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Here Is One I Made Earlier: Machine Learning Deployment
Published in Jesús Rogel-Salazar, Advanced Data Science and Analytics with Python, 2020
We can think of different categories of data products including: Raw data – Making data available though appropriate pipelines.Derived data – Processing and calculating fields that can be used later in the funnel.Algorithms – This has been the main topic of our discussions. We pass some (raw/derived) data through an algorithm to obtain understanding useful to the users.Decision support – Enabling the user to make a decision based on better information. The aim is to provide the information in an easy way to be consumed. These are the products we will talk about in this chapter.Automated decision-making – This is closer to the overall goal of artificial intelligence where the process of making a decision is delegated to the machine without user intervention.A categorisation of data products in increasing order of complexity.
Enterprise Systems
Published in Vivek Kale, Enterprise Process Management Systems, 2018
Data warehouses and knowledge management systems should enable future ERP systems to support more automated business decision-making and should be helpful in the complex decision-making needed in the context of fully integrated supply chain management. More automated decision-making in both front-office and back-office systems should eliminate/minimize human variability and error, greatly increase decision speed, and hopefully improve decision quality. Business intelligence tools, which are experiencing a significant growth in popularity, take internal and external data and transform it into information used in building knowledge that helps decision-makers to make more informed decisions.
Decision Procedures
Published in Satya Prakash Yadav, Dharmendra Prasad Mahato, Nguyen Thi Dieu Linh, Distributed Artificial Intelligence, 2020
In simpler terms, automated decision-making can be defined as “making a decision directly with the machine without any human intervention.” No human involvement or interference is there in any of the processes in between. In other words, it can be described as a machine gathering large amounts of data at a single place, getting it processed with the help of some algorithms, and then using those experiences and data knowledge in making its own decision.
Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities
Published in Information Systems Management, 2022
Christian Meske, Enrico Bunde, Johannes Schneider, Martin Gersch
Different risks exist regarding the use of AI systems. A major potential problem is “bias”, which comes in different facets. In certain situations, humans have a tendency to over-rely on automated decision-making, called “automation bias”, which can result in a potential failure to recognize errors in the black box (Goddard et al., 2012). As an example, medical doctors ignored their own diagnoses, even when they were correct, because their diagnosis was not recommended by the AI system (Friedman et al., 1999; Goddard et al., 2011). Furthermore, automation bias can foster the process of “deskilling”, either because of the attrition of existing skills or due to the lack of skill development in general (Arnold & Sutton, 1998; Sutton et al., 2018). Such problems highlight the overall risk of inappropriate trust of humans toward AI (Herse et al., 2018).
Human, Do You Think This Painting is the Work of a Real Artist?
Published in International Journal of Human–Computer Interaction, 2023
Jeongeun Park, Hyunmin Kang, Ha Young Kim
However, when the AI misjudged a fake as authentic, people were affected by the AI’s determination result and more often misjudged the authentic work as fake. In this regard, AI’s human decision-making support with high discrimination performance rather than automated decision-making by AI will likely become critical. Conditional collaboration between AI and humans is in the same vein as that discussed in previous studies, and as AI judgment results do not always lead to more accurate results, a higher-performance AI model is required for the combined effect of humans and AI (Gaube et al., 2021; Jacobs et al., 2021; Vaccaro & Waldo, 2019). Therefore, AI's high accuracy is expected to be of considerable importance to humans in AI-assisted decision-making in the future.