Relevant bibliographies by topics / AI Democratization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'AI Democratization'

Author: Grafiati
Published: 5 June 2025
Last updated: 12 July 2025

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Journal articles on the topic "AI Democratization"

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Wielinga, Bauke, and Stefan Buijsman. "A Relational Justification of AI Democratization." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 1567–77. http://dx.doi.org/10.1609/aies.v7i1.31747.

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While much has been written about what democratized AI should look like, there has been surprisingly little attention for the normative grounds of AI democratization. Existing calls for AI democratization that do make explicit arguments broadly fall into two categories: outcome-based and legitimacy-based, corresponding to outcome-based and process-based views of procedural justice respectively. This paper argues that we should favor relational justifications of AI democratization to outcome-based ones, because the former additionally provide outcome-independent reasons for AI democratization. Moreover, existing legitimacy-based arguments often leave the why of AI democratization implicit and instead focus on the how. We present two relational arguments for AI democratization: one based on empirical findings regarding the perceived importance of relational features of decision-making procedures, and one based on Iris Marion Young’s conception of justice, according to which the main forms of injustice are domination and oppression. We show how these arguments lead to requirements for procedural fairness and thus also offer guidance on the how of AI democratization. Finally, we consider several objections to AI democratization, including worries concerning epistemic exploitation.
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Costa, Carlos J., Manuela Aparicio, Sofia Aparicio, and Joao Tiago Aparicio. "The Democratization of Artificial Intelligence: Theoretical Framework." Applied Sciences 14, no. 18 (2024): 8236. http://dx.doi.org/10.3390/app14188236.

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The democratization of artificial intelligence (AI) involves extending access to AI technologies beyond specialized technical experts to a broader spectrum of users and organizations. This paper provides an overview of AI’s historical context and evolution, emphasizing the concept of AI democratization. Current trends shaping AI democratization are analyzed, highlighting key challenges and opportunities. The roles of pivotal stakeholders, including technology firms, educational entities, and governmental bodies, are examined in facilitating widespread AI adoption. A comprehensive framework elucidates the components, drivers, challenges, and strategies crucial to AI democratization. This framework is subsequently applied in the context of scenario analyses, offering insights into potential outcomes and implications. The paper concludes with recommendations for future research directions and strategic actions to foster responsible and inclusive AI development globally.
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Luchs, Inga. "AI for All?" A Peer-Reviewed Journal About 12, no. 1 (2023): 135–47. http://dx.doi.org/10.7146/aprja.v12i1.140445.

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Research in artificial intelligence (AI) is heavily shaped by big tech today. In the US context, companies such as Google and Microsoft profit from a tremendous position of power due to their control over cloud computing, large data sets and AI talent. In light of this dominance, many media researchers and activists demand open infrastructures and community-led approaches to provide alternative perspectives – however, it is exactly this discourse that companies are appropriating for their expansion strategies. In recent years, big tech has taken up the narrative of democratizing AI by open-sourcing their machine learning (ML) tools, simplifying and automating the application of AI and offering free educational ML resources. The question that remains is how an alternative approach to ML infrastructures – and to the development of ML systems – can still be possible. What are the implications of big tech’s strive for infrastructural expansion under the umbrella of ‘democratization’? And what would a true democratization of ML entail? I will trace these two questions by critically examining, first, the open-source discourse advanced by big tech, as well as, second, the discourse around the AI open-source community Hugging Face that sees AI ethics and democratization at the heart of their endeavour. Lastly, I will show how ML algorithms need to be considered beyond their instrumental notion. It is thus not enough to simply hand over the technology to the community – we need to think about how we can conceptualize a radically different approach to the creation of ML systems.
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Bilgin, Recep, Seydali Ekici, and Fatih Sezgin. "The effect of international relations on democratization of Turkey between 2002-2010 during justice and development party rule." Revista Amazonia Investiga 11, no. 57 (2022): 205–20. http://dx.doi.org/10.34069/ai/2022.57.09.22.

