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Introduction

In recent years, automated decision systems have become increasingly prevalent in various industries and applications, ranging from finance and healthcare to criminal justice and social media. These systems, which use algorithms to process data and make decisions, have the potential to greatly improve efficiency and accuracy. However, concerns have been raised about the lack of transparency and accountability in these systems, leading to the need for algorithm auditing and provenance tracking.

Algorithm auditing involves examining the algorithms used in automated decision systems to ensure that they are fair, transparent, and free from biases. Provenance tracking, on the other hand, involves keeping a record of the data and processes that were used to make a decision, allowing for greater transparency and accountability.

This thesis aims to investigate the importance of algorithm auditing and provenance tracking in automated decision systems, as well as the challenges and opportunities that come with implementing these practices. By understanding how these systems work and the potential risks they pose, we can develop better strategies for ensuring that they are ethical and reliable.

Table of Contents

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of automated decision systems
2.2 Importance of algorithm auditing and provenance tracking
2.3 Challenges in implementing algorithm auditing and provenance tracking
2.4 Ethical considerations in automated decision systems
2.5 Legal and regulatory frameworks related to algorithm auditing and provenance tracking
2.6 Case studies of algorithmic bias and discrimination
2.7 Existing approaches to algorithm auditing and provenance tracking
2.8 Tools and techniques for auditing algorithms and tracking provenance
2.9 Future directions in algorithm auditing and provenance tracking
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 Sampling strategy
3.5 Ethical considerations
3.6 Pilot study
3.7 Validity and reliability
3.8 Limitations of research methodology

Chapter 4: Discussion of Findings
4.1 Analysis of algorithm auditing practices
4.2 Evaluation of provenance tracking techniques
4.3 Comparison of different approaches
4.4 Recommendations for improving algorithm auditing and provenance tracking
4.5 Implications for policy and practice
4.6 Future research directions

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Conclusions
5.3 Contributions to the field
5.4 Limitations of the study
5.5 Recommendations for further research
5.6 Final remarks

Thesis Overview on Algorithm auditing and provenance tracking of automated decision systems

Automated decision systems are playing an increasingly important role in various industries, with algorithms being used to process data and make decisions at a scale that was previously unimaginable. The use of these systems has the potential to greatly improve efficiency and accuracy, but concerns have been raised about the lack of transparency and accountability in how decisions are made.

Algorithm auditing and provenance tracking are two practices that aim to address these concerns by examining the algorithms used in automated decision systems and keeping a record of the data and processes that were used to make a decision. This thesis will explore the importance of algorithm auditing and provenance tracking, the challenges and opportunities that come with implementing these practices, and the implications for policy and practice.

Through a comprehensive literature review, this thesis will provide an overview of automated decision systems, discuss the importance of algorithm auditing and provenance tracking, examine the challenges in implementing these practices, and review existing approaches and tools. The research methodology will be carefully designed to collect and analyze data related to algorithm auditing and provenance tracking, and the findings will be discussed in depth in Chapter 4.

By the end of this thesis, readers will have a deeper understanding of the ethical considerations, legal and regulatory frameworks, and potential risks associated with automated decision systems. They will also gain insights into how algorithm auditing and provenance tracking can help ensure that these systems are fair, transparent, and free from biases. The conclusions drawn from this research will provide recommendations for improving algorithm auditing and provenance tracking, as well as directions for future research in this field.

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