CDAO Government schedule

• Driving innovation and boosting efficiency through data-driven insights
• Optimizing business operations with data-driven process improvements
• Promoting transparency: Building trust and accountability between government and residents

• Discuss how the accuracy, completeness, and consistency of data impact AI performance
• Examine the ethical implications of data collection and usage, including privacy concerns and bias mitigation
• Explore how large datasets can enhance AI capabilities and the tools used to manage and process big data

• Improve communication between citizens & stakeholders with the use of new technology
• Better collect & analyse data to enhance decision-making, planning and policy formulation
• Learn to streamline your agency’s operations to reduce costs and increase efficiency

• Enabling transformation and decision making with trusted data
• Ensuring data quality, consistency, and integration
• Improving the quality and speed of government decision-making at scale by automating administrative tasks

• Communicating the purpose of data collection to citizens
• Assessing potential privacy risks and identifying mitigation measures
• Avoiding unnecessary or excessive data collection

• How can self-regulation in AI be a strong approach to better decision making  
• Identifying high-impact areas for AI implementation
• Strategies for integrating AI into public services
• Measuring ROI from AI investments

• The need for data officers to adopt strong security and governance processes
• How innovative technologies such as AI and Machine Learning can be leveraged to modernize government data architectures?
• Prioritize building an efficient organization by establishing clear data governance policies to promote data driven decisions at all levels

• Overview of AI applications in the public sector
• Benefits of AI for government operations and services
• Ethical considerations and bias in AI algorithms
• Transparency and accountability in AI decision-making

• Aligning your data & analytics strategy with cross-government & organization level priorities and goals
• Balancing strategic approaches to data analytics with the changing priorities of the wider organization
• How innovative technologies such as AI and Machine Learning can be leveraged to modernize government data architectures?

• Program impact measurement through personnel and data reporting requirements
• The use of evidence and data-driven decisions in policymaking & agency improvement efforts
• Utilizing data to validate a course of action before decision making

• Developing a workforce with strong analytical skills
• Investing in data literacy training for employees at all levels
• Reinforcing the importance of innovation

The widespread adoption of generative AI has excited the masses across nearly every industry regardless of company size and tenure. However, history has taught us that even the most transformative solutions can be rejected when its return on value is not clearly articulated. In this session, we will discuss the rise of generative AI, discriminative AI’s place in today’s workplace, pitfalls for AI, and how to maximize value for AI today and into the future.

• Forecast outcomes needed to help identify challenges and stay ahead of issues
• Implement AI to gain faster and more comprehensive approach to make better decision based polices
• Identify the patterns within communities or cities to address and act larger issues

• Defining the state of the possible – how mature are AI technologies currently, what are the realistic use-cases, and what is hype?
• Getting beyond the buzzword to identify what opportunities AI presents to Governmental organizations seeking to improve their delivery of services?
• Avoiding bias in data sets, algorithms, and machine learning models to ensure citizens are treated fairly & equitably

• Discuss when CDOs and CIOs in Federal Agencies must publicly report a data inventory used in AI systems
• Examine what details Federal Agencies must disclose for transparency and accountability under the law
• Analyze the potential positive and negative implications of sharing data used for AI generated analytics

• Techniques for maintaining data accuracy and reliability
• Ethical AI practices in the public sector
• Building public trust through AI governance
• Balancing innovation and ethics in AI development

• Enhancing the effectiveness and efficiency of each stage of policymaking
• Establishing robust governance structures from the outset of the AI/CX journey 
• Building consistent CX standards across government agencies and services

• Strategies for fostering a culture that values data and AI-driven decision-making across all levels of the organization
• How to ensure AI projects are aligned with the organization’s overall objectives and deliver measurable value
• Ensuring that AI initiatives adhere to ethical standards and promote fairness, transparency, and accountability

• With data & analytics talent in such demand, in what ways can the public sector compete with the private sector?
• Is the idea of public service our secret weapon, and how can we best deploy it? How can we ensure employees see the impact of their work, to help boost retention?
• How far is it possible to mitigate the need to recruit new talent by upskilling the employees we already have?
• What measures would help reduce the skills shortage to improve the issue in the long term?

• Where do you find new potential collaborators to best enable innovation?
• What common practical, cultural, and legal issues can make collaboration difficult, and how can these be overcome?

• In what ways can we use our data to bring a higher level of accountability to the public sector?
• How can we utilize data to deliver enhanced levels of transparency to the public and all stakeholders?
• What should the balance be between the need to use potentially sensitive information to enable innovation within Government, and the need to respect the privacy of private citizens?

• Aligning data initiatives with business goals and driving innovation through AI adoption
• Understanding their roles and responsibilities in the context of AI
• Addressing security and privacy concerns

• Key digital technologies that enhance resilience
• How data analytics can improve decision-making and responsiveness
• Strategies for protecting sensitive information
• Compliance with data protection regulations

• Developing ethical guidelines and policies
• Implementing ethical AI practices in development and deployment
• Creating an ethics review board

• Utilizing AI to identify and mitigate potential risks in agency operations
• Exploring new opportunities for innovation and growth through the integration of AI and data analytics
• How to leverage data analytics to make informed decisions that enhance agency performance