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Democratization takes place under different conditions in every country. The social structure and that of state play important roles, and there are many other internal and external factors for this process. Turkey also went through different phases for democratization processes. This is a qualitative study and formed by reviewing related literature and evaluating. It focuses on external factors between 2002 and 2010 because there was a struggle and long-lasting conflicts between secular elites and conservative democrats during this time. With the help and encouragement of European Union (EU), Justice and Development Party governments were able to eliminate the status quo inherited from 1980 military coup. Although democratization of Turkey proceeded with the effect of many different factors, the effect of international relations in this era was priceless for the governments of that time. Especially Turkey’s candidate process to membership of EU enforced conditionality by these countries. Even more the ruling party consented to democratize. Under the control of them, Turkey made a relatively smooth transition to more democratic state.
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Mboungou, Mouyabi Seke. "The democratization of innovation in africa - A perspective driven by artificial intelligence trends." i-manager's Journal on Artificial Intelligence & Machine Learning 3, no. 1 (2025): 1. https://doi.org/10.26634/jaim.3.1.20923.

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This paper provides a unique perspective on the democratization of innovation in Africa through Artificial Intelligence (AI) trends. This work avoids a problem statement and methodology, instead providing a holistic view of Africa's evolving innovation landscape, particularly in relation to AI. This unconventional approach serves as a preface to future study in the growing field of AI in Africa. By examining the intersection of AI trends and democratization of innovation, this paper offers insights into the transformative potential of AI technologies in addressing societal challenges, empowering local communities, and driving inclusive growth across the continent. As Africa embraces AI as a catalyst for progress, this abstract sets the stage for further exploration of the implications, opportunities, and challenges that lie ahead in harnessing the power of AI to unlock Africa's vast potential for innovation and development.
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Satwik Reddy Jambula. "From Democratization to Accountability: Ensuring Responsible AI on Low-Code Platforms." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 757–62. https://doi.org/10.32996/jcsts.2025.7.5.84.

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The democratization of artificial intelligence through low-code platforms has fundamentally transformed how organizations develop and deploy AI solutions, enabling non-technical professionals to build sophisticated applications without extensive programming knowledge. This democratization, while accelerating innovation and reducing time-to-market, simultaneously introduces significant governance challenges as AI development extends beyond traditional technical teams. Knowledge gaps, diffused responsibility, limited model visibility, and regulatory compliance difficulties emerge as critical concerns when powerful AI capabilities intersect with reduced technical oversight. Addressing these challenges requires embedding governance directly into development platforms rather than treating it as an external process. This article examines the transformative impact of low-code AI democratization and proposes an integrated framework incorporating embedded governance tools, explainability mechanisms, and role-based permissions to ensure responsible development. By analyzing implementations across diverse industries, the article demonstrates how organizations can maintain appropriate accountability while still benefiting from the accelerated innovation that democratized AI enables. The findings reveal that platforms incorporating integrated governance features achieve significantly higher adoption rates, reduced compliance incidents, and improved stakeholder trust, ultimately delivering superior business outcomes compared to platforms lacking such mechanisms.
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Huang, Ming-Hui, and Roland T. Rust. "The GenAI Future of Consumer Research." Journal of Consumer Research 52, no. 1 (2025): 4–17. https://doi.org/10.1093/jcr/ucaf013.

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Abstract We develop a novel generative AI (GenAI) trajectory, “democratization-average trap-model collapse,” to identify data and model challenges posed by GenAI, from which we project the GenAI future of consumer research. This trajectory consists of three key phenomena: democratization broadens consumer participation, the average trap produces generic responses, and model collapse occurs when GenAI outputs lose human sensibilities. Data and model challenges arise as democratization enhances data representation while also embedding real-world biases. The average trap, caused by next-token prediction models, leads to generic outputs that lack individuality. Additionally, model collapse occurs when GenAI increasingly learns from its own outputs, amplifying machine bias and diverging from human behavior. To address these challenges, researchers can leverage democratization to study marginalized consumers and prioritize human-centered research over purely data-driven methods. The average trap can be mitigated by fine-tuning models with task-specific and marginalized consumption data while engineering responses for uniqueness. Preventing model collapse requires integrating human–machine hybrid data and applying theories of mind to realign AI with human-centric consumption. Finally, we outline three future research directions: preserving data distribution tails to support consumption democratization, countering the average trap in next-token prediction, and reversing the trajectory from democratization to model collapse.
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Finley, Thomas K. "The Democratization of Artificial Intelligence: One Library’s Approach." Information Technology and Libraries 38, no. 1 (2019): 8–13. http://dx.doi.org/10.6017/ital.v38i1.10974.

