arXiv subjects

Rafael Andersson Lipcsey

Publications and source records attributed to Rafael Andersson Lipcsey.

4 recordsLinked to original sources

arXiv2026

Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts

Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritization: we must understand which risks are most severe, who is most vulnerable, and who is most responsible for addressing them. We report results from a three-round Delphi study conducted late 2025 with 272 international AI experts. Experts rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern. Experts estimated the five most severe harms in the next 5 years were likely to come from dangerous capabilities, competitive dynamics, weapons & cyberattacks (including CBRNE), power centralization, and false information. In a business-as-usual scenario, experts judged 18 of 24 risks as having a more than 10% probability of catastrophic outcomes (e.g., more than 1 million deaths or more than USD 100B in financial loss) in the next 5 years (2025-2030). In a scenario where pragmatic mitigations are implemented, experts still judged five risks as having a more than 10% probability of catastrophic outcomes: dangerous capabilities, weapons & cyberattacks, environmental harm, inequality & unemployment, and power centralization. All 24 risks were judged as being more than 5% likely to cause catastrophic outcomes. AI users and the general public were judged the most vulnerable to these risks, but experts assigned the highest responsibility for addressing them to general-purpose AI developers and governance actors (including governments, regulators, and standards bodies). Across most risks, experts identified information, finance, and national security as the most vulnerable sectors. These findings can guide AI risk prioritization and clarify expert expectations about who should bear responsibility for mitigation.

cs.CY↗

arXiv2025

AI Diffusion to Low- and Middle Income Countries; A Blessing or a Curse?

Rapid advances in AI have incited extensive inquiry into its effects on productivity and labor, potentially profound in both positive and negative ways. Often neglected, however, is comprehension of how AI technologies diffuse across and within economies. Developing nations, in particular, face substantial labor market impacts from either swift AI adoption or diminished competitiveness from sluggish diffusion. This paper examines the literature on technology diffusion and proposes a tripartite framework to elucidate AI diffusion pathways: global value chains, research collaboration, and inter-firm knowledge transfers. Employing these metrics, it evaluates AI diffusion in sixteen lower- and middle-income countries (LMICs) relative to four developed nations and assesses their dependency on the USA and China. Findings reveal a notable gap in AI diffusion between developed and developing economies, though this chasm is gradually closing. China emerges as a vital source of future diffusion via value chains, while the USA wields greater influence through research and knowledge transfers. Limitations include the exclusion of certain data sources and regions, and the absence of quantitative analysis on diffusion's relationship with technology intensity. Nonetheless, the research surfaces critical macro-level considerations about AI diffusion. It advocates mechanisms to redistribute AI-induced economic gains and bilateral agreements to complement international accords, thereby addressing the diverse needs and risks of economies entering an AI-dominated era. Future inquiries should explore the nexus between AI diffusion, technology intensity, and productivity; refine diffusion measurement methods; incorporate case studies and targeted policy recommendations; and delve deeper into LMIC-specific labor market outcomes.

econ.GN↗

arXiv2023

An Inquiry Into The Economic Linkages Between The Swedish Air Transport Sector And The Economy As A Whole In The Context Of The Covid-19 Pandemic

This thesis aims to assess the importance of the air transport sector for Sweden's economy in the context of the COVID-19 pandemic. Two complementary research goals are formulated. Firstly, investigating economic linkages of the Swedish air transport sector. Secondly, estimating the effects of the pandemic on Swedish air transport, and the spin-off effects on the economy as a whole. Overview of literature in the field reveals that while a fair amount of research exists on the importance of air transport, unsurprisingly, pandemic effects have yet to be investigated. The methodological framework chosen is input-output analysis, thus, the backbone of data materials used is the Swedish input-output table, complemented by additional datasets. For measuring economic linkages, basic input-output analysis is applied. Meanwhile, to capture the pandemic's impacts, a combination of inoperability analysis and the partial hypothetical extraction model is implemented. It is found that while Swedish air transport plays an important role in turning the cogs of the Swedish economy, when compared to all other sectors, it ranks on the lower end. Furthermore, while the COVID-19 pandemic has a detrimental short-term impact on Swedish air transport, the spin-off effects for the economy as a whole are milder. It is concluded, that out of a value-added perspective, the Swedish government, and by extension policy makers globally need not support failing airlines, and other air transport actors. Nonetheless, some aspects could not be captured through the methods used, such as the possible importance of airlines to national security at times of dodgy global cooperation. Lastly, to accurately capture not only short-term, but also long-term effects of the pandemic, future research using alternative, causality-based frameworks and dynamic input-output models will be highly beneficial.

econ.GN↗

arXiv2023

The Transformative Effects of AI on International Economics

As AI adoption accelerates, research on its economic impacts becomes a salient source to consider for stakeholders of AI policy. Such research is however still in its infancy, and one in need of review. This paper aims to accomplish just that and is structured around two main themes. Firstly, the path towards transformative AI, and secondly the wealth created by it. It is found that sectors most embedded into global value chains will drive economic impacts, hence special attention is paid to the international trade perspective. When it comes to the path towards transformative AI, research is heterogenous in its predictions, with some predicting rapid, unhindered adoption, and others taking a more conservative view based on potential bottlenecks and comparisons to past disruptive technologies. As for wealth creation, while some agreement is to be found in AI's growth boosting abilities, predictions on timelines are lacking. Consensus exists however around the dispersion of AI induced wealth, which is heavily biased towards developed countries due to phenomena such as anchoring and reduced bargaining power of developing countries. Finally, a shortcoming of economic growth models in failing to consider AI risk is discovered. Based on the review, a calculated, and slower adoption rate of AI technologies is recommended.

econ.GN↗