Vasupradha Ramachandran
The adoption of Artificial Intelligence (AI) by businesses in the past few years has opened up various opportunities in terms of corporate innovation, efficiency and increased profitability. Eventually, over time, AI became a crucial factor in corporate growth strategies. A wide range of companies has now adopted AI for various business functions such as analytics, process automation, cyber risk management, customer engagement, etc. In the next 5 years, the global AI market is expected to reach $126 billion. As the technology keeps developing, it becomes increasingly complex and challenging to understand how it functions as well as monitor it parallelly.
Despite the advantages, Artificial Intelligence has also exposed organizations to face ethical issues. This has increased the need for effective governance and risk management. Without appropriate risk management systems, businesses may be exposed to threats. This will not only affect the businesses but also the communities and individuals that are dependent on them. This article highlights the risks that are associated with adopting AI and ways to mitigate those risks.
Business risks in Artificial Intelligence (AI) deployments
Businesses are highly prone to be exposed to financial and reputational risks with the absence of risk management methodologies to address and identify the issues that arise with the implementation of AI applications.
Financial risks
While AI technologies, in most cases, can be deployed and delivered effectively using the existing IT governance. But the existing practices might not be suitable for addressing unexpected issues or challenges that raise as AI keeps evolving. This is because of the very nature of Artificial Intelligence. Gartner predicts that companies can use AI to gain higher business impacts in a very short time. For a technology that is self-learning, automated and algorithm powered, even simple inputs can result in providing unpredictable outcomes. This also makes it increasingly difficult to manage AI.
The increased demand for AI-based solutions has created a lot of business opportunities and essentially a lot of AI vendors. While most companies opt to buy their AI-powered solutions for their businesses, it is highly important to ensure the transparency of the algorithm used. Black box AI solutions with unexplainable algorithms disrupts important aspect of the business. In case of unexpected outcomes, the company must be able to defend itself and the algorithm-based decision making to several stakeholders, clients and even legal heads.
Additionally, increased reliance on technology has increased the rate of cyber threats. AI-based cyberattacks are becoming pretty common these days. Once the hacker infiltrates the AI system, the input parameters or the code used can be easily altered if the system does not have strong enough security along with regular checks and maintenance. AI-based cyberattacks are effective enough to destabilize the firm’s entire digital capabilities. This will have a massive impact on their business operations and revenue generation.
Reputational risks
Sometimes enterprise AI technologies may either directly or indirectly affect certain groups of people in society. Such kinds of AI are called biased AI. A biased AI can have a major impact on the reputation of the firm. Companies that are solely focused on profit gains sometimes exploit themselves to the risk of litigation which affects their reputation.
AI applications can also have biased and discriminatory outcomes if the data is trained to do so. For example, AI-powered hiring systems look for gender to hire people for certain roles. An open position for secretary will preferably be offered to a woman whereas the role of a driver would be offered to a male. This becomes even worse when historical, age-old data are being used to train and consolidate inequalities and discriminations that subconsciously exist in society.
Apart from business risks, there are also external risks and challenges such as taking measures to maintain public interest and predicting the developing landscape of AI to scale up the business.
Achieving responsible AI deployments
As a result of financial and reputational risks and challenges, companies have now started gaining awareness of the potential pitfalls and impact of AI on society. This raising awareness has increased the need for AI governance frameworks. AI governance can be achieved through 5 unique dimensions as follows.
Intent
It is important to ensure that the data is trained in a principled manner and verifying if the design and implementation process is ethical, well-aligned and appropriate. By doing this, businesses can seamlessly execute their risk management and undertake internal reviews.
Fairness
Ensure that the AI is not biased against any community, individual or culture under any circumstances. This will help firms in maintaining their standards and gain public recognition while minimizing external risks.
Transparency
Firms must ensure that the AI system is completely transparent where the processes are explainable and executable. Ensuring transparency will provide compliance and maintain stakeholder confidence while helping to develop the AI further.
Safety
Implementing robust capabilities in data governance, threat protection and privacy will largely improve the chances of identifying malicious attacks at a very early stage. This will enable mitigating unexpected outcomes, minimize legal liability and increase the efficiency of data usage.
Accountability
Firms must conduct audits and compliance assurance processes. Frequent audits and checks will help in avoiding a lot of complications in the long run. This will increase the confidence of potential stakeholders like auditors, customers, business partners, shareholders, etc.
A governance framework is only effective when it is properly implemented. Companies have to implement enterprise-grade governance infrastructure and mechanisms – an oversight committee to ensure adherence to control, risk register to note down the possible risk factors, analytics and testing to gain useful insights and policies and enforcements to establish norms, roles, accountabilities, approval, maintenance, guidelines, etc. The global governance market is expected to reach $1,016 million by 2026 with a CAGR of 65.5% between 2020 and 2026. Governance mechanisms must potentially focus on reviewing and maintaining AI systems at both application level and algorithm-wise to ensure the integrity of the application.
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