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This article argues that the current technological revolution that is happening in Artificial Intelligence is not just about its prevalence in daily life, but the real revolution is about the emergence of AI tools that may help to democratize its use. Lowering the barrier to a technology that is perceived more as science fiction than accessible for mass utilization. A Public Library shares its approach in leveraging available tools to enable AI education for all.
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DiCostanzo, Dominic J., Ahmet S. Ayan, Sachin R. Jhawar, Theodore T. Allen, and Emily S. Patterson. "Machine Learning Data Pipeline for the Democratization of AI." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 12, no. 1 (2023): 120–24. http://dx.doi.org/10.1177/2327857923121029.

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The use of artificial intelligence continues to increase. In healthcare, there has been a recent increase in AI applications to real-time individual patient clinical care, as opposed to population-based research or quality improvement efforts. However, the expertise to evaluate and implement these solutions is limited and often congregates in academic medical centers, creating barriers to adoption for smaller community and rural centers. Lowering the barrier to entry for innovative tools can help address disparities in patient outcomes due to access and other urban/rural contributors. We describe a strategy for evaluating commercially available machine learning models to disseminate lessons learned from developing, validating, and implementing machine learning-based models in clinical care in radiation therapy. In addition, we share an end-to-end data pipeline as open-source code with the tools necessary to identify, extract, organize, and process the data for use in machine-learning applications. We illustrate the application of this data pipeline to the use of brachytherapy to treat female cervical cancer patients. The example will show how we used the proposed pipeline to extract 708 potential participants and applied the developed methods and visualizations to clean the data providing 144 study participants for inclusion in our study. Finally, we discuss the anticipated challenges in implementing machine learning models in commercially available FDA-approved devices and suggest solutions using discrete tools built in different programming languages.
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Burnside, Mercedes, Hamish Crocket, Michael Mayo, John Pickering, Adrian Tappe, and Martin de Bock. "Do-It-Yourself Automated Insulin Delivery: A Leading Example of the Democratization of Medicine." Journal of Diabetes Science and Technology 14, no. 5 (2019): 878–82. http://dx.doi.org/10.1177/1932296819890623.

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Digital innovations have led to an explosion of data in healthcare, driving processes of democratization and foreshadowing the end of the paternalistic era of medicine and the inception of a new epoch characterized by patient-centered care. We illustrate that the “do it yourself” (DIY) automated insulin delivery (AID) innovation of diabetes is a leading example of democratization of medicine as evidenced by its application to the three pillars of democratization in healthcare (intelligent computing; sharing of information; and privacy, security, and safety) outlined by Stanford but also within a broader context of democratization. The heuristic algorithms integral to DIY AID have been developed and refined by human intelligence and demonstrate intelligent computing. We deliver examples of research in artificial pancreas technology which actively pursues the use of machine learning representative of artificial intelligence (AI) and also explore alternate approaches to AI within the DIY AID example. Sharing of information symbolizes the core philosophy behind the success of the DIY AID evolution. We examine data sharing for algorithm development and refinement, for sharing of the open-source algorithm codes online, for peer to peer support, and sharing with medical and scientific communities. Do it yourself AID systems have no regulatory approval raising safety concerns as well as medico-legal and ethical implications for healthcare professionals. Other privacy and security factors are also discussed. Democratization of healthcare promises better health access for all and we recognize the limitations of DIY AID as it exists presently, however, we believe it has great potential.
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Dissertations / Theses on the topic "AI Democratization"

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Kinde, Lorentz. "Testing AI-democratization : What are the lower limits of textgeneration using artificial neural networks?" Thesis, Luleå tekniska universitet, Datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77167.

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Articial intelligence is an area of technology which is rapidly growing. Considering it'sincreasing inuence in society, how available is it? This study attempts to create a web contentsummarizer using generative machine learning. Several concepts and technologies are explored, most notably sequence to sequence, transfer learning and recursive neural networks. The study later concludes how creating a purely generative summarizer is unfeasible on a hobbyist level due to hardware restrictions, showing that slightly more advanced machine learning techniques still are unavailable to non-specialized individuals. The reasons why are investigated in depth using an extensive theoretical section which initially explains how neural networks work, then natural language processing at large and finally how to create a generative recurrent articial neural network. Ethical and societal concerns concerning machine learning text generation is also discussed, along with alternative approaches to solving the task at hand.
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Books on the topic "AI Democratization"

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Urbinati, Nadia. Ai confini della democrazia: Opportunità e rischi dell'universalismo democratico. Donzelli, 2007.

