Big Tech Companies Research Articles

Video Generative AI in Digital Marketing: A SWOT Analysis on E-Commerce

Artificial Intelligence (AI) is reshaping the way various business processes are performed, most rapidly in digital marketing, due to its advanced computational capabilities. ECommerce companies are in advantageous position to adopt and reap the benefits of AI for many reasons including the readily available computational infrastructure and big data related to their users and processes. Meanwhile, big tech companies and startups are competing to create and improve generative AI models, that create high-definition videos based on inputs in the form of text, image or videos. The current research specifically aims to analyse the video outputs of various video generating AI models available for digital marketing purposes in ecommerce businesses and to rationally analyse how those video generative AI models benefit or affect e-commerce companies. To meet the research objectives, mixed methodologies of experimentation and content analysis were deployed. It was observed that the video outputs from the tested models were not yet suitable for digital marketing and appeared underdeveloped. Additionally, the SWOT analysis revealed advantages like low costs, alongside concerns such as intellectual property issues.

Attributing Responsibility to Big Tech for Mass Atrocity: Social Media and Transitional Justice

Big Tech companies such as Meta, the owner of Facebook, are increasingly accused of enabling human rights violations. The proliferation of toxic speech in their digital platforms has been in the background of recent episodes of mass atrocity, the most salient of which recently transpired in Myanmar and Ethiopia. The involvement of Big Tech companies in mass atrocity raises multiple normative and conceptual challenges. One is to properly conceptualize Meta’s responsibility for the circulation of toxic speech. On one view, endorsed by the corporation itself, Meta can be absolved from any significant share of responsibility for these atrocities because toxic speech is the speech of some (rogue) users, hosted but neither created nor endorsed by the company; if anything, Meta is responsible for failing to anticipate and swiftly remove that speech. I will argue that this view is misleading, as it misses the underlying forces crafting toxic speech. Meta’s business model relies on what one might call the algorithmic capture of attention, which it achieves by manipulating its users and by creating an environment in which manipulative practices of some users thrive over others. This fact alone turns the company into a co-creator of toxic speech rather than a mere conduit of the toxic speech of others. As a result, it is safe to claim that Meta bears significant causal responsibility and sufficient moral responsibility for the dissemination of toxic speech, such that it justifies its inclusion in transitional justice processes and grounds its moral obligation to act in ways that advance these processes.

“Platform Schooling” Through “Platform Tutoring”? Unpacking the Relationship Between FJP Trainings and Journalistic Skills

ABSTRACT Big tech companies are increasingly suggesting tools and trainings to journalists, extending their well-documented influence in journalism. The discussion around journalistic skills have persisted for over a century and still today this topic remains vital attracting significant scholarship. Therein, the platformization of journalism is seen as a major challenge within the field, with an impact on the foundations of journalism challenging journalistic skills and work processes in newsrooms. This study entangles, to understand through the lens of visual-text centric approach the ways FJP through its trainings and e-courses suggested to journalists are encouraging certain journalistic skills to emerge. After the selection of three journalist-oriented tools provided by FJP, I collected 124 trainings, and 28 self-paced courses offered by the tools in their website to unpack the suggested skills. Findings suggest that FJP trainings and e-courses are promoting a set of platform-centric skills, which are deeply intertwined with platform-specific algorithms and business models. This can be viewed as a form of platform schooling which, in addition to journalism schools and work environments, mediates understanding of what are and what are not successful skills when practicing journalism.

Digital sovereignty and artificial intelligence: a normative approach

Digital sovereignty is a term increasingly used by academics and policymakers to describe efforts by states, private companies, and citizen groups to assert control over digital technologies. This descriptive conception of digital sovereignty is normatively deficient as it centres discussion on how power is being asserted rather than evaluating whether actions are legitimate. In this article, I argue that digital sovereignty should be understood as a normative concept that centres on authority (i.e., legitimate control). A normative approach to digital sovereignty is beneficial as it supports critical discourse about the desirability of actors’ assertions of control. It is also more closely aligned with traditional definitions of sovereignty that are grounded in ideas of sovereign authority. To operationalise this normative approach to digital sovereignty and demonstrate the deficiencies of a descriptive approach, the role that “Big Tech” companies are playing in controlling artificial intelligence is considered from both perspectives. Through this case study, it is highlighted that Big Tech companies assert a high degree of control (i.e., descriptive digital sovereignty), but that they lack strong input legitimacy and have a questionable amount of output legitimacy. For this reason, it is argued that Big Tech companies should only be considered quasi-sovereigns over AI.

