to against adversarial attacks. The integration of blockchain adds further security during the transfer of model updates. Blockchain-enabled FL can be a trusted platform for collaborative health AI research.

Review of the Transition to Energy 5.0 in the Context of Non-Renewable Energy Sustainable Development

The problems of achieving the UN’s sustainable development goals related to providing both developed and developing countries with cheap and accessible energy, as well as in the context of taking climate action, cannot be solved, on the one hand, without a transition to Energy 5.0, within the framework of the upcoming Fifth Industrial Revolution. On the other hand, it cannot be carried out without ensuring a “seamless” Fourth Energy Transition, which poses new challenges for the technological modernization of power production from non-renewables. Along with this, the expected transition to a human-centric Industry 5.0 challenges researchers to identify obstacles to the diffusion of technologies within hydrocarbon production industries and ways to overcome them in regard to the upcoming Mining 5.0 and Oil and Gas 5.0 environment. In this regard, the purpose of this review is to analyze the structure of scientific publications in this field of research on the human-centric development of technologies in terms of these platforms in order to outline the basis for further research. To achieve this goal, this review provides a multifaceted overview of the main technologies of Industry 5.0, embodied within Energy 5.0, Mining 5.0, and Oil and Gas 5.0, such as collaborative artificial intelligence and co-bots, digital tees, the industrial Internet of Everything, smart cities, and industry; their human-centric nature is revealed as the basis for achieving significant sustainable development goals. This review concludes that there is a need for further analysis of certain areas of the transition to Energy 5.0, such as the human-centric development of digital technologies of Industry 5.0 in the fuel and energy sector, and the revision of its role in terms of achieving the sustainable development goals in the future.

Designing an Inclusive Artificial Intelligence (AI) Curriculum for Elementary Students to Address Gender Differences With Collaborative and Tangible Approaches

This study presents an inclusive K-12 AI curriculum for elementary schools, focusing on six design principles to address gender disparities. The curriculum, designed by the researchers and an elementary teacher, uses tangible tools, and emphasizes collaboration in solving daily problems. The MANOVA results revealed initial gender differences in AI knowledge. Following MANCOVA analysis showed significant improvements in AI knowledge, as well as perceptions and behavioral intentions toward AI, effectively bridging the observed knowledge gaps without any significant differences attributed to gender or initial knowledge levels. This evidence underscores the success of tangible and collaborative AI interventions in uniformly enhancing AI knowledge and positively changing perceptions and behavioral intentions among all participants, regardless of gender. Both female and non-binary students felt increased engagement and reduced anxiety toward AI, with improved comprehension and a preference for collaborative learning. This study contributes to the design of inclusive AI interventions, highlighting gender differences and including non-binary students as an essential part of the analysis.

Navigating the AI horizon in hospitality: a novel classification and future research agenda

PurposeThis study aims to evaluate Artificial Intelligence (AI) research in the hospitality industry based on the service AI framework (mechanical-thinking-feeling) and highlight prospective avenues for future inquiry in this growing domain.Design/methodology/approachThis paper conceptualizes timely concepts supported by research spanning multiple domains.FindingsThis research introduces a novel classification for the domain of AI hospitality research. This classification encompasses prediction and pattern recognition, computer vision, NLP, behavioral research, and synthetic data generation. Based on this classification, this study identifies and elaborates upon five emerging research topics, each linked to a corresponding set of research questions. These focal points encompass the realms of interpretable AI, controllable AI, AI ethics, collaborative AI, and synthetic data generation.Originality/valueThis viewpoint provides a foundational framework and a directional compass for future research in AI within the hospitality industry. It pushes the industry forward with a balanced approach to leveraging AI to augment human potential and enrich customer experiences. Both the classification and the research agenda would contribute to the body of knowledge that will guide the industry toward a future where technology and human service coalesce to create unparalleled value for all stakeholders.

Mitigating machine learning bias between high income and low–middle income countries for enhanced model fairness and generalizability

Collaborative efforts in artificial intelligence (AI) are increasingly common between high-income countries (HICs) and low- to middle-income countries (LMICs). Given the resource limitations often encountered by LMICs, collaboration becomes crucial for pooling resources, expertise, and knowledge. Despite the apparent advantages, ensuring the fairness and equity of these collaborative models is essential, especially considering the distinct differences between LMIC and HIC hospitals. In this study, we show that collaborative AI approaches can lead to divergent performance outcomes across HIC and LMIC settings, particularly in the presence of data imbalances. Through a real-world COVID-19 screening case study, we demonstrate that implementing algorithmic-level bias mitigation methods significantly improves outcome fairness between HIC and LMIC sites while maintaining high diagnostic sensitivity. We compare our results against previous benchmarks, utilizing datasets from four independent United Kingdom Hospitals and one Vietnamese hospital, representing HIC and LMIC settings, respectively.

