Navigating the ethical landscape of artificial intelligence: Challenges, frameworks, and responsible deployment
- Title
- Navigating the ethical landscape of artificial intelligence: Challenges, frameworks, and responsible deployment
- Creator
- Nanjundan P.; Indu P.V.; Thomas L.
- Description
- In artificial intelligence (AI), machine learning (ML) has become a game-changing concept that allows systems to learn from experience and get better without explicit programming. This chapter explores the main ideas, techniques, and applications of ML, offering a succinct introduction to the field. The first step in the process is to gain a basic understanding of supervised learning, which is the process by which algorithms learn to make predictions or judgements from labelled training data. Next, we introduce unsupervised learning, which emphasizes finding patterns in unlabelled data and frequently results in interesting findings and clustering. To emphasize the importance of reinforcement learning in decision-making processes, the paradigm is presented where agents learn by interacting with an environment and receiving feedback. Ideas related to ML, such as feature engineering, model assessment, and the balance between variance and bias, are discussed. The significance of quality data in ML applications is emphasized, along with the impact of data pretreatment on model performance. It also clarifies how neural networks, a branch of ML, simulate the workings of the human brain. The ability of deep learning, a branch of ML that makes use of multi-layered neural networks, to handle challenging tasks such as speech and picture recognition is being investigated. In order to emphasize the necessity of responsible ML model deployment and usage, practical factors are emphasized, including the significance of ethical considerations and responsible AI. The final section of the chapter offers a preview of MLs future, discussing issues and trends that practitioners and researchers should be aware of. This chapter essentially functions as a thorough introduction to ML principles, providing an overview of the wide range of ML approaches, applications, and ethical issues that support the technologys transformative potential across a range of industries. 2025 selection and editorial matter, G. Sucharitha, Anjanna Matta, M. Srinivas and Sachi Nandan Mohanty; individual chapters, the contributors.
- Source
- Artificial Intelligence Technologies for Engineering Applications, pp. 1-13.
- Date
- 2025-01-01
- Publisher
- CRC Press
- Coverage
- Nanjundan P., Department of Data Science, CHRIST University, Maharashtra, Lavasa, India; Indu P.V., Department of Data Science, CHRIST University, Maharashtra, Lavasa, India; Thomas L., CHRIST University, Maharashtra, Lavasa, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-104026546-8; 978-103276581-5
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Nanjundan P.; Indu P.V.; Thomas L., “Navigating the ethical landscape of artificial intelligence: Challenges, frameworks, and responsible deployment,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/17518.