Who Gets Paid (for) What? The Cultural Political Economy of News Content in Generative AI

One of the key controversies that generative artificial intelligence (AI) has recently stirred was whether compensation is due for the copyrighted materials used to train AI models. This article explores the logic, trajectories, and dynamics of content generation, including news, through generative AI in two distinctive yet intertwined domains. Guided by a cultural political economy approach, it examines how both the political context (validation/legitimation of AI-generated news content by established news media) and the economic context (use of unpaid and underpaid labor in the forms of freely scraped data and data annotation work) shape the deployment of news content on AI models. It further untangles how the space for serious, independent journalism may shrink, as big tech companies’ algorithmic technologies emerge as a solution to contemporary problems in journalism. A clear danger here is that AI companies’ proprietary algorithms, language training models, and value-laden parameters are incompatible with journalism's democratic obligations and responsibilities.

Exploring the landscape of automated species identification apps: Development, promise, and user appraisal

Abstract Two decades ago, Gaston and O'Neill (2004) deliberated on why automated species identification had not become widely employed. We no longer have to wonder: This AI-based technology is here, embedded in numerous web and mobile apps used by large audiences interested in nature. Now that automated species identification tools are available, popular, and efficient, it is time to look at how the apps are developed, what they promise, and how users appraise them. Delving into the automated species identification apps landscape, we found that free and paid apps differ fundamentally in presentation, experience, and the use of biodiversity and personal data. However, these two business models are deeply intertwined. Going forward, although big tech companies will eventually take over the landscape, citizen science programs will likely continue to have their own identification tools because of their specific purpose and their ability to create a strong sense of belonging among naturalist communities.

First International Workshop on Quantum SoftwareEngineering: The Next Evolution (QSE-NE) Summary 2024

As we enter the new quantum age, tech giants like IBM, Google, and Microsoft are leading the way. But, there is a major challenge: we need to turn exciting quantum possibilities into real, practical tools. That's where Quantum Software Engineering (QSE) comes in, and it's why "Quantum Software Engineering: The next Evolution (QSE-NE)" workshop is proposed. This workshop is conducted to help guide this change. It aims to highlight the latest ideas in QSE and get people from different fields talking, from university researchers to big tech companies. Our main goals were to share new QSE methods, tools, and studies and to tackle the tough problems we face in developing quantum software. This workshop is all about setting the direction for the future of building quantum software, at a time when it is really crucial. More details can be found at: https://sites.google.com/view/qse-ne/ home.

Veri Sömürgeciliği Çağında Verinin Sömürgecilikten Kurtulması: Profesör Nick Couldry ile Bir Söyleşi

In May 2024, the Faculty of Communication at Üsküdar University hosted the 11th International Communication Symposium, which this year has been delineated on the overarching theme 'Digital Inequality and Data Colonialism’. Among the scholars and subject matter experts serving as keynote speakers during its proceedings, Prof. Dr. Nick Couldry along with Prof. Dr. Ulises A. Mejias- gave an insightful keynote speech entitled “Data Grab: The New Colonialism of Big Tech”, which focused on the subjects explored in the groundbreaking research recently published by the two scholars and entitled Data Grab: The New Colonialism of Big Tech and How to Fight Back. In this context, as founders of the concept of data colonialism, Couldry and Mejias discussed further implications of this concept and how Big Tech companies are nowadays grabbing users’ data, exploiting labour and connections for their profit. We interviewed Professor Nick Couldry on data colonialism and why it represents a radically different concept from those already existing in sociology and communication studies. Moreover, we asked what we can do as users of websites and social media platforms

Issues of assessing the FinTech impact on the financial sector's structure

Subject. This article examines the areas of penetration of FinTech and the impact of the expansion of FinTech and BigTech companies on the structure of the financial sector. Objectives. The article aims to analyze the processes of the emergence of the FinTech phenomenon and its growing penetration into all spheres of finance, identify the consequences of these processes for the structure of the financial sector, as well as formulate the principles for the formation of a balanced structure of the financial sector in the context of its ongoing digitalization. Methods. For the study, I used the systems approach, comparative intermarket analysis, impact analysis of the technological component of economic development at the macro level. Results. The article reveals the main forms and manifestations of the impact of digitalization on the structure of the financial sector and the main activities and types of services of FinTech companies. The article also formulates the principles for the formation of a balanced competitive structure of the financial sector. Relevance. The results of the study can be used by the Bank of Russia and other national financial market regulators to improve approaches to building a modern competitive balanced financial structure.

