Strategically constructed narratives on artificial intelligence: What stories are told in governmental artificial intelligence policies?

The "Magical Theory" of AI in Medicine: Thematic Narrative Analysis

BACKGROUND

The discourse surrounding medical artificial intelligence (AI) often focuses on narratives that either hype the technology's potential or predict dystopian futures. AI narratives have a significant influence on the direction of research, funding, and public opinion and thus shape the future of medicine.

OBJECTIVE

The paper aims to offer critical reflections on AI narratives, with a specific focus on medical AI, and to raise awareness as to how people working with medical AI talk about AI and discharge their "narrative responsibility."

METHODS

Qualitative semistructured interviews were conducted with 41 participants from different disciplines who were exposed to medical AI in their profession. The research represents a secondary analysis of data using a thematic narrative approach. The analysis resulted in 2 main themes, each with 2 other subthemes.

RESULTS

Stories about the AI-physician interaction depicted either a competitive or collaborative relationship. Some participants argued that AI might replace physicians, as it performs better than physicians. However, others believed that physicians should not be replaced and that AI should rather assist and support physicians. The idea of excessive technological deferral and automation bias was discussed, highlighting the risk of "losing" decisional power. The possibility that AI could relieve physicians from burnout and allow them to spend more time with patients was also considered. Finally, a few participants reported an extremely optimistic account of medical AI, while the majority criticized this type of story. The latter lamented the existence of a "magical theory" of medical AI, identified with techno-solutionist positions.

CONCLUSIONS

Most of the participants reported a nuanced view of technology, recognizing both its benefits and challenges and avoiding polarized narratives. However, some participants did contribute to the hype surrounding medical AI, comparing it to human capabilities and depicting it as superior. Overall, the majority agreed that medical AI should assist rather than replace clinicians. The study concludes that a balanced narrative (that focuses on the technology's present capabilities and limitations) is necessary to fully realize the potential of medical AI while avoiding unrealistic expectations and hype.

Agenda-setting in nascent policy subsystems: issue and instrument priorities across venues

The "policy subsystem" has long been a key concept in our understanding of how policies on a given topic are produced. However, we know much less about policymaking in nascent policy subsystems. This article draws on the theories of agenda-setting and venue shopping to argue that the similarity and convergence of policy subsystems' agendas across different institutional venues and over time are features that distinguish more nascent policy subsystems from their more established, mature counterparts. In simple terms, policy venues' agendas converge when policy actors begin to discuss the same issues and instruments instead of talking past one another. The article illustrates this argument using textual data on Germany's emerging Artificial Intelligence (AI) policy: print media debates, parliamentary debates, and a government consultation from the period between November 2017 and November 2019. The insights from our analysis show that actors emphasize somewhat different policy issues and instruments related to AI in different venues. Nevertheless, the longitudinal analysis suggests that the debate does seem to converge across different venues, which indicates the formation of a subsystem-specific policy agenda regarding AI.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11077-023-09514-5.

Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment

There is an increase in cyberattacks directed at the network behind firewalls. An all-inclusive approach is proposed in this assessment to deal with the problem of identifying new, complicated threats and the appropriate countermeasures. In particular, zero-day attacks and multi-step assaults, which are made up of a number of different phases, some malicious and others benign, illustrate this problem well. In this paper, we propose a highly Boosted Neural Network to detect the multi-stageattack scenario. This paper demonstrated the results of executing various machine learning algorithms and proposed an enormously boosted neural network. The accuracy level achieved in the prediction of multi-stage cyber attacks is 94.09% (Quest Model), 97.29% (Bayesian Network), and 99.09% (Neural Network). The evaluation results of the Multi-Step Cyber-Attack Dataset (MSCAD) show that the proposed Extremely Boosted Neural Network can predict the multi-stage cyber attack with 99.72% accuracy. Such accurate prediction plays a vital role in managing cyber attacks in real-time communication.