Corruption, according to Transparency International, is the abuse of assigned power or status by a person to receive personal gain. Did you know approximately US$2.6 trillion in public funds is stolen/embezzled annually? India is the 85 least corrupt nation out of 180 countries, according to the 2021 Corruption Perceptions Index reported by Transparency International.  

In most cases, corruption involves public servants, politicians and government officials, but it can also take place in other types of institutions, such as financial entities, businesses, civil society organizations etc. To increase the efficiency of the fight against corruption, the introduction of AI and ML tools is currently one of the possible solutions. Earlier this year, Minister of State Jitendra Singh spoke about using AI to prevent corruption in India.

According to a recent article by Adalan AI, the ability of autonomous learning and the capability to scan, review and analyze extremely big amounts of data in a short amount of time make AI machines a promising tool in the fight against corruption. Generally, detecting fraud, tax evasion, bribery, illegal bids, and procurements take a lot of time and effort for humans. However, these assignments would be completed with speed and efficiency when transferred to machines.  

Extremely dangerous  

Fighting corruption by civil society organizations, journalists or citizens can be extremely dangerous in some countries. Also, public officials or employees of private companies who are willing to disclose cases of corruption in their working space often prefer to stay silent because, as whistle-blowers, they face lots of dangers and a lack of protection.  

The Adalan AI article spoke about the Transparency International launching the software named Dozorro, which was built on Machine Learning. The software goes through thousands of procurements and tenders and detects the ones prone to corruption risks. The platform is being actively utilized by civil society organizations that monitor procurement processes.  

Private companies often face the same difficulties as corporate corruption can undermine their public reputation. AI here can help them secure their public image as well.  

Data digitalization 

Since the efficiency of AI tools come from their data, it is important to digitalize the data they are trained on. In many countries, paper-based practices are steadily abandoned, and public services are mostly digitized. Open data on public procurements, tenders, bids, government transactions, declarations of public officials etc., are collected and published through digital platforms.  

Different types of data can be used for building corruption-fighting AI machines. They are: 

1. Open government data 
2. Crowdsourced data 
3. Digital traces 
4. State institutions or private company-specific data 

 Overcoming the challenges 

There are lots of challenges and trade-offs associated with the implementation of AI. Some challenges include bias, climate of surveillance, susceptibility to repetitional losses, and Inconsistency of AI-based approaches with the systematic approach. An additional challenge can be a “black box problem”. To be efficient and not make falsely positive or falsely negative decisions, AI algorithms applied in the corruption fight must be precise and flexible to learn based on the newly received information. 

To overcome these challenges, the credibility and accuracy of AI tools should be ensured. For which the decision made by AI should be investigated by human analysts. Also, the purpose of fighting corruption should be carried out in line with the protection of ethical principles of AI and fundamental human rights.  

Even though a “black box problem” cannot be entirely eliminated when dealing with AI, it can be mitigated by some effort’s particularly, algorithms on which AI tools are based should be designed in a form that describes the process of decision-making in a detailed way and makes it accessible along with the final decision. And finally, the data gathered should be relevant, sufficient, authentic and impartial.