360 Degree Risk Assessment for Reliable ContingentWorkforce Operations - A Business Credibility Model

The gig economy, while providing big tech companies with unparalleled access to specialized talent and operational agility, simultaneously introduces a complex spectrum of risks, ranging from financial liabilities to regulatory non-compliance and performance inefficiencies. Traditional risk management frameworks fall short of addressing the dynamic nature of these challenges. This paper presents the 360° Risk Assessment Model, an advanced, data-driven framework leveraging machine learning to deliver a real-time, multi-faceted evaluation of risk. By dynamically quantifying and recalibrating risk scores, this model empowers enterprises to strategically mitigate potential losses, optimize decision-making, and enhance the governance of their contingent workforce ecosystem.

Analyzing Mobility Patterns at Scale in Pandemic Scenarios Leveraging the Mobile Network Ecosystem

The ubiquity and pervasiveness of mobile network technologies has made them so deeply ingrained in our everyday lives that by interacting with them for very simple purposes (e.g., messaging or browsing the Internet), we produce an unprecedented amount of data that can be analyzed to understand our behavior. While this practice has been extensively adopted by telcos and big tech companies in the last few years, this condition, which was unimaginable just 20 years ago, has only been mildly exploited to fight the COVID-19 pandemic. In this paper, we discuss the possible alternatives that we could leverage in the current mobile network ecosystem to provide regulators and epidemiologists with the right understanding of our mobility patterns, to maximize the efficiency and extent of the introduced countermeasures. To validate our analysis, we dissect a fine-grained dataset of user positions in two major European countries severely hit by the pandemic. The potential of using these data, harvested employing traditional mobile network technologies, is unveiled through two exemplary cases that tackled macro and microscopic aspects.

The US National Security State and Big Tech: frenemy relations and innovation planning in turbulent times*

I analyze the relation between what Weiss (2014) dubbed the United States National Security State (US NSS) and US Big Tech focusing on artificial intelligence (AI). I argue that the US NSS was an innovation planner until the 1990s and advance the hypothesis that, amid that vacuum, this millennium has seen the emergence of AI planning by US Big Tech. This has resulted in tensions with the US NSS given the centrality of AI in the military–industrial complex and ultimately for buttressing American primacy, which has always been the US NSS’s main goal. Amid today’s global turbulence, this tension has leaned towards a strategic yet asymmetric alliance that I define as a frenemy relation. Beyond the tit-for-tat between the US NSS and Big Tech companies, their experiences as innovation planners open space for prefiguring an alternative, a democratic way of planning innovation for the common good.

Quantification of Automatic Speech Recognition System Performance on d/Deaf and Hard of Hearing Speech.

To evaluate the performance of commercial automatic speech recognition (ASR) systems on d/Deaf and hard-of-hearing (d/Dhh) speech. A corpus containing 850 audio files of d/Dhh and normal hearing (NH) speech from the University of Memphis Speech Perception Assessment Laboratory was tested on four speech-to-text application program interfaces (APIs): Amazon Web Services, Microsoft Azure, Google Chirp, and OpenAI Whisper. We quantified the Word Error Rate (WER) of API transcriptions for 24 d/Dhh and nine NH participants and performed subgroup analysis by speech intelligibility classification (SIC), hearing loss (HL) onset, and primary communication mode. Mean WER averaged across APIs was 10 times higher for the d/Dhh group (52.6%) than the NH group (5.0%). APIs performed significantly worse for "low" and "medium" SIC (85.9% and 46.6% WER, respectively) as compared to "high" SIC group (9.5% WER, comparable to NH group). APIs performed significantly worse for speakers with prelingual HL relative to postlingual HL (80.5% and 37.1% WER, respectively). APIs performed significantly worse for speakers primarily communicating with sign language (70.2% WER) relative to speakers with both oral and sign language communication (51.5%) or oral communication only (19.7%). Commercial ASR systems underperform for d/Dhh individuals, especially those with "low" and "medium" SIC, prelingual onset of HL, and sign language as primary communication mode. This contrasts with Big Tech companies' promises of accessibility, indicating the need for ASR systems ethically trained on heterogeneous d/Dhh speech data. 3 Laryngoscope, 2024.