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Book chapters on the topic "AI Democratization"

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Luce, Leanne. "Democratization and Impacts of AI." In Artificial Intelligence for Fashion. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3931-5_12.

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Masood, Adnan, and Adnan Hashmi. "Democratization of AI Using Cognitive Services." In Cognitive Computing Recipes. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4106-6_1.

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Khan, Sadia, Alfonso Morales, and Beth Plale. "Democratization is a Process, not a Destination: Operationalizing Ethics and Democratization in a Cyberinfrastructure for AI Project." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71304-0_3.

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Vuppalapati, Chandrasekar, Anitha Ilapakurti, Sharat Kedari, Rajasekar Vuppalapati, Jayashankar Vuppalapati, and Santosh Kedari. "Democratization of AI to Small Scale Farmers, Albeit Food Harvesting Citizen Data Scientists, that Are at the Bottom of the Economic Pyramid." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39512-4_55.

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"How to Safeguard AI." In The Democratization of Artificial Intelligence. transcript-Verlag, 2019. http://dx.doi.org/10.14361/9783839447192-015.

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Schieferdecker, Ina, Jürgen Großmann, and Martin A. Schneider. "How to Safeguard AI." In The Democratization of Artificial Intelligence. transcript Verlag, 2019. http://dx.doi.org/10.1515/9783839447192-015.

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"The Political Affinities of AI." In The Democratization of Artificial Intelligence. transcript-Verlag, 2019. http://dx.doi.org/10.14361/9783839447192-010.

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"AI, Democracy and the Law." In The Democratization of Artificial Intelligence. transcript-Verlag, 2019. http://dx.doi.org/10.14361/9783839447192-016.

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McQuillan, Dan. "The Political Affinities of AI." In The Democratization of Artificial Intelligence. transcript Verlag, 2019. http://dx.doi.org/10.1515/9783839447192-010.

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Djeffal, Christian. "AI, Democracy and the Law." In The Democratization of Artificial Intelligence. transcript Verlag, 2019. http://dx.doi.org/10.1515/9783839447192-016.

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Conference papers on the topic "AI Democratization"

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Castro, Keylin Alessandra, Joselyn Alvarado Siwady, Erika Castillo, Alberto Alonzo, Manuel Cardona, and María Elena Perdomo. "Artificial Intelligence for All: Challenges and Harnessing Opportunities in AI Democratization." In 2024 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT). IEEE, 2024. https://doi.org/10.1109/icmlant63295.2024.00030.

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Plale, Beth, Sadia Khan, and Alfonso Morales. "Democratization of AI: Challenges of AI Cyberinfrastructure and Software Research." In 2023 IEEE 19th International Conference on e-Science (e-Science). IEEE, 2023. http://dx.doi.org/10.1109/e-science58273.2023.10254950.

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Zhang, Zhaonian, and Richard Jiang. "User-Centric Democratization towards Social Value Aligned Medical AI Services." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/702.

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Democratic AI, aiming at developing AI systems aligned with human values, holds promise for making AI services accessible to people. However, concerns have been raised regarding the participation of non-technical individuals, potentially undermining the carefully designed values of AI systems by experts. In this paper, we investigate Democratic AI, define it mathematically, and propose a user-centric evolutionary democratic AI (u-DemAI) framework. This framework maximizes the social values of cloud-based AI services by incorporating user feedback and emulating human behavior in a community via a user-in-the-loop iteration. We apply our framework to a medical AI service for brain age estimation and demonstrate that non-expert users can consistently contribute to improving AI systems through a natural democratic process. The u-DemAI framework presents a mathematical interpretation of Democracy for AI, conceptualizing it as a natural computing process. Our experiments successfully show that involving non-tech individuals can help improve performance and simultaneously mitigate bias in AI models developed by AI experts, showcasing the potential for Democratic AI to benefit end users and regain control over AI services that shape various aspects of our lives, including our health.
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Mirje, V., Y. F. Lim, C. E. Dragomir, Y. Shariffuddin, and S. Selvaraju. "Driving AI Democratization: A Strategic Framework for Wide-Scale Adoption Across Organization." In EAGE Workshop on Data Science - From Fundamentals to Opportunities. European Association of Geoscientists & Engineers, 2023. http://dx.doi.org/10.3997/2214-4609.202377011.

