Bangladesh has faced systemic corruption with various public and private sectors, resulting in inefficiencies, economic disparities, and hardships for the common people. Under the former regime, corruption was rampant, leading to biased policymaking that benefited vested quarters at the cost of national wealth and public welfare. To ensure a sustainable future, it is imperative to reform state policies and eliminate corruption. This can be done through the integration of artificial intelligence (AI) and automated data-driven systems to monitor societal transitions. This initiative aims to empower the youth and universities in Bangladesh with AI-driven tools to actively participate in societal transition. By leveraging AI for unbiased decision-making and transparent monitoring, it seeks to eliminate corruption, enhance policy efficiency, and ensure equitable national development and public welfare.

The negative impact of corruption has permeated all key areas, including: (1) Energy security: predatory costs and economic inefficiencies and energy-related challenges have led to a severe energy crisis and financial burdens on the people putting their energy rights in danger. (2) Food security: poor policies and productivity, an oligopolistic market, especially in import, mismanagement of subsidies, and exploitation by middlemen have resulted in food shortage, adulteration, and price hikes. As a result, consumers' right to food is unprotected. (3) Health security: oligopolistic market, inaction by regulatory bodies, unethical business practices in the private sector and corruption in the healthcare and medicine sector have led to inadequate medical facilities, mismanagement of resources, and lack of affordable treatment options. Therefore, health rights are at stake. (4) Education security: poor-quality, corruption in recruitment and physical infrastructural development, and mismanagement of resources plague the sector. Former government officials, such as vice-chancellors of public universities and faculty members, have used employment as business opportunities, accepting bribes to recruit unqualified people. The same model was followed by the schools and colleges under Monthly Pay Order (MPO) all over the country. This not only compromises standards but also violates the right to quality education of the people. (5) Other areas: from financial security to national safety, biased policies and inefficient governance have created systemic instability and public dissatisfaction.

To counteract these, a fundamental shift in governance is needed—one that replaces human bias and vested interests with data-driven, AI-powered decision-making systems. AI has the potential to revolutionise governance by ensuring that policies are designed based on real-time, unbiased, and verifiable data.

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Power network AI can be deployed to collect real-time data from power generation, transmission, and distribution systems. By eliminating human intervention in energy allocation and pricing decisions, the system can ensure equitable distribution of electricity based on real demand and supply, power supply free from predatory costs, system loss reduction, power generation at the least cost, detection of fraudulent activities such as unauthorised power consumption, system loss and billing manipulation, and optimised renewable energy integration, reducing dependency on fossil fuels and enhancing power supply and economic sustainability.

Data-driven AI systems can be implemented to regulate the agricultural sector and ensure fair price monitoring to prevent market manipulation, automated tracking of crop yields, predicting potential shortages and suggesting policy adjustments in real-time, transparent subsidy distribution, ensuring that benefits reach the farmers rather than corrupt intermediaries, and control of market price to prevent manipulation.

The healthcare system can be improved through AI-driven solutions such as automated tracking of medical supplies and pharmaceuticals preventing corruption in procurement and distribution, predictive analytics for disease outbreaks allowing better allocation of resources, and AI-assisted telemedicine improving healthcare access in rural and underserved areas.

The education sector requires urgent intervention to eliminate corruption and improve quality. AI-driven solutions can help by automating faculty and staff recruitment, monitoring educational standards, identifying underperforming institutions and enforcing accountability. AI-based personalised learning systems can enhance the quality of education and make it accessible to all students. It can also detect fraudulent degrees and academic misconduct, ensuring credibility in educational institutions.

Governance and policy reforms must be guided by AI-driven analysis that eliminates human biases. Key benefits include unbiased policy recommendations based on economic, social, and environmental data rather than political influences, automated fraud detection systems in financial transactions, procurement, and contract management and real-time data integration from multiple sources, ensuring transparency and accountability in government decision-making.

For AI-driven systems to function effectively, it is essential to establish: (1) Automated data collection mechanisms, reducing human interference and chances of manipulation; (2) Data sanitisation protocols, ensuring that collected data is accurate, relevant, and free from bias; and (3) Blockchain and cryptographic security frameworks, preventing unauthorised alterations or misuse of data.

The integration of AI-driven systems in governance is not just a technological upgrade—it is a revolutionary step towards fairness, efficiency, and accountability. By replacing corrupt human interventions with data-driven AI decisions, Bangladesh can overcome the inefficiencies of the past and build a future where public welfare is prioritised over vested interests.

M. Shamsul Alam is energy adviser at the Consumers Association of Bangladesh (CAB), and professor of electrical and electronic engineering at Daffodil University.

Views expressed in this article are the author's own.

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