Collaborative artificial intelligence system for investigation of healthcare claims compliance

Healthcare fraud, waste and abuse are costly problems that have huge impact on society. Traditional approaches to identify non-compliant claims rely on auditing strategies requiring trained professionals, or on machine learning methods requiring labelled data and possibly lacking interpretability. We present Clais, a collaborative artificial intelligence system for claims analysis. Clais automatically extracts human-interpretable rules from healthcare policy documents (0.72 F1-score), and it enables professionals to edit and validate the extracted rules through an intuitive user interface. Clais executes the rules on claim records to identify non-compliance: on this task Clais significantly outperforms two baseline machine learning models, and its median F1-score is 1.0 (IQR = 0.83 to 1.0) when executing the extracted rules, and 1.0 (IQR = 1.0 to 1.0) when executing the same rules after human curation. Professionals confirm through a user study the usefulness of Clais in making their workflow simpler and more effective.

Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.

The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare. We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively. Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare. Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.

Skybound Intelligence: AI's Impact on Drone Technology

The integration of Artificial Intelligence (AI) with drone technology has revolutionized various industries, from agriculture and construction to defense and emergency response. This paper aims to provide an overview of the multifaceted role AI plays in enhancing drone capabilities and addressing challenges in different domains.Firstly, the paper discusses how AI-driven autonomy enables drones to perform complex tasks autonomously, such as navigation, obstacle avoidance, and mission planning. Advanced algorithms, including machine learning and computer vision, empower drones to interpret and respond to dynamic environments in real-time, significantly expanding their operational capabilities.Secondly, the paper examines the crucial role of AI in data processing and analysis for drones. AI algorithms enable drones to collect, process, and analyze vast amounts of data quickly and efficiently, extracting actionable insights for various applications. In agriculture, for instance, drones equipped with AI can analyze crop health, identify pest infestations, and optimize resource allocation for improved yields.Furthermore, the paper explores how AI enhances the security and safety of drone operations. AI-powered systems enable drones to detect and mitigate potential risks, such as unauthorized access, airspace violations, and mechanical failures, thereby ensuring safer and more reliable operations, especially in densely populated areas or sensitive environments. Moreover, the paper discusses the implications of AI in enabling swarming capabilities among drones. Through collaborative AI algorithms, drones can coordinate their actions, communicate intelligently, and achieve collective goals efficiently, leading to applications like search and rescue, surveillance, and infrastructure inspection on a large scale. Lastly, the paper addresses the ethical and regulatory considerations associated with the integration of AI in drone technology, including privacy concerns, algorithmic biases, and the need for transparent decision-making processes. Keywords: AI-enabled drones, Autonomous UAVs, Drone autonomy, AI-driven navigation, Machine learning for drones, and Drone surveillance

Collaborative innovations in Artificial Intelligence (AI): Partnering with leading U.S. tech firms to combat human trafficking

This article reviews, integrates, and expands upon research initiatives that explore the development and implementation of advanced artificial intelligence (AI)–driven tools and methodologies. In exploring collaborations with leading U.S. AI technology firms, including Nvidia, Dataiku, DataRobot, and C3.ai, this study is specifically aimed at identifying, preventing, and combating human trafficking. This collaborative effort seeks to create a synergistic framework that capitalizes on the unique capabilities and resources of each firm. The overarching goal is to enhance the effectiveness of AI technologies in detecting trafficking activities, analyzing diverse data sources, and providing vital support to law enforcement and NGOs in rescue and prevention efforts. The comprehensive review delves into the multifaceted applications of AI, emphasizing its role in prevention through machine learning, data analytics, and natural language processing. It navigates through collaborative initiatives, presenting partnerships with prominent U.S. tech firms, accompanied by case studies that showcase successful collaborative projects. Challenges inherent in collaborative AI efforts are addressed, and the paper proposes strategic solutions to overcome these obstacles. The ethical and legal considerations section scrutinizes the implications of deploying AI in human trafficking prevention, exploring the delicate balance between innovation and safeguarding privacy and civil liberties. Furthermore, the final section anticipates future directions and recommendations, providing insights into emerging technologies that could enhance anti-trafficking efforts. Specific recommendations are offered for fortifying collaborations between AI developers and tech firms, and the paper discusses broader implications for policy, suggesting avenues for future research in the dynamic field of AI-driven human trafficking prevention. In essence, this comprehensive review not only synthesizes existing knowledge but also integrates a forward-looking research initiative, contributing significantly to the ongoing discourse on leveraging technology to combat human trafficking on a global scale.

