Artificial Intelligence
Author-Ruchita Kulkarni
Kohinoor Business School
MMS-Roll no-0222030
Literature Review
Explaining Data-driven decisions made by AI Systems: The Counterfactual Approach.
FERNANDEZ-LORIA et al. (2022) This article provides counterfactual explanations for the judgements made by model-based AI systems. The counterfactual method which defines an explanation as a set of system data inputs that causally drives the decision and is irreducible. It indicates how this framework can be used to provide explanations for decisions made by general data-driven AI systems that can incorporate features with arbitrary data types and multiple predictive models, and proposes a heuristic procedure to find the most useful explanations depending on the context, before contrasting counterfactual explanations with methods that explain model predictions by weighting features according to their importance.
Algorithmic Transference: People Overgeneralize Failures of AI in the Government.
LONGONI, CHIARA et al. (2023) This article states that Artificial intelligence is transforming advertising theory and practice. However, while AI applications abound, it appears that not enough questions are being posed about the ontological, technical, and ethical implications of artificially intelligent advertising networks. This article emphasizes the importance of adopting a maieutic attitude for academics, practitioners, and other advertising stakeholders, including the AI-ad-consuming public
Drivers of salespeople’s AI acceptance: what do managers think?
CHEN, JING et al. (2022) This article analyses the drivers of salespeople’s AI acceptance from the perspective of managers. According to the article, perceived simplicity of use, self-efficacy, perceived management support, and digitalization are all favourably associated to salespeople’s adoption of AI. Additionally, digitalization mediates the relationship between salespeople’s selling capabilities and their acceptance of AI. According to the findings, managers must build adequate digital infrastructure, cultivate organisational support to motivate AI adoption and usage, develop professional training to educate salespeople on the proper use of AI, and reduce salespeople’s perceived risk of AI usage in order to incentivize AI acceptance.
To Be or Not to Be …Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents.
CHANDRA,SHALINI et al.(2022) According to this article, businesses are continually experimenting with cutting-edge technical solutions in order to offer continuous, timely, and effective client service. Companies have expressed a growing interest in creating and adopting conversational AI agents and chatbots, among other AI-based interactional technologies, to replace the requirement for human service agents to provide customer assistance. Customers must use and interact with these products effectively for conversational AI to have a positive business impact. This interaction also depends on how closely conversational AI resembles the people it is meant to replace. Therefore, in order to ensure seamless user contact, businesses need to understand what human-like traits and skills conversational AI bots should possess. The emphasis on “human-likeness” as a means of encouraging user engagement
Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey
LAMSAL, RABINDRA et al. (2023) This article states that the emergence of social media platforms offers an incredibly, infinitely rich source of general knowledge about the world, but the biggest issue is how to process and comprehend this unstructured, raw data in order to look beyond individual observations and understand the field of situation awareness. With a focus on microblog social media data and applications to situation awareness, this article offers a thorough survey of artificial intelligence research. It presents both classic work and cutting-edge methodologies in six thematic areas: crime, disasters, finance, physical environment, politics, and health and population. This article offers a fresh, unified methodological viewpoint, highlights significant findings and difficulties, and outlines future research paths.
AI in Finance: Challenges, Techniques, and Opportunities.
LONGBING CAO et al. (2023) This article uses applications of AI in finance to refer to approaches in financial businesses, with both traditional and modern AI techniques used to increasingly broader sectors of finance, economics, and society. In contrast to reviews that discuss the problems, aspects, and opportunities of finance that have benefited from specific or some new-generation AI and data science techniques, or the progress of applying specific techniques to resolving specific financial problems, this article provides a comprehensive and dense landscape of the overwhelming challenges, techniques, and opportunities of AIDS research in finance. Also detailed are the challenges of financial businesses and data, followed by a complete categorization and a dense summary of decades of AIDS research in finance.
Trustworthy Artificial Intelligence: A Review.
KAUR, DAVINDER et al. (2023) According to this article, algorithms and AI are having a significant impact. Systems are widely utilised in business, government, education, and the justice system. These systems have a lot of benefits, but they can also damage users occasionally, thus it is crucial to make them trustworthy, reliable, and safe. To make systems trustworthy, a number of conditions, including fairness, explainability, accountability, reliability, and acceptance, have been accepted. Through the prism of the literature, the survey analyses each of these various requirements. It gives a broad review of various strategies that can lessen the hazards associated with AI while boosting users’ confidence in and adoption of the systems.
Artificial Intelligence, Data-Driven Learning, and the Decentralized Structure of Platform Ecosystems.
CLOUGH et al. (2022) This article is about the automotive industry. Technology such as machine learning, artificial intelligence, and the Internet of Things, and began to develop data-driven decision-making techniques in order to compete in global smart manufacturing. This paper provides a revolutionary design framework for a completely automated smart vehicle manufacturing business that uses FAI for decision-making and SC rules for process execution and control. The proposed design includes a novel element called Trust Threshold Limit (TTL) that helps moderate the excessive use of embedded equipment, tools, energy, and cost functions, hence decreasing waste in manufacturing processes. This study focuses on the application of AI in decentralised Blockchain with smart contracts, as well as the company’s trading rules that efficiently handle market risk assessments during a socioeconomic crisis.
Combining Crowd & Machine Intelligence to detect false news on social media.
Xuan Wei et al. (2022) The dramatic increase in the distribution of false news on social media has had a significant impact on numerous domains, including news ecosystems, politics, economics, and public trust, particularly in light of the COVID-19 infodemic. This article proposes integrating two forms of scalable crowd judgements with machine intelligence to combat the false news crisis. Specifically, try to design a novel framework called CAND that first extracts relevant human and machine judgements from data sources such as news features and scalable crowd intelligence. The results also generate many valuable insights, such as the complementary value of human and machine intelligence, the possibility of using human intelligence for early detection, and the robustness of our approach to intentional manipulation….
