Hello, friends. Welcome to the session Conference 42 Beyond Algorithms, and today's session is Building Trust in AI Enhanced Public Sector Systems. This session is about revolutionizing government operations through ethical AI integration. So let's get started. Myself, I'm the presenter, Sanjiv pga, and I have 21 years of experience working in it sector across all domains. I have currently I'm working for Amazon, but prior to that I have worked in different domains, higher education for a huge part of my career and other domains such as retail and supply chain. And yeah I have done more than 12. A full lifecycle implementation of ERP systems and currently working on AI systems. So let's get started on the topic, the AI revolution in public sector. What we have seen over the years is that after the introduction of chat, GPT and ai, artificial intelligence. Artificial intelligence existed for many years, but with the introduction of chat, GPD Open ai, things have changed around us. Things like how we do our day-to-day work has changed a lot and how it has been revolution revolutionizing the public sector is what we are going to see here. Now, we have seen 60% reduction in pre previously incidents through enhanced advanced protective frameworks. And automated compliance monitoring, which has been a major pain point. Previously, we have also seen 75% administrative overhead elimination, streamlining workflows, workflows with intelligent process automation. We have also seen 82% enhanced service accuracy, delivering precise. Citizen Services. Three critical pillars of the AI in public sector is AI integration in public sector is the first one is comprehensive data privacy, which is of utmost important. We know data is the new gold, and the data privacy is of utmost importance. So comprehensive data privacy is one of the, key benefits of having the AI integration in public sector. This helps in adaptive protection framework that evolve with emerging threats. Second is ethical algorithm, all algorithmic fairness, which is robust bias detection and mitigation protocols across all demographics. The third one is system interability. Which is nothing but fully transparent decision processes with accessible explanations. Let's move on to the next slide now. Privacy protection framework. In previous pre protection framework, the three main components are comprehensive data audit, which is systematic in inventory and classification of all sensitive information sources. And repositories. The next is granular access control, which is multifactor authentication, like single sign-on and using mul multiple factors, authentication through phone or through text messages. Then role-based permissions with traction logging and verification. And the third one is advance. De-identification, which is automated personal identifiable information removal with cryptographic safeguards and compliance. Now let's look into each one of these algorithmic fairness implementation. This is all about defining the fairness metrics, establishing comprehensive standards tailored to each demographic group. With quantifiable equity measures. The next is measure outcomes. In Measure outcomes. What we look into is we conduct rigorous data analysis across all populations segments to identify disparate impacts and effectiveness. The third is stakeholder feedback. Where we look into integrating diverse community perspectives to validate real world equity outcomes and guide improvements. Now based on the feedback, we also adjust the models. We, so this is a ongoing circle, how we make sure that the algorithm is fairly implemented, which the last one is adjust model based on the feedback. So implement precise algorithm refinements based on famous findings to eliminate detected biases. Moving on to the next one. Now interpretability mechanisms, technical transparency, comprehensive system architecture, documentation. With accessible code repositories and detailed process workflows is critical to interpretability of the system of the AI interface. The second is decision explanation. Citizen friendly explanations that translate complex algorithmic decisions into understandable rationales with contextual examples. The third one is human oversight. Robust review channels with guaranteed human intervention options and clear appeal processes for all automated decisions. Next. Looking at public sector specific requirements. Now these are the three main pillars of the public sector requirements when it comes to AI integration. The first one is enhanced security, 40% higher resource allocation and higher resource allocation for critical protection. Hardened infrastructure against sophisticated cyber threats, data sovereignty, compliance across multiple jurisdictions. The next is frequent bias assessment, mandatory quarterly audits with documented remediation, multidisciplinary stakeholder review panels with citizen representation, transparent publication of assessment, findings and actions. The third one is regulatory compliance, which is harmonized alignment across federal, state, and local requirements. The third is second. Next is comprehensive audit trails for automated decisions, and the last one is structured reporting to overall committees and legislators and all other stakeholders. Now we have done some case studies and here are the outcomes of the case study of the AI integration in public sector. Now what we have seen is that the processing time is much less with the AI integration compared to the usual legacy in-house without AI integration. Next is error rates. The error rate is also significantly less. The third is public trust score. The public trust score, we see much higher than a regular compared to AI processes. And the fourth one, which is the biggest factor, is the service capacity. Now having an AI integrated public sector of businesses, we have seen the service capacity increase exponentially. Now implementation roadmap. The different steps of implementing the AI interface is in the public sector, specifically is assessment, ethics, audit of current systems to understand how the current system works, what are the challenges stakeholder need, analysis, technical readiness, evaluation. These are the three assessments that we need to do. The next is framework design. In Framework design, the first step is customize the ethical principles. Next is create governance structure, and the third is define success metrics. The next is implementation steps. The implementation steps are in a iterative fashion. It's phase technology integration, staff capability building and public communication campaign. So these are the steps for info implementation. Other implementation roadmap, success factors, there are four suc prime success factors. The first is inclusive development. Engage diverse stakeholders from day one that is critical to the success of the implementation. Next is. Capability building continuous staff ethics training program to make sure that they are able to support the application and support the business processes or the system once it is implemented. Third is transparent communication. Clear public explanation of AI usage and if there are any challenges there, address those challenges early, earlier in earlier stages of implementation. The last one would be adaptive governance, which is regular review and refinement cycles. This makes stay aligned with the customer needs. So adaptive governance is key to sustain development and success of the implementation. Now, key takeaways. Ethical drive drives performance organizations with strong ethical frameworks achieve superior technical outcomes. Next is trust is measurable. Public confidence metrics directly correlate with transparency mechanisms, so trust is definitely a measurable metric. So over here, unique public requirements, government AI demands specialized security and fairness protocols. And the fourth is continuous evolution. Making sure that regular maintenance and regular improvements are done to stay in stay in base with the developments happening. Moving to the next slide, and that was the last slide. So thank you so much for joining the session. I hope you have a wonderful day. Thank you.