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works.

Artificial intelligence in respiratory care: Current scenario and future perspective.

This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.

Federated Learning: Advancing Healthcare through Collaborative Artificial Intelligence

Abstract More and more healthcare data are becoming easily accessible from clinical institutions, patients, insurance companies and the pharmaceutical industry, amongst others, due to the quick development of computer software and hardware technologies. With this access, data science technologies have a never-before-seen chance to generate data-driven insights and raise the standard of healthcare delivery. However, healthcare data are frequently fragmented and private, making it challenging to produce reliable results across populations. The electronic health records of various patient populations, for instance, are owned by multiple hospitals, and because of their sensitive nature, it is challenging for hospitals to share these records. This poses a substantial obstacle to creating generalisable, effective analytical methods that require various ‘big data’. Federated learning offers an excellent opportunity to integrate disparate healthcare data sources while protecting privacy. Federated learning uses a central server to train a standard global model while retaining all the sensitive data in local institutions where it belongs.

Does artificial intelligence promote green innovation? An assessment based on direct, indirect, spillover, and heterogeneity effects

This paper investigates the intricate relationship between artificial intelligence (AI) and green innovation within the context of sustainable development goals. As societies strive to achieve sustainability, understanding the dynamics between technological advancements and environmental progress becomes paramount. Drawing from panel data encompassing 51 countries between 2000 and 2019, this study employs fixed-effects models, mediated effects models, and spatial Durbin models to meticulously examine the influence of AI on green innovation. The empirical findings reveal a robust and significantly positive correlation between AI and green innovation, highlighting the critical role of AI in fostering environmental innovation. Heterogeneity analysis across developed and developing economies delineates variations in the impact of AI on green innovation, shedding light on the influence of economic development levels and financial structures. Developed nations showcase a more pronounced AI-green innovation relationship compared to their developing counterparts, highlighting the complexities of technology adoption within distinct economic landscapes. Moreover, this study delves into the transmission mechanisms underlying the AI-green innovation nexus, revealing the mediating roles of industrial structure and human capital. Industrial upgrading and the enhancement of human capital emerge as crucial pathways through which AI indirectly stimulates green innovation. Spatial analyses reveals the spatial relevance of green innovation globally, emphasizing AI's substantial impact not only within domestic spheres but also across neighboring regions. There are significant direct, indirect, and total effects of AI on green innovation, highlighting its spillover characteristics and the catalytic role it plays in driving collaborative AI development on a global scale. This research contributes nuanced insights into the interplay between AI and green innovation, providing a foundation for policymakers, businesses, and researchers to comprehend the multifaceted dimensions of technological interventions in fostering sustainable innovation. The findings emphasize the imperative of collaborative efforts in utilizing AI's potential to propel green innovation, thereby advancing global sustainability agendas.

CFLIT: Coexisting Federated Learning and Information Transfer

Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables collaborative AI model training across distributed mobile edge devices. By exploiting the superposition property of multiple-access channels, over-the-air computation allows concurrent model uploading from massive devices over the same radio resources, and thus significantly reduces the communication cost of FL. In this paper, we study the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coexistence</i> of over-the-air FL and traditional information transfer (IT) in a mobile edge network, where an access point (AP) coordinates a set of devices for over-the-air FL and serves multiple devices for information transfer in the meantime. We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coexisting federated learning and information transfer (CFLIT)</i> communication framework, where the FL and IT devices share the wireless spectrum in an orthogonal frequency division multiplexing (OFDM) system. Under this framework, we aim to maximize the IT data rate and guarantee a given FL convergence performance by optimizing the long-term radio resource allocation. A key challenge that limits the spectrum efficiency of the coexisting system lies in the large overhead incurred by frequent communication between the server and edge devices for FL model aggregation. To address the challenge, we rigorously analyze the impact of the computation-to-communication ratio on the convergence of over-the-air FL in wireless fading channels. The analysis reveals the existence of an optimal computation-to-communication ratio that minimizes the amount of radio resources needed for over-the-air FL to converge to a given error tolerance. Based on the analysis, we propose a low-complexity online algorithm to jointly optimize the radio resource allocation for both the FL devices and IT devices. We further derive an analytical expression of the achievable data rate of IT users. Extensive numerical simulations verify the superior performance of the proposed design for the coexistence of FL and IT devices in wireless cellular systems.

Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective

Federated learning (FL) is a promising distributed framework for collaborative artificial intelligence model training while protecting user privacy. A bootstrapping component that has attracted significant research attention is the design of incentive mechanism to stimulate user collaboration in FL. The majority of works adopt a broker-centric approach to help the central operator to attract participants and further obtain a well-trained model. Few works consider forging participant-centric collaboration among participants to pursue an FL model for their common interests, which induces dramatic differences in incentive mechanism design from the broker-centric FL. To coordinate the selfish and heterogeneous participants, we propose a novel analytic framework for incentivizing effective and efficient collaborations for participant-centric FL. Specifically, we respectively propose two novel game models for contribution-oblivious FL (COFL) and contribution-aware FL (CAFL), where the latter one implements a minimum contribution threshold mechanism. We further analyze the uniqueness and existence for Nash equilibrium of both COFL and CAFL games and design efficient algorithms to achieve equilibrium solutions. Extensive performance evaluations show that there exists free-riding phenomenon in COFL, which can be greatly alleviated through the adoption of CAFL model with the optimized minimum threshold.

Major Trends in Today's Intelligent Robotics in Light of the Creation of Collaborative Artificial Intelligence

Robotics and advances in artificial intelligence are breakthrough innovations with considerable promise and the potential to radically change the economic and social aspects of society. Research on the development of robotics is still scarce. This article fills the gap by analyzing the creation and diffusion of robotics innovation and the role of intellectual property in this process. The robotics innovation ecosystem is based on cooperative networks of independent specialists, scientific organizations, and companies. The state plays a significant role in supporting the innovation activity in the considered sphere, first of all, through grant financing, placement of defense orders and implementation of national strategies of robotics development. Competitions and prizes are an important incentive for creating innovations. Patenting is used by companies to protect intellectual property from encroachment by third parties, to ensure freedom of action, to license technology, and to protect against prosecution. The industrial revolution has changed the very notion of means and means of production in the minds of the masses in such a way that the process of robotization and the introduction of artificial intelligence in different areas of life has become inevitable. The article presents an analysis of possible ways of disclosing the concept of "artificial intelligence" as a legal category and its correlation with the concept of "robot", considers the issues of legal responsibility for the performance of work by artificial intelligence, studies the possibility of recognizing the holder of artificial intelligence work as a subject of law.

Sharing Data With Shared Benefits: Artificial Intelligence Perspective.

Artificial intelligence (AI) and data sharing go hand in hand. In order to develop powerful AI models for medical and health applications, data need to be collected and brought together over multiple centers. However, due to various reasons, including data privacy, not all data can be made publicly available or shared with other parties. Federated and swarm learning can help in these scenarios. However, in the private sector, such as between companies, the incentive is limited, as the resulting AI models would be available for all partners irrespective of their individual contribution, including the amount of data provided by each party. Here, we explore a potential solution to this challenge as a viewpoint, aiming to establish a fairer approach that encourages companies to engage in collaborative data analysis and AI modeling. Within the proposed approach, each individual participant could gain a model commensurate with their respective data contribution, ultimately leading to better diagnostic tools for all participants in a fair manner.

The AI Effect: Rethinking Design Workflows for Enhanced Productivity and Creativity

Advancements in artificial intelligence and machine learning are transforming how designers work with computers. This thesis explores the developing relationship between artificial intelligence (AI) and design processes, focusing on how it impacts the creation of digital experiences. This study examines the impact of AI on design, highlighting how AI can improve efficiency and creativity alongside ethical challenges and the need to preserve human uniqueness in digital creations. It investigates AI's effects on design workflows, creativity, and the designer's evolving role through interviews, content analysis, case studies, and surveys. The aim is to understand AI's influence on designer productivity, innovation, and ethical issues. The need for this research stems from significant changes in the design field due to AI, focusing on optimizing design workflows within ethical and user-centric frameworks. This thesis contributes to discussions on AI in design, advocating for a thoughtful integration of AI. It is based on assumptions about design's digital transformation and AI's ethical implications, framing the inquiry into this intricate topic. With an emphasis on the collaborative intelligence model, wherein human and AI synergies enhance design outcomes, the study investigates the practical, ethical, and creative facets of AI in design. This research aims to empower designers to navigate the evolving AI landscape effectively, advocating for a future where AI enhances human creativity and problem-solving in the design of compelling digital experiences.