Artificial Intelligence (AI) has revolutionized various domains of software development, promising
solutions that can adapt and learn. However, the rise of AI systems has also been accompanied
by ethical concerns, primarily related to the unintentional biases these systems can inherit
during the development process. This thesis presents a thematic literature review aiming to
identify and examine the existing methodologies and strategies for preventing bias in iterative
AI software development.
Methods employed for this review include a formal search strategy using defined inclusion
and exclusion criteria, and a systematic process for article sourcing, quality assessment, and
data collection. 29 articles were analyzed, resulting in the identification of eight major themes
concerning AI bias mitigation within iterative software development, ranging from bias in data
and algorithmic processes to fairness and equity in algorithmic design.
Findings indicate that while various approaches for bias mitigation exist, gaps remain. These
include the need for adapting strategies to agile or iterative frameworks, resolving the trade-off
between effectiveness and fairness, understanding the complexities of bias for tailored solutions,
and assessing the real-world applicability of these techniques. This synthesis of key trends and
insights highlights these specific areas requiring further research.
