AI and Data Utilization What are stumbling points What is Ethics Framework Recently, there has been an increase in efforts to utilize data acquired through business to provide new value to people and society. However, in data utilization, actions taken with good intentions can often become problematic. Even if there are no legal issues, situations that cause public backlash may happen. These problematic factors are referred to as "stumbling points". When attempting new things through AI and data utilization, it is important to find stumbling points in advance and take measures to address them. However, simply confirming based on legal regulations is insufficient, and it is necessary to find them from an ethical standpoint. The "Ethics Framework" is the most appropriate content in modern times, which analyzes recent problem cases and organizes them into an easy-to-understand system. The Ethics Framework is organized into five ethical items in the figure on the right. Below the ethical items are 14 stumbling points and 49 checklist items. 1 Propriety 2 Accountability 3 Fairness 4 Safety and security 5 Information protection
SAFETY CAMERAS - Pre-AI Operational Verification Based on Ethical Framework - Efforts of Kakogawa City Since FY 2017, approximately 1,500 SAFETY CAMERAS have been installed. Installed mainly in the school commuting routes, school surroundings, and park surroundings of elementary schools. Considering residents' privacy. Strengthening communities through local monitoring of children and the elderly. Achievements so far Crime prevention effect and early resolution of crimes. The number of crimes in the city has been less than half in about four years since the installation. In FY 2022, 150 AI-enabled Advanced SAFETY CAMERAS were installed. The aim is to further create a safe and secure town. Detecting unusual sounds such as screams, and warning the surrounding area using a speaker and a Rotating light. Detecting dangerous driving behavior of automobiles and alerting pedestrians through warning lights. Automatically grasping traffic and pedestrian flow data and utilizing it for city planning considerations. Verification through Ethics Framework. Verification results. By using the 14 "stumbling points" and 49 check items, we can comprehensively check the response situation from an ethical perspective. Confirmed that appropriate measures have been taken for all check items. Flow from data acquisition to utilization Camera body Intermediate server Data linkage platform Acquired data New value Image / Audio Unusual sound Vehicle approach pedestrian traffic Town planning considerations Image / Audio Used for AI training for unusual sound detection. Crime prevention (Rotating lights and speakers) Accident prevention (Rotating lights and speakers) Unusual sound Vehicle approach pedestrian traffic Non-processed data *To be discarded Processed data ※No personal information included Derived data
1.1 Satisfaction with data utilization 1.2 Easing concerns with providing personal information 1.3 Balance between the risks and benefits of providing personal information 2 Accountability 2.1 Explanation of derived data results 2.2 Potential for biases and errors in derived data 3 Fairness 3.1 Possibility of feelings of unfairness and discrimination based on derived data 4 Safety and security 4.1 Potential impact of derived data on individuals, organizations, and local communities 4.2 Potential for abuse 4.3 Data stored/managed in Japan 5 Information protection 5.1 Information security measures 5.2 Control at provision/publishing point 5.3 Appropriate deletion of collected data 5.4 Identification of individuals by matching with other data 5.5 Infringement of rights relating to data provided stumbling points Ethics stumbling points 1 Propriety 1.1 Satisfaction with data utilization 1.1.1 [Propriety of purpose] Is the purpose of data utilization generally appropriate? 1.1.2 [Propriety of collection] Is the data collected for the purpose appropriate in general? 1.1.3 [Propriety of generation method] Is the method for generating derived data from collected data generally considered appropriate? 1.1.4 [Propriety of classification or meaning] Are the types and implications of the generated derived data generally considered appropriate? 1.1.5 [Propriety of how data should be used] Is the use of derived data generally considered appropriate? 1.1.6 [Explanation of propriety] Are you explaining to data providers so that they understand the propriety of these practices? checklist items The Ethics Framework 14 points 49 items
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