To Bot or Not to Bot? How AI Companions Are Reshaping Human Services and Connection

Curated on Posted on by Stefaan Verhulst

Essay by Julia Freeland Fisher: “Last year, a Harvard study on chatbots drew a startling conclusion: AI companions significantly reduce loneliness. The researchers found that “synthetic conversation partners,” or bots engineered to be caring and friendly, curbed loneliness on par with interacting with a fellow human. The study was silent, however, on the irony behind these findings: synthetic interaction is not a real, lasting connection. Should the price of curing loneliness really be more isolation?

Missing that subtext is emblematic of our times. Near-term upsides often overshadow long-term consequences. Even with important lessons learned about the harms of social media and big tech over the past two decades, today, optimism about AI’s potential is soaring, at least in some circles.

Bots present an especially tempting fix to long-standing capacity constraints across education, health care, and other social services. AI coaches, tutors, navigators, caseworkers, and assistants could overcome the very real challenges—like cost, recruitment, training, and retention—that have made access to vital forms of high-quality human support perennially hard to scale.

But scaling bots that simulate human support presents new risks. What happens if, across a wide range of “human” services, we trade access to more services for fewer human connections?…(More)”.

Overcoming challenges associated with broad sharing of human genomic data

Curated on Posted on by Stefaan Verhulst

Paper by Jonathan E. LoTempio Jr & Jonathan D. Moreno: “Since the Human Genome Project, the consensus position in genomics has been that data should be shared widely to achieve the greatest societal benefit. This position relies on imprecise definitions of the concept of ‘broad data sharing’. Accordingly, the implementation of data sharing varies among landmark genomic studies. In this Perspective, we identify definitions of broad that have been used interchangeably, despite their distinct implications. We further offer a framework with clarified concepts for genomic data sharing and probe six examples in genomics that produced public data. Finally, we articulate three challenges. First, we explore the need to reinterpret the limits of general research use data. Second, we consider the governance of public data deposition from extant samples. Third, we ask whether, in light of changing concepts of broad, participants should be encouraged to share their status as participants publicly or not. Each of these challenges is followed with recommendations…(More)”.

Smart cities: the data to decisions process

Curated on Posted on by Stefaan Verhulst

Paper by Eve Tsybina et al: “Smart cities improve citizen services by converting data into data-driven decisions. This conversion is not coincidental and depends on the underlying movement of information through four layers: devices, data communication and handling, operations, and planning and economics. Here we examine how this flow of information enables smartness in five major infrastructure sectors: transportation, energy, health, governance and municipal utilities. We show how success or failure within and between layers results in disparities in city smartness across different regions and sectors. Regions such as Europe and Asia exhibit higher levels of smartness compared to Africa and the USA. Furthermore, within one region, such as the USA or the Middle East, smarter cities manage the flow of information more efficiently. Sectors such as transportation and municipal utilities, characterized by extensive data, strong analytics and efficient information flow, tend to be smarter than healthcare and energy. The flow of information, however, generates risks associated with data collection and artificial intelligence deployment at each layer. We underscore the importance of seamless data transformation in achieving cost-effective and sustainable urban improvements and identify both supportive and impeding factors in the journey towards smarter cities…(More)”.

Towards Best Practices for Open Datasets for LLM Training

Curated on Posted on by Stefaan Verhulst

Paper by Stefan Baack et al: “Many AI companies are training their large language models (LLMs) on data without the permission of the copyright owners. The permissibility of doing so varies by jurisdiction: in countries like the EU and Japan, this is allowed under certain restrictions, while in the United States, the legal landscape is more ambiguous. Regardless of the legal status, concerns from creative producers have led to several high-profile copyright lawsuits, and the threat of litigation is commonly cited as a reason for the recent trend towards minimizing the information shared about training datasets by both corporate and public interest actors. This trend in limiting data information causes harm by hindering transparency, accountability, and innovation in the broader ecosystem by denying researchers, auditors, and impacted individuals access to the information needed to understand AI models.
While this could be mitigated by training language models on open access and public domain data, at the time of writing, there are no such models (trained at a meaningful scale) due to the substantial technical and sociological challenges in assembling the necessary corpus. These challenges include incomplete and unreliable metadata, the cost and complexity of digitizing physical records, and the diverse set of legal and technical skills required to ensure relevance and responsibility in a quickly changing landscape. Building towards a future where AI systems can be trained on openly licensed data that is responsibly curated and governed requires collaboration across legal, technical, and policy domains, along with investments in metadata standards, digitization, and fostering a culture of openness…(More)”.

