Ethics lines and Machine learning: a design and simulation of an Association Rules Algorithm for exploiting the data

TL;DR

This paper proposes a method using association rules and the Apriori algorithm to analyze data from ethics lines, aiming to identify behavioral patterns and anomalies for better ethical compliance.

Contribution

It introduces a novel process for applying association rule mining to ethics line data, including a simulation demonstrating its potential and limitations.

Findings

Successfully identified behavior patterns and anomalies in synthetic data.

Demonstrated strengths and limitations of the Apriori algorithm in this context.

Provided a framework for ethical data analysis using data mining techniques.

Abstract

Data mining techniques offer great opportunities for developing ethics lines, tools for communication, participation and innovation whose main aim is to ensure improvements and compliance with the values, conduct and commitments making up the code of ethics. The aim of this study is to suggest a process for exploiting the data generated by the data generated and collected from an ethics line by extracting rules of association and applying the Apriori algorithm. This makes it possible to identify anomalies and behaviour patterns requiring action to review, correct, promote or expand them, as appropriate. Finally, I offer a simulated application of the Apriori algorithm, supplying it with synthetic data to find out its potential, strengths and limitations.