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Vol. 4, Issue 12, Part A (2018)
Human-centric machine learning: Addressing user experience and ethical considerations
Human-centric machine learning: Addressing user experience and ethical considerations
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Abstract
As machine learning (ML) systems become increasingly intertwined with our daily lives, it is imperative to shift the focus from sheer algorithmic advancements to a more comprehensive consideration of the end user's experience and ethical implications. This review paper explores the multifaceted landscape of Human-Centric Machine Learning (HCML), delving into the pivotal intersection of user experience (UX) and ethical considerations. By synthesizing existing research and advancements, this paper aims to shed light on the challenges and opportunities inherent in fostering a harmonious relationship between artificial intelligence (AI) systems and human users.
As machine learning (ML) systems become increasingly intertwined with our daily lives, it is imperative to shift the focus from sheer algorithmic advancements to a more comprehensive consideration of the end user's experience and ethical implications. This review paper explores the multifaceted landscape of Human-Centric Machine Learning (HCML), delving into the pivotal intersection of user experience (UX) and ethical considerations. By synthesizing existing research and advancements, this paper aims to shed light on the challenges and opportunities inherent in fostering a harmonious relationship between artificial intelligence (AI) systems and human users.
The first section of the review underscores the pivotal role of UX in the adoption and acceptance of ML technologies. Analyzing user interactions, preferences, and expectations, we unravel the intricate dynamics that shape the user-machine relationship. Key UX factors such as transparency, interpretability, and user empowerment emerge as critical elements in designing ML systems that seamlessly integrate into human-centric environments.
Ethical considerations form the nucleus of the second section, wherein we dissect the ethical challenges posed by ML applications. Delving into issues of bias, fairness, accountability, and privacy, we highlight the ethical tightrope that ML practitioners must navigate. This section critically evaluates existing ethical frameworks and proposes a nuanced approach that addresses the unique ethical dimensions of HCML, acknowledging the dynamic nature of both technology and societal values.
The third section synthesizes insights from the preceding discussions, offering a comprehensive framework for developing and evaluating HCML systems. We introduce a user-centric ethical design paradigm that emphasizes continuous user engagement, feedback loops, and algorithmic transparency. This paradigm is underpinned by a commitment to safeguarding user rights, minimizing biases, and fostering trust in ML systems.
Pages: 65-69 | 121 Views 47 Downloads
How to cite this article:
Rishi Ashtana, Omprakash, Vikash. Human-centric machine learning: Addressing user experience and ethical considerations. Int J Appl Res 2018;4(12):65-69. DOI: 10.22271/allresearch.2018.v4.i12a.11467
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