Beware the Intention Economy: Collection and Commodification of Intent via Large Language Models

Curated on Posted on by Stefaan Verhulst

Article by Yaqub Chaudhary and Jonnie Penn: “The rapid proliferation of large language models (LLMs) invites the possibility of a new marketplace for behavioral and psychological data that signals intent. This brief article introduces some initial features of that emerging marketplace. We survey recent efforts by tech executives to position the capture, manipulation, and commodification of human intentionality as a lucrative parallel to—and viable extension of—the now-dominant attention economy, which has bent consumer, civic, and media norms around users’ finite attention spans since the 1990s. We call this follow-on the intention economy. We characterize it in two ways. First, as a competition, initially, between established tech players armed with the infrastructural and data capacities needed to vie for first-mover advantage on a new frontier of persuasive technologies. Second, as a commodification of hitherto unreachable levels of explicit and implicit data that signal intent, namely those signals borne of combining (a) hyper-personalized manipulation via LLM-based sycophancy, ingratiation, and emotional infiltration and (b) increasingly detailed categorization of online activity elicited through natural language.

This new dimension of automated persuasion draws on the unique capabilities of LLMs and generative AI more broadly, which intervene not only on what users want, but also, to cite Williams, “what they want to want” (Williams, 2018, p. 122). We demonstrate through a close reading of recent technical and critical literature (including unpublished papers from ArXiv) that such tools are already being explored to elicit, infer, collect, record, understand, forecast, and ultimately manipulate, modulate, and commodify human plans and purposes, both mundane (e.g., selecting a hotel) and profound (e.g., selecting a political candidate)…(More)”.

Good government data requires good statistics officials – but how motivated and competent are they?

Curated on Posted on by Stefaan Verhulst

Worldbank Blog: “Government data is only as reliable as the statistics officials who produce it. Yet, surprisingly little is known about these officials themselves. For decades, they have diligently collected data on others –  such as households and firms – to generate official statistics, from poverty rates to inflation figures. Yet, data about statistics officials themselves is missing. How competent are they at analyzing statistical data? How motivated are they to excel in their roles? Do they uphold integrity when producing official statistics, even in the face of opposing career incentives or political pressures? And what can National Statistical Offices (NSOs) do to cultivate a workforce that is competent, motivated, and ethical?

We surveyed 13,300 statistics officials in 14 countries in Latin America and the Caribbean to find out. Five results stand out. For further insights, consult our Inter-American Development Bank (IDB) report, Making National Statistical Offices Work Better.

1. The competence and management of statistics officials shape the quality of statistical data

Our survey included a short exam assessing basic statistical competencies, such as descriptive statistics and probability. Statistical competence correlates with data quality: NSOs with higher exam scores among employees tend to achieve better results in the World Bank’s Statistical Performance Indicators (r = 0.36).

NSOs with better management practices also have better statistical performance. For instance, NSOs with more robust recruitment and selection processes have better statistical performance (r = 0.62)…(More)”.

Nearly all Americans use AI, though most dislike it, poll shows

Curated on Posted on by Stefaan Verhulst

Axios: “The vast majority of Americans use products that involve AI, but their views of the technology remain overwhelmingly negative, according to a Gallup-Telescope survey published Wednesday.

Why it matters: The rapid advancement of generative AI threatens to have far-reaching consequences for Americans’ everyday lives, including reshaping the job market, impacting elections, and affecting the health care industry.

The big picture: An estimated 99% of Americans used at least one AI-enabled product in the past week, but nearly two-thirds didn’t realize they were doing so, according to the poll’s findings.

