This essay explores visually how keeping data and AI on‑device could empower not only users, but also companies.
Can “Conscious Data Contribution” Help Users to Exert “Data Leverage” Against Technology Companies?
Tech users currently have limited ability to act on concerns regarding the negative societal impacts of large tech companies. However, recent work suggests that users can exert leverage using their role in the generation of valuable data, for instance by withholding their data contributions to intelligent technologies. We propose and evaluate a new means to exert this type of leverage against tech companies: "conscious data contribution" (CDC).
A Collectivist, Economic Perspective on AI
Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before. The word ``intelligence'' is being...
Data privacy concerns in AI companion apps - Surfshark
As AI grows, digital companions help with loneliness but raise questions about user data privacy. Many seek virtual relationships, but it's important to remember they are business-driven, not personal bonds.
Personal data storage is an idea whose time has come
Data Ownership as a conversation changes when data resides primarily with people-governed institutions rather than corporations.
Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies
Many powerful computing technologies rely on implicit and explicit data contributions from the public. In this paper, we synthesize emerging research that seeks to better understand and help people action this data leverage. Drawing on prior work in areas including machine learning, human-computer interaction, and fairness and accountability in computing, we present a framework for understanding data leverage that highlights new opportunities to change technology company behavior related to privacy, economic inequality, content moderation and other areas of societal concern.
Reclaim Control | over your technology, your devices, your data, your digital life.
You have the right to control your data, devices, and services. We coordinate, develop and foster communities of Reclaimers who want digital independence. Together, let's reclaim the right to a technical future of data sovereignty, digital autonomy, responsible governance, and collective care online for all.
Something Big Is Happening
A personal note for non-tech friends and family on what AI is starting to change.
Not OK, Google | TechCrunch
At its hardware launch event in San Francisco yesterday, Alphabet showed the sweeping breadth of its ambition to own consumers' personal data, as computing continues to accelerate away from static desktops and screens, coalescing into a cloud of connected devices with the potential to generate far more data -- and data of a far more intimate nature -- than ever before.
HUGE — AI Without Giving Up Your Privacy
We're building an Agentic AI Platform running on your Device that lives with YOU, isolated from the cloud, fundamentally reimagining the relationship between humans and artificial intelligence through ownership, privacy, and massive context. In an age of capture and control, HUGE sells independence and sovereignty.
Backflip: Apple now wants to train AI models with user data after all
Apple’s promise was simple: Personal data from iPhone users will never be used to train AI models. That is now over.
Blog
For quite some time now, i have been working on and off on a fully self-hosted search engine, in hope to make it easier to search across Personal data in an end to end manner. Even as individuals, we are hoarding and generating more and more data with no end in sight. Such "personal" data is being stored from local hard-disks to corporate controlled cloud-centers which makes it distributed in nature. So for following discussion, "Personal" meaning would be flexible enough to accommodate resources on a remote server and/or on different devices, as long the user could prove authentication and/or authorization to that data. Current implementation supports only "images", but eventual goal is also to support other modalities like video, text and audio, some code would be shared, while some new code would be required to better extract Features for each modality.
Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations
Despite widespread speculation about artificial intelligence's impact on the future of work, we lack systematic empirical evidence about how these systems are actually being used for different tasks. Here, we present a novel framework for measuring AI usage patterns across the economy. We leverage a recent privacy-preserving system to analyze over four million Claude.ai conversations through the lens of tasks and occupations in the U.S. Department of Labor's O*NET Database. Our analysis reveals that AI usage primarily concentrates in software development and writing tasks, which together account for nearly half of all total usage. However, usage of AI extends more broadly across the economy, with approximately 36% of occupations using AI for at least a quarter of their associated tasks. We also analyze how AI is being used for tasks, finding 57% of usage suggests augmentation of human capabilities (e.g., learning or iterating on an output) while 43% suggests automation (e.g., fulfilling a request with minimal human involvement). While our data and methods face important limitations and only paint a picture of AI usage on a single platform, they provide an automated, granular approach for tracking AI's evolving role in the economy and identifying leading indicators of future impact as these technologies continue to advance.
A Collectivist, Economic Vision for AI
Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before. The word “intelligence” is being used as a North Star for the development of this technology, with human cognition viewed as a baseline. This view neglects the fact that humans are social animals and that much of our intelligence is social and cultural in origin. Moreover, failing to properly situate aspects of intelligence at the social level contributes to the treatment of the societal consequences of technology as an afterthought. The path forward is not merely more data and compute, and not merely more attention paid to cognitive or symbolic representations, but a thorough blending of economic and social concepts with computational and inferential concepts at the level of algorithm design. , The recent focus of AI has been on the exploitation of massive data to mimic and augment human cognitive capabilities. A complementary focus is provided by the social and economic aspects of intelligence.
MyData
The human-centric approach to data is aimed at a fair, sustainable, and prosperous digital society. In such a society, people get value from their data and set the agenda on how it is used. And for organisations, the ethical use of data is always the most attractive option.
Living in Data: A Citizen's Guide to a Better Information Future (Paperback)
Jer Thorp’s analysis of the word “data” in 10,325 New York Times stories written between 1984 and 2018 shows a distinct trend: among the words most closely associated with “data,” we find not only its classic companions “information” and “digital,” but also a variety of new neighbors—from “scandal” and “misinformation” to “ethics,” “friends,” and “play.”To live in data in the twenty-first century
How Artificial Intelligence Constrains the Human Experience
AbstractArtificial intelligence (AI) and related technologies are transforming many consumption activities, powering breakthroughs that expand the human experience by enhancing human capabilities, performance, and creativity. While this explains the consumer enthusiasm and rapid adoption of these technologies, AI systems can also have the opposite effect: reducing and constraining the range of experiences that are available to consumers. This article examines the mechanisms through which AI can constrain the human experience, considering individual, interpersonal, and societal processes. Our analysis uncovers a complex interplay between the advantages of AI and its inadvertent negative repercussions, which potentially restrict human autonomy, self-identity, relational dynamics, and social behavior. In this article, we propose three different mechanisms at the core of these constraining forces: parametric reductionism, agency transference, and regulated expression. Our exploration of these mechanisms highlights the risks connected to system design and points to questions and implications for future researchers and policymakers.