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Gheibi, Noushin, and Stefan Boeschen. "Democratization in Industry via Multi-Agent Systems, The case of a production company." In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004709.

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Democracy is typically a question of political government. Nevertheless, in recent years, the forms of democratic development have changed in the course of the governance debate. According to Council of Europe, E-democracy tools use technology to boost key democratic values like participation, inclusivity, efficiency, effectiveness, transparency, openness and accountability within the democratic system. Alongside civil society, companies are playing an increasingly important role in the establishment of collective order. The difficult aspects of this development can be seen in the concentration of market power and the circumvention of employee co-determination. At the same time, however, Small and Medium Enterprises (SME) sometimes take on the role of pioneers. One key example is about AI-based decision support systems in order to realize new decision-making and co-determination opportunities. This raises the question of what potential for democratization and, if so, in what form, is actually emerging here. On the one hand, this article raises the question of how aspects of a “democratization” in companies can be realized and presents a conceptual approach for analysing such ambitions. On the other hand, specific challenges of a “democratization” via digital tools will be worked out by analysing the case study of a SME. An important result is that forms of democratization via multi-agent systems are only perceived as democratic if they are introduced and procedurally anchored within the company through social processes that are perceived as legitimate.
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Vuppalapati, Chandrasekar, Anitha Ilapakurti, Sharat Kedari, Jaya Vuppalapati, Santosh Kedari, and Raja Vuppalapati. "Democratization of AI, Albeit Constrained IoT Devices & Tiny ML, for Creating a Sustainable Food Future." In 2020 3rd International Conference on Information and Computer Technologies (ICICT). IEEE, 2020. http://dx.doi.org/10.1109/icict50521.2020.00089.

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Xu, Yichong, Chenguang Zhu, Shuohang Wang, et al. "Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/383.

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Most of today's AI systems focus on using self-attention mechanisms and transformer architectures on large amounts of diverse data to achieve impressive performance gains. In this paper, we propose to augment the transformer architecture with an external attention mechanism to bring external knowledge and context to bear. By integrating external information into the prediction process, we hope to reduce the need for ever-larger models and increase the democratization of AI systems. We find that the proposed external attention mechanism can significantly improve the performance of existing AI systems, allowing practitioners to easily customize foundation AI models to many diverse downstream applications. In particular, we focus on the task of Commonsense Reasoning, demonstrating that the proposed external attention mechanism can augment existing transformer models and significantly improve the model's reasoning capabilities. The proposed system, Knowledgeable External Attention for commonsense Reasoning (KEAR), reaches human parity on the open CommonsenseQA research benchmark with an accuracy of 89.4% in comparison to the human accuracy of 88.9%.
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Afanasiev, Oleksandr. "PROBLEMS OF USING ARTIFICIAL INTELLIGENCE IN NET ART PRACTICES." In “Müasir incəsənət məkanında süni intellekt: problemlər və perspektivlər” Beynəlxalq elmi-nəzəri konfrans. İnformasiya Texnologiyaları İnstitutu, 2025. https://doi.org/10.25045/asucaai.2025.32.

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The article reveals the impact of artificial intelligence (AI) on the development of net art – a form of media art, the main tool and environment of which is the Internet. AI is considered in the context of the evolution of net art. Modern artistic practices that use algorithmic technologies are analyzed, and the main ethical and social challenges associated with the introduction of AI into artistic processes are identified. Special attention is paid to the issue of authorship, as well as the transformation of the interaction between the artist, the viewer, and the artistic environment. The possibility of democratization and development of activist net-art practices is considered, thanks to the wide availability of AI. However, the risks of censorship, information manipulation, and commercialization of the creative process are identified due to the dependence of AI on large technological corporations. The study emphasizes the need for a critical reflection of these processes to understand future trends in the development of net art in the context of modern visual culture.
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Vuppalapati, Raja, Chandrasekar Vuppalapati, Anitha Ilapakurti, et al. "Democratization of Artificial Intelligence (AI) to Small Scale Farmers: A Framework to Deploy AI Models to Tiny IoT Edges That Operate in Constrained Environments." In 9th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009358706520657.

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Shekhar, S., N. Choudhary, R. Saraiya, et al. "Solving the Talent Challenge for Artificial Intelligence." In ADIPEC. SPE, 2024. http://dx.doi.org/10.2118/222820-ms.

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Abstract 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.
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