Potential Abstract:
This empirical study explores the impact of distributed hashing algorithms on addressing algorithmic inequities in educational settings. With the proliferation of algorithmic decision-making systems in education, concerns have been raised regarding the perpetuation of systemic biases and inequities. Leveraging a mode of address that emphasizes distributed hashing techniques, this study investigates how such algorithms can mitigate the perpetuation of biases and promote equity in educational outcomes. Through a mixed-methods approach, including data analysis and stakeholder interviews, the study examines the effectiveness of distributed hashing in reducing algorithmic biases and promoting fair decision-making in educational contexts. Findings suggest that distributed hashing algorithms offer a promising avenue for addressing algorithmic inequities by enhancing transparency, accountability, and equity in the decision-making process. The implications of these findings for educational policy, practice, and research are discussed, highlighting the potential of distributed hashing algorithms to advance equity and fairness in algorithmic decision-making systems.
Potential References:
- Distributed pool mining and digital inequalities, From cryptocurrency to scientific research
- Effects of algorithmic flagging on fairness: Quasi-experimental evidence from Wikipedia
- Stream-aware indexing for distributed inequality join processing
- Efficient distributed locality sensitive hashing
- Demographic-reliant algorithmic fairness: Characterizing the risks of demographic data collection in the pursuit of fairness