When Conscientious employees meet Intelligent Machines: An Integrative approach inspired by Complementarity theory & role theory.
POK MAN TANG et al. (2022) This article is based on the observation that over the past century, conscientiousness has come to be seen as the most important trait for predicting performance. This consensus is due in part to these employees’ ability to work with traditional 20th-century technology. Such pairings balance the systematic nature of conscientious employees with the technology’s need for user input and direction to perform tasks–resulting in a complementary match. Hence, this article calls into question whether the conscientiousness of conscientious employees is the most important trait for predicting performance.
Conclusion:
AI usage is increasing day by day and people have started using it for decision making as AI is coming up with new features also managers must build digital infrastructure also AI have develop professional training to educate salespeople on the proper use of AI, and reduce salespeople’s perceived risk of AI usage in order to incentivize AI acceptance also because of it Customers interact with these products effectively for conversational AI to have a positive business impact AI tries to ensure seamless user contact, businesses need to understand what human-like traits and skills conversational AI bots. AI also offers a fresh, unified methodological viewpoint, highlights significant findings and difficulties, and outlines future research paths also AI is developing new financial methodologies that use particular strategies to address particular financial issues. Systems are widely used in business, government, education, and the justice system, and as a result, the use of AI has greatly increased in recent years. AI aims to speed up work processes by automating them and making them simpler for people to handle. Therefore, it is necessary for individuals to gain new skills and keep themselves up to date with emerging technologies. It is also highly important for humans to adapt to new AI technology.
References
CHANDRA, S.; SHIRISH, A.; SRIVASTAVA, S. C. . (2022) To Be or Not to Be …Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents. Journal of Management Information Systems, [s. l.], v. 39, n. 4, p. 969–1005, 2022. DOI 10.1080/07421222.2022.2127441.Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=cc7c5d9b-8ee0-3969-ac03-4789c92f6c2a. Acesso em: 12 maio. 2023.
CHEN, J.; ZHOU, W. (2022) Drivers of salespeople’s AI acceptance: what do managers think? Journal of Personal Selling & Sales Management, [s. l.], v. 42, n. 2, p. 107–120, 2022. DOI 10.1080/08853134.2021.2016058. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=7a67c90d-69c1-3ceb-a45b-d8545048278a. Acesso em: 9 maio. 2023.
CLOUGH, D. R.; WU, A. (2022) Artificial Intelligence, Data-Driven Learning, and the Decentralized Structure of Platform Ecosystems. Academy of Management Review, [s. l.], v. 47, n. 1, p. 184–189, 2022. DOI 10.5465/amr.2020.0222. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=dfa5158d-7e3c-3b12-bc54-b0b751bfd6ac. Acesso em: 9 maio. 2023.
FERNÁNDEZ-LORÍA, C.; (2022) Explaining Data-Driven Decisions Made by Ai Systems: The Counterfactual Approach. MIS Quarterly, [s. l.], v. 46, n. 3, p. 1635–1660, 2022. DOI 10.25300/MISQ/2022/16749. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=e416fea3-ec64-36a4-b248-e9b042baa9e9. Acesso em: 9 maio. 2023.
KAUR, D. et al. (2023) Trustworthy Artificial Intelligence: A Review. ACM Computing Surveys, [s. l.], v. 55, n. 2, p. 1–38, 2023. DOI 10.1145/3491209. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=321fbe9b-b63c-3513-9da4-23e1f32f187f. Acesso em: 9 maio. 2023.
LAMSAL, R.; HARWOOD, A.; RODRIGUEZ READ, M. (2023) Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey. ACM Computing Surveys, [s. l.], v. 55, n. 4, p. 1–38, 2023. DOI 10.1145/3524498. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=ea09c89a-707e-3b17-a3a1-5d797803f0b3. Acesso em: 9 maio. 2023.
LONGBING CAO et al. (2023) AI in Finance: Challenges, Techniques, and Opportunities. ACM Computing Surveys, [s. l.], v. 55, n. 3, p. 1–38, 2023. DOI 10.1145/3502289. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=1123173b-539d-3640-ba75-fc81274b4c50. Acesso em: 9 maio. 2023.
LONGONI, C.; CIAN, L.; KYUNG, E. J. (2023) Algorithmic Transference: People Overgeneralize Failures of AI in the Government. Journal of Marketing Research (JMR), [s. l.], v. 60, n. 1, p. 170–188, 2023. DOI 10.1177/00222437221110139. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=42fe9db6-59ac-338f-8d7d-e0974245984f. Acesso em: 9 maio. 2023.
POK MAN TANG et al. (2022) When Conscientious Employees Meet Intelligent Machines: An Integrative Approach Inspired by Complementarity Theory and Role Theory. Academy of Management Journal, [s. l.], v. 65, n. 3, p. 1019–1054, 2022. DOI 10.5465/amj.2020.1516. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=5775e17c-264d-34d5-8ee3-4850c5736282. Acesso em: 9 maio. 2023.
XUAN WEI et al. (2022) Combining Crowd and Machine Intelligence to Detect False News on social media. MIS Quarterly, [s. l.], v. 46, n. 2, p. 977–1008, 2022. DOI 10.25300/MISQ/2022/16526. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=a93fb483-f5fb-3cc4-99d7-393a09918ac4. Acesso em: 9 maio. 2023.