## 6 Conclusions  
The findings of this paper determine which are the most productive and influential references in the research subject of risk assessment of artificial intelligence in business. We have identified the following:  
* The evolution of production on the research subject through the years;
* The most cited papers;
* The most productive and most influential journals, authors, institutions, and countries;
* The existing collaboration links between countries;
* The landscape and evolution of the selected research subject;
* The emerging concepts that specialized scholars are now dealing with. Based on the results and the discussion above, we recommend the following actionable insights for scientists, businesses, and policymakers:  
1. Focus on explainable AI: Researchers should prioritize developing and implementing explainable AI (XAI) techniques. This will enhance transparency and trust in AI systems, making it easier for stakeholders to understand and manage AI-driven decisions. 2. Enhance cybersecurity measures: Given the increasing integration of AI in business operations, it is crucial to incorporate robust cybersecurity measures. This includes developing AI systems resilient to cyber threats and maintaining data privacy and security. 3. Interdisciplinary collaboration: Encourage collaboration between AI researchers and experts in cognitive biases, behavioral economics, and organizational psychology. This interdisciplinary approach can provide deeper insights into AI's human and organizational impacts, leading to more comprehensive risk assessments. 4. Policy development: Policymakers should consider the implications of AI risk assessment in their regulatory frameworks. This includes creating guidelines that promote the ethical use of AI and addressing potential risks associated with AI deployment in various sectors. Initiatives like the EU AI Regulation could be a reference for them. 5. Continuous monitoring and adaptation: Businesses should establish mechanisms for continuous monitoring of AI systems to identify and mitigate risks promptly. This involves regular audits, updates to AI models, and adapting to new threats and challenges as they arise. Databases such as that at [[https://airisk.mit.edu](https://airisk.mit.edu)]([https://airisk.mit.edu](https://airisk.mit.edu)) (accessed on 10 January 2025) could be helpful for them. This research offers interesting findings related to the emerging technologies of human-computer Interaction because although AI is revolutionizing the dynamics of human-computer interaction, this advancement presents a dual-edged sword: as the scientific community initiates rigorous studies on AI risk assessment within organizational contexts, it is imperative to consider critical issues such as data privacy, consumer protection, intellectual property, AI corporate digital responsibility, ethics, multimodal AI risks, bias, and explainable AI. Addressing these factors is essential to ensure the successful integration and interaction of AI systems with human operators. These challenges, together with the promising line of research in how the social and governmental initiatives, such as regulations and guidelines could have accelerated the scientific progress of AI risk assessment, are signaled as future research directions for our community. Regarding limitations, it is necessary to state that this study is limited to the WoS Core Collection database. Although this repository is well recognized in all areas, we could expect differences in results and conclusions if another database is used, as discussed in Section 3. Another issue we have identified is that the results include both the risk assessment of AI in businesses and AI-based risk assessment in businesses. Although that does not reduce the importance of this paper, future research separating both concepts should be interesting for specialized research.