دانلود مقاله به سوی حکمرانی هوش مصنوعی: شناسایی بهترین روش ها و موانع و نتایج بالقوه
خلاصه
1. معرفی
2 پس زمینه
3 روش شناسی
4 تجزیه و تحلیل مورد
5 بحث
ضمیمه
اعلامیه ها
منابع
Abstract
1 Introduction
2 Background
3 Methodology
4 Case Analysis
5 Discussion
Appendix
Declarations
References
چکیده
در سال‌های اخیر هوش مصنوعی (AI) به عنوان یک فناوری با پتانسیل فوق‌العاده برای شرکت‌ها در کسب مزیت عملیاتی و رقابتی دیده شده است. با این حال، با وجود استفاده از هوش مصنوعی، کسب‌وکارها همچنان با چالش‌هایی مواجه هستند و نمی‌توانند فوراً به دستاوردهای عملکردی پی ببرند. علاوه بر این، شرکت ها باید سیستم های هوش مصنوعی قوی را معرفی کنند و خطرات هوش مصنوعی را کاهش دهند، که بر اهمیت ایجاد شیوه های حاکمیت هوش مصنوعی مناسب تأکید دارد. این مطالعه، بر اساس تجزیه و تحلیل موردی مقایسه ای از سه شرکت در بخش انرژی، چگونگی اعمال حاکمیت هوش مصنوعی را برای ترویج توسعه برنامه های کاربردی هوش مصنوعی قوی که اثرات منفی ایجاد نمی کند، بررسی می کند. این مطالعه نشان می‌دهد که کدام شیوه‌ها برای تولید دانشی قرار می‌گیرند که به تصمیم‌گیری کمک می‌کند و در عین حال بر موانع با اقدامات توصیه‌شده منجر به نتایج دلخواه غلبه می‌کند. این مطالعه با کاوش در ابعاد اصلی مرتبط با حاکمیت هوش مصنوعی در سازمان‌ها و با کشف شیوه‌هایی که زیربنای آن‌ها هستند، کمک می‌کند.
Abstract
In recent years artificial intelligence (AI) has been seen as a technology with tremendous potential for enabling companies to gain an operational and competitive advantage. However, despite the use of AI, businesses continue to face challenges and are unable to immediately realize performance gains. Furthermore, firms need to introduce robust AI systems and mitigate AI risks, which emphasizes the importance of creating suitable AI governance practices. This study, explores how AI governance is applied to promote the development of robust AI applications that do not introduce negative effects, based on a comparative case analysis of three firms in the energy sector. The study illustrates which practices are placed to produce knowledge that assists with decision making while at the same time overcoming barriers with recommended actions leading to desired outcomes. The study contributes by exploring the main dimensions relevant to AI’s governance in organizations and by uncovering the practices that underpin them.
Introduction
As businesses adopt Artificial Intelligence (AI), they are faced with new value propositions, but they also have to deal with new challenges, such as reducing the gap between intent and action(Amershi et al., 2019; Enholm et al., 2021; Mishra & Pani, 2020). Artificial intelligence has been perceived as a tool with which we can layer many different functions or as a solution to problems that are beyond the ability of traditional applications to solve. (Smuha, 2019). In order to gain a competitive advantage over their competitors (Raisch & Krakowski, 2021), businesses have implemented and deployed AI solutions to automate their processes, increase efficiency and reduce costs (Frank et al., 2019; Gregory et al., 2020). To achieve these goals, AI governance is essential. According to Butcher and Beridze (2019), AI governance “can be characterized as a variety of tools, solutions, and levers that influence AI development and applications”. Yet, further research is needed to better determine how AI Governance can be introduced into a company and whether AI governance can assist a company in achieving its objectives.
While AI has the potential to generate business value in terms of performance, productivity and effectiveness, it is not autonomous, as it works in concert with human capabilities (Zhang et al., 2021). Consequently, organizational capabilities are the results of combining and deploying multiple complementary resources within a firm to achieve competitive advantage (Mikalef & Gupta, 2021). When a firm optimizes its firm-level resources and adopts AI technological innovations, it can enhance its transformed projects' business value which drives business value and impacts performance (Wamba-Taguimdje et al., 2020). Simultaneously, the AI algorithms can be considered performative in the sense that they assist in decision-making, the extent to which their use can form organizational processes, or even take autonomous decisions (Faraj et al., 2018; Grønsund & Aanestad, 2020) that leads to new organization capabilities through AI. The use of AI, for instance, could create more substantial customer acquisition or higher customer lifetime value and lower operating costs or reduce credit risk.
Discussion
In this study, we set out to explore the underlying activities that comprise an organization’s AI governance. Specifcally, we built on the prior distinction between structural, relational, and procedural dimensions of governance in order to understand how organizations are planning around their AI deployments. Through a multi-case study of three organizations that have been using AI for several years, we conducted a series of interviews with key respondents and identifed a set of activities that were relevant under each of the three dimensions, as well as challenges they faced during deployments of AI and how they managed to overcome them. Our analysis essentially points out the various obstacles that AI governance is oriented to overcoming, and the mechanisms employed to operationalize them.
Specifcally, we fnd that the obstacles that are identifed during the process of deploying AI are observable at diferent phases and concern diferent job roles. When it comes to difcult management responsibilities that a business owner must do, AI solutions can always provide a variety of responses and probabilities for each of these alternatives. However, AI lacks the ability to make decisions in specifc contexts. To make the ultimate decision, a business owner or manager must employ intuition to reconcile the choices provided by AI (Kar & Kushwaha, 2021). In addition, they span various levels of analysis, from the personal, such as fear of AI and reluctance of employees to adopt it, to organizational-level ones, such as organizational directives on how to comply with laws and regulations. What is more, the study reveals not only that AI governance is a multi-faceted issue for organizations but that it spans multiple levels, therefore requiring a structured approach when it is deployed. In addition, different concerns emerge at diferent phases of AI projects, so AI governance also encapsulates a temporal angle in its formation and deployment.