Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability?

Area of Science:

• Computer Science
• Ethics
• Law

Background:

• Machine learning algorithms increasingly influence critical decisions.
• Current algorithmic systems often lack transparency, hindering accountability.
• The need for transparency in artificial intelligence (AI) decision-making is a growing concern.

Purpose of the Study:

• To investigate whether transparency can restore accountability in algorithmic decision-making systems.
• To examine the objections and feasibility of full transparency for AI systems.
• To propose alternative approaches for enhancing understanding of algorithmic processes.

Main Methods:

• Analysis of objections to full transparency, including privacy, gaming the system, and competitive edge.
• Evaluation of the effectiveness of transparency in achieving accountability.
• Exploration of interpretability methods for machine learning models.

Main Results:

• Full transparency for the public is generally not advisable due to significant risks.
• Oversight bodies may require full transparency as a feasible option for accountability.
• Sophisticated algorithms are often inherently opaque, limiting answerability.

Conclusions:

• Complete public transparency for algorithmic systems is currently not feasible or advisable.
• Transparency for specialized oversight bodies is a more practical approach.
• Prioritizing the interpretability of machine learning models is crucial for understanding and accountability.