Hello everyone. Myself, Ji. I have more than 14 years of experience in CRM systems. Today we are going to discuss about the A power CM policy and how it is impacting between the innovation and surveillance. This presentation explains how a driven CRM system transform community policy while raising important questions about the server lines and similarities raise of a port policy. There are three main key impacted areas, widespread adoption, miserable impact, community focus in widespread adoption. A CRM system now manage millions subsidies, interactions annually across major metropol areas. Miserable impact departments report significance, improvement in response, coordination and feedback Collection and community focus. Neutral C enable more targeted community engagement and resource allocation. Benefits of a driven system. There are main benefits of the A driven system. Sentiment analysis, faster response, performance tracking, sentiment analysis, algorithm flag, community detections with high accuracy, allowing prevention accents. This means it collects the data from the different sources like 9 1 1 calls, case management, citizen compliance, and retracts. Ignore them. Faster response. Predictive alert systems have reduced response time in high risk areas for performance tracking officer engagement. Screen courts correlated with improved community satisfaction. There are success cases, 24% response time, average reduction in emergency response times across implementation departments, and 37% satisfaction increase in the community satisfactory scores where systems are transparent. 42% engagement, more citizen police and positive interaction reported in participating communities. The shadow side of the algorithm bias unfair targeting of certain communities. Documented case shows greater algorithm flagging in minority communities despite similar incident rates. The enforcement cycles are systems that learn from historical data, often perpetuate existing bias in policy patterns, and as well as limited warfare too. Public trust concerns the data collections, use policies and surveillance spheres when it comes to the data collections. Citizens often unaware of how the personal data is being gathered when citizens don't understand how decisions are made. The database collected and how it is going to be used. So in this case, the trash can be broken down, uses policies, unclear boundaries on how information affects polishing decisions, subline fears, especially in communities that have already exposed reward policy, the lack of transparency make it even harder to build trust community, a transparency framework. There are four core principles of community transparency framework. One is visibility documentation, participation review visibility. Public facing dashboard shows how algorithm make recommendations, the documentation, citizen accessible engagement logs, track all system guidance, interactions. That means they should be able to track their cases, how it was routed. Who was resolved on what basis? They resolved participation, community involvement in system design and implementation, like hosting public workshops at town halls, reviewing mandated third party algorithm audit conductor quarterly. Just like companies have independent financial audit, a system that how affect public should regularly fairness audits the framework results in pilot cities. Boston implementation Ocland Pilot Denver Adoption. Boston implementation Trust in policy technology increased 40 some percent after dashboard launches. Citizen complaints about technology misses dropped by 62%. The Ocland Pilot community or site committee review all algorithms changes public trust metrics improve 31% first year of the implementation, then adaption. Quarterly town hall discussion, CM Insights with distance due to this engagement with the police portal increased 83% after transparency measure key framework conference, public dashboards, technical documentation, citizen review boards impact as these are the four key components of framework. So when it comes to public dashboard, real time display of algorithm activity. And impact accessible to the all citizens technical documentation. Simplified explanation of how a systems make recommendation. Citizen review board community members participating in the quarterly algorithm reviews, impact assessment, regular evaluation of these system effects and different demo group groups. There are four ways of implementation pathways assessment, adaption, community engagement, continuous improvements. When it come to the assessment, conduct comprehensive audit of existing AI systems and gather technical community feedback on specific concepts. Adaption when it comes to the adaption. Implement technical modification to enhance algorithm transparency and establish clear accountability protocols. Community engagement. Create formal channels for citizens Prep citizens. Participants in the system review with the documented feedback integration process. Continuous implement, established quarterly independent audit and systematic implementation of evidence based on defense. There are proven stakeholders, benefits, law enforcement, community member, government officials in the law enforcement. Increasing community trust leads to better information sharing and cooperation officers report less stress from community tensions, community members. Greater understanding of how the data is used, reduced, and it reduces the fear of the serve lines. Visible feedback loops show input creates the real changes. Government officials reduce the tension between the police and community measure communities. Measurable improvement in public satisfaction with governance, technical recommendations, open source components, data minimization, bias testing, citizen control, so when it come to the open source components. Critical algorithms should be open for the public review while protecting security elements. The data minimization collect only necessary information with clear retention and detention policies, bias testing, regular testing, edged diverse dataset to identify that correct and algorithm biases. Citizen control allow individual to view and manage their data with reasonable security constraints. Balancing innovation rights. A CM system offers powerful tools for improving policy. Their benefits must be balanced with transparency and accountability. The community algorithm transparency framework provides a proven path forward. Thank you everyone.