Explaining the Challenges of Accountability in Machine Learning Systems Beyond Technical Obstacles

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Author(s): Srinivas Kumar Palvadi (Koneru Lakshmaiah Education Foundation, India)
Copyright: 2024
Pages: 28
Source title: Quantum Innovations at the Nexus of Biomedical Intelligence
Source Author(s)/Editor(s): Vishal Dutt (AVN Innovations Pvt. Ltd., India), Abhishek Kumar (Department of CSE, UIE, Chandigarh University, Punjab, India), Sachin Ahuja (Chandigarh University, India), Anupam Baliyan (Geeta University, India)and Narayan Vyas (AVN Innovations Pvt. Ltd., India)
DOI: 10.4018/979-8-3693-1479-1.ch003

Keywords: Knowledge Management and Library Science / Medical Diagnosis and Practice / Medical Information Science Reference / Quantum Information & Computing

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Abstract

The ability to make a note regarding machine learning systems decisions for people is becoming increasingly sought after, particularly in situations where decisions have significant repercussions for those affected and where capability in terms of maintaining is required. To increase comprehension based on referred to as “black box” mechanism, explaining ability is frequently cited as a technical obstacle in the design of ML systems and decision procedures. The quantities that ML systems aim to optimize must be specified by their users. This leads to the revealing of policy trade-offs that may have previously been hidden or implicit. Important decisions, as well as judgments, help what may need to be explicitly discussed in public debate as ML's use in policy expands.

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Explaining the Challenges of Accountability in Machine Learning Systems Beyond Technical Obstacles

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