E-GOVERNANCE
WHEN AI TAKES CENTRE STAGE
Sanjeewaka Kulathunga asserts that people and the state have gained from AI
Traditionally, e-governance tried to improve accessibility and operational efficiency through digitalisation. In this context, the emergence of AI represents a fundamental shift.
automation One of the most significant contributions of artificial intelligence to e-governance is predictive analytics. By analysing historical and real-time data, governments can anticipate the needs of citizens, foresee potential shortages of resources and prepare more effectively for emergencies.
Predictive models can forecast unemployment trends, anticipate surges in hospital admissions and identify regions that are vulnerable to natural disasters. This foresight supports better resource management, informed policy making and enhanced risk mitigation.
NLP – or Natural Language Processing – further revolutionises communications between governments and the public. AI powered chatbots and virtual assistants can respond instantly to citizen queries, and reduce the burden on call centres by offering round-the-clock services.
This simplifies access by eliminating the need to navigate complex bureaucratic procedures. Citizens interact with intelligent digital agents who understand their concerns, and provide clear and timely guidance.
A vital aspect of AI’s integration is process automation. Robotic process automation and intelligent systems can manage repetitive tasks such as document verification, application processing and compliance checks without human intervention.
Automation increases efficiency, minimises human error and shortens processing times. When combined with the real-time data capabilities of IoT, artificial intelligence empowers governments to deliver more adaptive and responsive services.
ENGAGEMENT Across the world, governments are embracing AI driven e-governance initiatives. These often include extensive training programmes aimed at enhancing digital literacy and building artificial intelligence related skills among public servants and citizens alike.
Such capacity building efforts are designed not only to improve service delivery but also prepare the workforce for the digital economy.
Recruitment of digital experts, integration of AI in critical sectors such as healthcare and fostering data sharing across government departments are typical elements of modernisation strategies that will create more agile public administration.
Governments are also exploring participatory governance models that utilise such platforms to increase citizen involvement in policy making.
These initiatives encourage democratic engagement by enabling the public to co-create policies through AI assisted forums to increase transparency and trust in governmental processes.
For this to be effectively adopted in e-governance, comprehensive and structured frameworks are essential.
These typically encompass multiple interconnected layers such as secure and interoperable digital infrastructure, high quality and representative data sources to support policy, ethical standards to govern AI usage, and mechanisms that foster innovation through collaboration between the public and private sectors.
Such a holistic approach ensures that artificial intelligence technologies are integrated systematically and responsibly across government functions while upholding societal values.
International assessments of digital governance maturity emphasise the importance of resilient infrastructure, services designed around citizen needs and the strategic use of emerging technologies such as AI.
Equally critical is the commitment to digital inclusivity, which seeks to provide equitable access to digital government services by marginalised and rural populations.
INTEGRATION AI integration in public administration creates several challenges. Ethical concerns are paramount in maintaining public trust. Many citizens feel uncomfortable when decisions that affect them are made by opaque algorithms without transparent explanations.
Ensuring transparency in decision-making processes is crucial so that citizens can understand, question and even challenge algorithmic decisions.
Artificial intelligence systems trained on incomplete or skewed datasets risk perpetuating or even worsening social inequities. Some automated recognition technologies exhibit higher error rates for certain demographic groups.
Governments must therefore, prioritise the use of diverse, representative data and implement fairness metrics that continuously mitigate discriminatory outcomes.
PROTECTION Because AI depends heavily on large volumes of personal information, protecting citizen data through robust cybersecurity measures and clear governance policies is essential. Compliance with data protection regulations ensures accountability and safeguards citizens’ rights to privacy and security.
Automation increases efficiency