Reimagining Deep Learning Systems through the Lens of Data Systems

The high-profile success of Deep Learning (DL) at Big Tech companies, including recent Large Language Models (LLMs) such as the GPT and Llama families, has led to high demand among Web companies, consumer app companies, enterprises, healthcare, domain sciences, and even digital humanities and arts to adopt modern DL for their applications. The scale of DL workloads, domain-specific datasets, and publicly available pre-trained base models keeps growing. Naturally, tackling issues of scalability, usability , and resource/cost efficiency of DL systems are critical to democratizing modern DL-powered AI. We find that some key lessons from the decades of work on data system design, implementation, and optimization-when adapted prudently-can go a long way toward that goal. Specifically, our work shows that new analogues of multi-query optimization for DL systems can substantially reduce runtimes and costs, while improving ease of use. This article lays out how we reimagine DL workloads that way and summarizes the technical contributions powering this transformation.

The Age of Datafeudalism: From Digital Panopticon to Synthetic Democracy

In “Datafeudalism: The Domination of Modern Societies by Big Tech Companies” (Saura García in Phil Technol 37(3):1–18, 2024a) I analysed the concept of datafeudalism and its implications for the proper functioning of democracy. In this article, I put forward the hypothesis that big digital companies are exercising domination over the current social context and its different functional spheres, such as politics and democracy, and critique the negative implications that datafeudalism is having for the proper functioning of modern democratic systems. In this regard, Wörsdörfer (Phil Technol 37(3):1–5, 2024) has offered a critical commentary on the content in question. In particular, he focuses his discourse on the concept of the digital panopticon and its link with datafeudalism. The following replay present a reasoned response to these questions.

Datafeudalism: The Domination of Modern Societies by Big Tech Companies

This article critically examines the domination exerted by big digital companies on the current social, economic, and political context of modern societies, with a particular focus on the implications for the proper functioning of democracy. The objective of this article is to introduce and develop the concept of datafeudalism, expose its emergence for the proper functioning of modern societies and democracy, and to propose courses of action to reverse this situation. To achieve this purpose, firstly, the evolution from surveillance capitalism to datafeudalism will be discussed. Secondly, the structures and operating logic of data feudalism will be analyzed. Thirdly, the harmful impacts of datafeudalism on the proper functioning of the democratic systems of the European Union will be examined. Finally, an attempt will be made to outline courses of action that will make it possible to reverse the situation of economic, social and political tyranny exercised by big digital companies through datafeudalism.

‘Datafeudalism: The Domination of Modern Societies by Big Tech Companies’: A Commentary

‘Datafeudalism: The Domination of Modern Societies by Big Tech Companies’: A Commentary

Legal Challenges Surrounding Participation of Big Tech Companies in the Russian Social Networking Market

Legal Challenges Surrounding Participation of Big Tech Companies in the Russian Social Networking Market

FinTech services from BigTech companies

AbstractThe so‐called BigTech companies Amazon, Alphabet, Apple and Meta and are constantly attempting to grow into new business areas. Due to their expertise in data analysis, they have managed to quickly establish themselves in many industries as digital ecosystems and, as part of this strategy, are also expanding their footprint in financial services. However, the biggest challenge that BigTech companies are facing in financial services is customer acceptance. This paper contributes to the emerging field of digital ecosystems and the acceptance of sensitive services by users. Although the BigTech firms have gained much attention, only little empirical analysis is available. This paper aims to shed light on the determinants of customer acceptance of BigTech banking services. Based on a survey in Switzerland this research develops an analytic model to identify and test the relevant determinants. The results indicate that the strongest significant influences were found in subjective risk and trust which clearly demonstrates the still‐existing incumbents' advantage over the BigTech companies.

Ten Years of the Single Supervisory Mechanism: Looking into the Past, Navigating into the Future

ABSTRACT In November 2024 European banking supervision will have been operational for 10 years. The Single Supervisory Mechanism (SSM) has evolved from a start-up to a mature, well-established, and respected supervisor. Harmonized and transparent supervisory practices have been implemented, whilst the European banking sector has proved to be resilient. Nevertheless, whilst acknowledging the progress that has been made, Europe should not rest on its laurels. As the global financial landscape is continuously evolving, the European framework must also evolve. Growing geopolitical tensions, the rise of FinTech and BigTech companies, the ongoing digital transformation, and climate change not only impact banks but also add more complexity to the work of supervisors. This is due to unexpected and difficult-to-model events, the creation of new business models, products, and services, as well as the emergence of cultural, behavioural, and ethical considerations that should be taken into account. To address these challenges, supervisors should enhance their competencies, approaches, and tools to stay ahead of evolving market dynamics and to remain aligned with the rapid evolution of technology and the risks that climate change poses. Equally important, ensuring thorough and efficient supervision requires fostering and strengthening collaboration and information sharing between all relevant authorities.