The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges

Innovative applications of artificial intelligence during the COVID-19 pandemic

The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.

Augmented Intelligence, Augmented Responsibility?

Intelligence Augmentation Systems (IAS) allow for more efficient and effective corporate processes by means of an explicit collaboration between artificial intelligence and human judgment. However, the higher degree of system autonomy, along with the enrichment of human capabilities, amplifies pre-existing issues of the distribution of moral responsibility: If an IAS has caused harm, firms who have operated the system might argue that they lack control over its actions, whereas firms who have developed the system might argue that they lack control over its actual use. Both parties rejecting responsibility and attributing it to the autonomous nature of the system leads to a variety of technologically induced responsibility gaps. Given the wide-ranging capabilities and applications of IAS, such responsibility gaps warrant a theoretical grounding in an ethical theory, also because the clear distribution of moral responsibility is an essential first step to govern explicit morality in a firm using structures such as accountability mechanisms. As part of this paper, first the necessary conditions for the distribution of responsibility for IAS are detailed. Second, the paper develops an ethical theory of Reason-Responsiveness for Intelligence Augmentation Systems (RRIAS) that allows for the distribution of responsibility at the organizational level between operators and providers. RRIAS provides important guidance for firms to understand who should be held responsible for developing suitable corporate practices for the development and usage of IAS.

Ethical Risk Factors and Mechanisms in Artificial Intelligence Decision Making

While artificial intelligence (AI) technology can enhance social wellbeing and progress, it also generates ethical decision-making dilemmas such as algorithmic discrimination, data bias, and unclear accountability. In this paper, we identify the ethical risk factors of AI decision making from the perspective of qualitative research, construct a risk-factor model of AI decision making ethical risks using rooting theory, and explore the mechanisms of interaction between risks through system dynamics, based on which risk management strategies are proposed. We find that technological uncertainty, incomplete data, and management errors are the main sources of ethical risks in AI decision making and that the intervention of risk governance elements can effectively block the social risks arising from algorithmic, technological, and data risks. Accordingly, we propose strategies for the governance of ethical risks in AI decision making from the perspectives of management, research, and development.

Artificial intelligence in E-Commerce: a bibliometric study and literature review

This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China-based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.