Tools adapted to Ethical Analysis of Data Bias

Abstract

Data Bias, a bias embedded in data during collection, storing, and use, and in the apps used by a human, is an emerging issue of data privacy exemplified by Artificial Intelligence bias (AI bias). This issue is becoming gradually an added vulnerability to data ethics, an added threat to data security, and an added burden to data protection. It has an effect to induce a reduction in data protection expenditure, and is crucial to the success of any creative endeavours in the data-driven technology-intensive era, including Engineering, exemplified by AI bias, and AI bias is bias created when biased data creeps in during design, development, and training of AI algorithms. AI indeed culminates in a phenomenon in which the populace jumps, yet a sober minority steers away from because of the pervasive cyber-threats that AI bias raises. At issue is not data bias per se, nor the multi-dimensional issues induced by human bias, which are usually complex and slippery, but a need for a method to enable a holistic view covering the technical, financial, legal, social, ethical, and ecological aspects of a given problem, action, policy, or decision. Recommendable is a method composed of the Ethical Matrix Algorithm and Hexa-dimension Metric Algorithm (Lee, forthcoming) based respectively on the Ethical Matrix and Hexa-dimension Metric (Lee, 2021).