  • These products included navigation apps, personal virtual assistants, weather forecasting apps, streaming services, shopping websites and social media platforms.
  • Ellyn Maese, a senior research consultant at Gallup, told Axios that the disconnect is because there is “a lot of confusion when it comes to what is just a computer program versus what is truly AI and intelligent.”

Zoom in: Despite its prevalent use, Americans’ views of AI remain overwhelmingly bleak, the survey found.

  • 72% of those surveyed had a “somewhat” or “very” negative opinion of how AI would impact the spread of false information, while 64% said the same about how it affects social connections.
  • The only area where a majority of Americans (61%) had a positive view of AI’s impact was regarding how it might help medical diagnosis and treatment…

State of play: The survey found that 68% of Americans believe the government and businesses equally bear responsibility for addressing the spread of false information related to AI.

  • 63% said the same about personal data privacy violations.
  • Majorities of those surveyed felt the same about combatting the unauthorized use of individuals’ likenesses (62%) and AI’s impact on job losses (52%).
  • In fact, the only area where Americans felt differently was when it came to national security threats; 62% of those surveyed said the government bore primary responsibility for reducing such threats…(More).”

Governing artificial intelligence means governing data: (re)setting the agenda for data justice

Curated on Posted on by Stefaan Verhulst

Paper by Linnet Taylor, Siddharth Peter de Souza, Aaron Martin, and Joan López Solano: “The field of data justice has been evolving to take into account the role of data in powering the field of artificial intelligence (AI). In this paper we review the main conceptual bases for governing data and AI: the market-based approach, the personal–non-personal data distinction and strategic sovereignty. We then analyse how these are being operationalised into practical models for governance, including public data trusts, data cooperatives, personal data sovereignty, data collaboratives, data commons approaches and indigenous data sovereignty. We interrogate these models’ potential for just governance based on four benchmarks which we propose as a reformulation of the Data Justice governance agenda identified by Taylor in her 2017 framework. Re-situating data justice at the intersection of data and AI, these benchmarks focus on preserving and strengthening public infrastructures and public goods; inclusiveness; contestability and accountability; and global responsibility. We demonstrate how they can be used to test whether a governance approach will succeed in redistributing power, engaging with public concerns and creating a plural politics of AI…(More)”.

Data sharing restrictions are hampering precision health in the European Union

Curated on Posted on by Stefaan Verhulst

Paper by Cristina Legido-Quigley et al: “Contemporary healthcare is undergoing a transition, shifting from a population-based approach to personalized medicine on an individual level. In October 2023, the European Partnership for Personalized Medicine was officially launched to communicate the benefits of this approach to citizens and healthcare systems in member countries. The main debate revolves around the inconsistency in regulatory changes within personal data access and its potential commercialization. Moreover, the lack of unified consensus within European Union (EU) countries is leading to problems with data sharing to progress personalized medicine. Here we discuss the integration of biological data with personal information on a European scale for the advancement of personalized medicine, raising legal considerations of data protection under the EU General Data Protection Regulation (GDPR)…(More)”.

Artificial Intelligence Narratives

Curated on Posted on by Stefaan Verhulst

A Global Voices Report: “…Framing AI systems as intelligent is further complicated and intertwined with neighboring narratives. In the US, AI narratives often revolve around opposing themes such as hope and fear, often bridging two strong emotions: existential fears and economic aspirations. In either case, they propose that the technology is powerful. These narratives contribute to the hype surrounding AI tools and their potential impact on society. Some examples include:

Many of these framings often present AI as an unstoppable and accelerating force. While this narrative can generate excitement and investment in AI research, it can also contribute to a sense of technological determinism and a lack of critical engagement with the consequences of widespread AI adoption. Counter-narratives are many and expand on the motifs of surveillance, erosions of trust, bias, job impacts, exploitation of labor, high-risk uses, the concentration of power, and environmental impacts, among others.

These narrative frames, combined with the metaphorical language and imagery used to describe AI, contribute to the confusion and lack of public knowledge about the technology. By positioning AI as a transformative, inevitable, and necessary tool for national success, these narratives can shape public opinion and policy decisions, often in ways that prioritize rapid adoption and commercialization…(More)”