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on Artificial Intelligence |
| By: | Ellenrieder, Sara; Kallina, Emma Marlene; Pumplun, Luisa; Gawlitza, Joshua; Ziegelmayer, Sebastian; Buxmann, Peter |
| Abstract: | The deployment of machine learning (ML)-based decision support systems (DSSs) in high-risk environments such as radiology is increasing. Despite having achieved high decision accuracy, they are prone to errors. Thus, they are primarily used to assist radiologists in their decision making. However, collaborative decision making poses risks to the decision maker, e.g. automation bias and long-term performance degradation. To address these issues, we propose combining findings of the research streams of explainable artificial intelligence and education to promote human learning through interaction with ML-based DSSs. We provided radiologists with explainable vs non-explainable decision support that was high- vs low-performing in a between-subject experimental study to support manual segmentation of 690 brain tumor scans. Our results show that explainable ML-based DSSs improved human learning outcomes and prevented false learning triggered by incorrect decision support. In fact, radiologists were able to learn from errors made by the low-performing explainable ML-based DSS. |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:141971&r=ain |
| By: | Nicolas Ameye; Jacques Bughin; Nicolas van Zeebroeck |
| Abstract: | This paper studies the diffusion of artificial intelligence (AI) within firms, from exploration to local adoption to full-scale exploitation. The optimal timing of technology adoption represents a balance between preempting the risk of competition and time needed to acquire necessary complements, to ensure a successful return on investment. We formulate and test the idea that this balance changes along the adoption curve from experimentation to exploitation. We first model the decision of a firm facing Cournot competition to explore then exploit AI and assess the role of a variety of internal complements (technological and organizational) as well as competitive rivalry in these processes. Based on this theoretical model, a reduced form model of internal diffusion of AI is then estimated. Three results emerge: (1) rivalry triggers a competitive technology race that prevails in the exploitation more than in the exploration phase; (2) direct AI complements (such as machine learning) favor both adoption and exploitation, while indirect complements (such as cloud and big data) matter more for the experimentation than for the exploitation phase; (3) organizational complements are important for exploiting AI at scale, while technological ones drive exploration and adoption more than exploitation. |
| Keywords: | Artificial Intelligence, Adoption, Exploitation, Diffusion, Competition, Complements |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:ict:wpaper:2013/368095&r=ain |
| By: | Julien Gosse; Chris CM Forman; Nicolas van Zeebroeck |
| Abstract: | Today’s world is profoundly transformed by two major revolutions. The first one is related to the sustainability transition, while the other relates to the digital transformation of our economies and societies. Recently, regions such as Europe have put the integration between these two transformations high on their agenda. A term was coined for it: the twin transition. In a nutshell, the twin transition aims at leveraging the potential of technologies such as Cloud technologies, Internet of Things (IoT) and Artificial Intelligence (AI) to tackle the sustainability transition. |
| Keywords: | Digital Transformation, Environmental Management, Sustainability Practices, Green ICT, ICT for Green |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:ict:wpaper:2013/368092&r=ain |
| By: | Ahmad Haidar (LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], IMT-BS - MMS - Département Management, Marketing et Stratégie - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris]) |
| Abstract: | The rapid integration of Artificial Intelligence (AI) into various sectors has yielded significant benefits, such as enhanced business efficiency and customer satisfaction, while posing challenges, including privacy concerns, algorithmic bias, and threats to autonomy. In response to these multifaceted issues, this study proposes a novel integrative theoretical framework for Responsible AI (RAI), which addresses four key dimensions: technical, sustainable development, responsible innovation management, and legislation. The responsible innovation management and the legal dimensions form the foundational layers of the framework. The first embeds elements like anticipation and reflexivity into corporate culture, and the latter examines AI-specific laws from the European Union and the United States, providing a comparative perspective on legal frameworks governing AI. The study's findings may be helpful for businesses seeking to responsibly integrate AI, developers who focus on creating responsibly compliant AI, and policymakers looking to foster awareness and develop guidelines for RAI. |
| Keywords: | AI for Sustainability, AI Governance, Digital Strategy, Digital Transformation, Integrative Framework, IT Governance, Responsible AI, Responsible Innovation Management |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04364949&r=ain |
| By: | Charles Hoffreumon; Chris CM Forman; Nicolas van Zeebroeck |
| Abstract: | While the adoption and use of artificial intelligence (AI) are now significant, overall adoption rates remain low (Kazakova et al 2020; McElheran et al 2023; Zolas et al 2020). One recent line of work has argued that AI represents a type of general-purpose technology (GPT) (Bresnahan and Trajtenberg 1995; Trajtenberg 2019; Furman and Seamans 2019), and so requires significant downstream innovation to adapt general solutions to unique user needs (Brynjolfsson, Rock, and Syverson 2019, 2021; Goldfarb, Taska, and Teodoridis 2023). At the firm level, this will require a combination of business process innovation but also the development of software that will fit the unique needs of firms. This need for complementary adaptation and innovation has historically proven to be difficult (Bresnahan and Greenstein 1996). |
| Keywords: | Artificial Intelligence, Technology Adoption, Complementarities |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:ict:wpaper:2013/368089&r=ain |
| By: | Frank Xing |
| Abstract: | Large language models (LLMs) have drastically changed the possible ways to design intelligent systems, shifting the focuses from massive data acquisition and new modeling training to human alignment and strategical elicitation of the full potential of existing pre-trained models. This paradigm shift, however, is not fully realized in financial sentiment analysis (FSA), due to the discriminative nature of this task and a lack of prescriptive knowledge of how to leverage generative models in such a context. This study investigates the effectiveness of the new paradigm, i.e., using LLMs without fine-tuning for FSA. Rooted in Minsky's theory of mind and emotions, a design framework with heterogeneous LLM agents is proposed. The framework instantiates specialized agents using prior domain knowledge of the types of FSA errors and reasons on the aggregated agent discussions. Comprehensive evaluation on FSA datasets show that the framework yields better accuracies, especially when the discussions are substantial. This study contributes to the design foundations and paves new avenues for LLMs-based FSA. Implications on business and management are also discussed. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.05799&r=ain |