Artificial Intelligence (AI) is swiftly transforming industries through increased efficiency, process automation, and novel solutions. 74% of India's senior executives are in the process of restructuring their businesses to factor in AI adoption. 78% of financial institutions are in the process of or planning to adopt gen-AI in their businesses. With increasing AI adoption, regulatory issues are cropping up. This encourages companies and policymakers to create frameworks that promote ethical, open, and secure AI use. Regulatory agencies are placing guidelines in action to regulate AI's possible risks. These risks include data privacy, bias, accountability, and transparency.

For Indian businesses, dealing with regulatory concerns around AI is essential. Non-compliance can result in legal implications and loss of customer trust. If you're a business leader, this article provides key steps for you to navigate AI regulations.

1. Understand AI regulations applicable to your business

The first thing you would need to do is to have a detailed understanding of the laws and regulations around AI. In India, businesses must comply with national laws and guidelines such as:

• Digital Personal Data Protection Act (2023): Governs the collection, storage, and processing of personal data by AI systems.
• Reserve Bank of India (RBI) Guidelines: Provide specific directives for AI use in financial services, including credit scoring, fraud detection, and customer service.
• Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules (2021): Cover online platforms and ethical use of AI-generated content.

2. Regulatory risk assessment

Prior to the deployment of AI systems, companies must perform an extensive regulatory risk assessment. It entails the identification of possible risks of AI utilisation, their estimation, and the creation of mitigation plans.

Regulatory risk assessment focuses on the following:

• Data Privacy Risks: Determine if AI models process personal data responsibly in accordance with data protection legislation.
• Bias and Discrimination: Scrutinise training information and AI code to detect and remove biases that may result in unjust decisions.
• Transparency and Explainability: Make AI systems produce transparent and interpretable outputs, especially in high-risk use cases such as finance and healthcare.
• Accountability: Enforce accountability for decisions made using AI, with human intervention in vital processes.

For instance, a bank that uses AI to approve loans must prevent its AI models from biasing against female, ethnic minority, or lower socio-economic applicants.

3. Implement sound data governance policies

Data forms the foundation of AI, and data management ethically is the key to compliance with regulations. Organisations must implement sound data governance practices that will guarantee data quality, security, and ethical use.

Sound data governance practices include:

• Data collection and consent: Obtain clear user consent before collecting personal data. Inform users how their data will be used by AI systems.
• Data minimisation: Gather only the data required for AI processing, lowering the risk of data breaches.
• Anonymisation and encryption: Employ methods such as anonymisation and encryption to safeguard sensitive data from unauthorised access.
• Data lifecycle management: Put policies in place to control data from collection to deletion, meeting data retention needs.

4. Create ethical AI frameworks and guidelines

Having ethical guidelines for AI use is vital in order to avoid regulatory problems. Ethical AI frameworks define best practices for designing, deploying, and managing AI systems so they comply with organisational values and legal standards.

An ethical AI framework consists of fairness and non-discrimination, transparency, accountability and user education. For instance, an AI recruitment company should indicate whether or not an AI tool decides on hiring and give reasons to non-selected candidates. This keeps it open and trustworthy to stakeholders.

5. Set up AI compliance and monitoring procedures

Continuous monitoring of compliance is very important in resolving regulatory concerns in AI utilisation. Companies need to have procedures for periodic review of AI systems to ensure regulatory compliance and adjust to new legal demands.

Some compliance and monitoring measures that you can consider are:

• Perform regular audits to ensure AI obeys ethical standards.
• Use AI monitoring tools to monitor system performance and maintain compliance with set protocols.
• Maintain records of AI development, testing and decision-making processes.
• Develop protocols to address compliance breaches, including notifying authorities, managing public communication, and implementing corrective actions.

An example of good compliance monitoring is a bank with AI-driven credit assessments. Having regular audits conducted on its AI models, the bank would be able to comply with RBI guidelines while having the fairness and transparency of the lending procedures assured.

6. Interact with regulators

Active engagement with regulators helps businesses predict regulatory changes. Regulators sometimes guide and provide advisory services to firms adopting AI. Some engagement strategies that you could adopt when it comes to engagement with regulators involve:

• Cooperating with regulators: Offer comments on draft regulations and engage in regulatory sandbox programs.
• Participating in industry forums: Contribute to the debate on AI regulation and learn from peers.
• Remaining up-to-date: Periodically read publications and releases from regulatory agencies to be able to foresee shifts in AI compliance obligations.

Conclusion

Resolution of regulatory concerns in AI applications is a complex process involving a strategic and proactive strategy. For Indian companies, including NBFCs and those in businesses such as online marketplaces, compliance with regulatory guidelines is not just a statutory requirement but also a competitive strength. By adopting transparent compliance plans, having risk assessments, having strong data governance, and interacting with regulators, companies can use the power of AI ethically and responsibly.