Artificial intelligence and corporate social responsibility can strengthen anti-corruption efforts
by Oludolapo Makinde, Doctoral Student in Law, University of British Columbia
Studies show that corporate governance processes that incorporate CSR, deter corruption.
In previous years, Canada’s ranking among the top 10 countries on the Transparency International Corruption Perception Index gave the impression that Canada was a country relatively clean of corruption.
However, Canada has slipped in the rankings in recent years, coming in at No. 11 in 2020 and 13 in 2021. This gradual decline has been attributed to, among other things, Canada’s limited enforcement of the Organization for Economic Co-operation and Development (OECD) Anti-Bribery Convention, an anti-corruption convention that requires countries to criminalize the bribery of foreign public officials.
Bribery of foreign public officials is of particular concern for Canada, considering the tendency for Canadian multinationals to engage in bribery and corruption in the Global South where corruption is rampant and anti-bribery enforcement is lax.
Given that this is an ongoing concern, how then can companies conduct business responsibly? In addition to legislative instruments, such as the Corruption of Foreign Public Officials Act, the OECD convention and the Extractive Sector Transparency Measures Act, I suggest that companies should explore how an integrated corporate social responsibility (CSR) and artificial intelligence (AI) approach can help mitigate corruption risks.
CSR and corporate corruption
CSR is a stakeholder-focused approach to business that encourages corporations to make voluntary contributions to the sustainable development and improvement of society. This goes beyond legal and regulatory requirements to voluntary, self-regulatory actions taken by corporations.
Using CSR as an anti-corruption tool involves reinforcing the need for companies to fill governance gaps in anti-corruption regulation and not exploit the lack of adequate legal and institutional frameworks when operating in the Global South.
Adopting this approach means companies have to go beyond the bare minimum, by not just complying with anti-corruption legislation, but viewing anti-corruption as a social responsibility. Doing so will centre transparency and accountability at the heart of a company’s operations, and communicate to stakeholders and shareholders that the company places a premium on these values.
An anti-corruption CSR paradigm will infuse meaning and purpose into corporate anti-corruption programs by ensuring that anti-corruption codes, clubs and disclosure mechanisms are not merely box-ticking exercises, but part and parcel of the way a company does business.
In terms of the effectiveness of CSR measures, studies show that corporate governance processes that incorporate CSR, deter corruption, provided that institutional frameworks are effective and freedom of the press is guaranteed.
Despite these benefits, further studies reveal that most companies do not view CSR as an anti-corruption tool and are not incorporating it into their governance processes.
AI and corporate corruption
In addition to CSR, there has been much excitement about the future of AI in anti-corruption work. AI has increasingly become a part of our daily lives, from digital assistants like Siri and Alexa, to self-driving cars like Teslas and ride-hailing applications like Uber.
Given that AI has been useful in so many ventures, anti-corruption scholars are eager to apply it to their work. In fact, AI has been described as “the next frontier in anti-corruption.”
AI holds a number of benefits for anti-corruption work, and is particularly good at detecting corrupt activities that were formerly undetectable by human efforts — all at a much faster rate.
For example, the Tax Administration Services of Mexico used AI algorithms and analytical tools to detect 1,200 fraudulent businesses and 3,500 fraudulent transactions within a three-month period. This would have taken about 18 months to achieve without AI assistance.
However, AI and anti-corruption discussions so far have mostly focused on governmental efforts to address corporate corruption, not on companies using AI to mitigate corporate corruption — even though many of them already use AI to maximize profit.
In the corporate anti-corruption context, AI can provide companies with a proposed investment destinations or transactions and help detect corruption risks in such ventures and improve due diligence processes.
AI can also provide more information for yearly anti-corruption policy reviews and assist in designing training based on AI analyses of company processes, reports and operations.
However, there are ethical concerns that arise from AI use. These concerns include violations of the right to privacy and freedom of expression, AI data biases or the replication of bias in AI. There are also concerns that such technologies in themselves can be used as tools to facilitate corruption as corrupt persons may use AI to fine-tune their processes.
The Brazilian machine learning application called MARA is a case study in such concerns. This application involves evaluating the probability of an individual acting corruptly based on data retrieved from their social security number.
This raises privacy concerns, considering social security numbers contain sensitive information. The application also depends on criminal conviction data, which may be biased since marginalized groups are often over-represented in the criminal justice system. An application like MARA is likely to proliferate this bias.
While CSR can help establish a culture founded on transparency and accountability, AI tools can assist companies in implementing these values within the organization. Companies should explore an integrated approach, while taking into consideration the challenges that arise with the use of AI.