IBM India/South Asia Blog

Analyst Blog: The Need for a Robust Governance Framework to Build AI Responsibly

Mar 27, 2024

By Neha Gupta, Senior Research Manager, IDC

According to IDC FutureScape: Worldwide Digital Business Strategies 2024 Predictions — India Implications, a document that tracks digital business predictions across industries in India, by 2027, 25% of enterprises will develop new key performance indicators (KPIs) that are directly tied to digital business outcomes through the application of advanced analytics and AI, and by the same year, 40% of enterprises will have mastered the use of generative AI (GenAI) to codevelop digital products and services leading to double revenue growth compared with their competitors.

The need for robust AI ethics, data governance, and trust

As AI becomes integral to businesses, it brings both unprecedented opportunities and significant responsibilities. The direct impact on people’s lives underscores the need for robust AI ethics, data governance, and trust. With the emergence of ML and GenAI, ethical challenges, such as bias and transparency, have become paramount. It is then imperative for companies to manage AI responsibly, ensuring transparency in training data and algorithms to build trust. Ultimately, for AI to be trusted, it must be explainable, with stakeholders understanding the rationale behind its recommendations. Responsible AI principles are essential for harnessing AI’s potential while mitigating unintended consequences.

Responsible AI is AI that is aligned with human-centered values and is focused on reducing the unintended consequences of AI by ensuring that the system’s intent and use are aligned with the norms and values of the users it aims to serve. AI governance is an overarching framework and platform that manages an organization’s use of AI with a large set of processes, methodologies, and tools.

AI governance as crucial for scalable and responsible enterprise initiatives

With poor AI governance, as well as the absence of clear accountability or responsibility for AI, CIOs and IT executives will be expected to take ownership of this governance and provide leadership to the organization at large. To accomplish this, IT leaders must proactively embrace controlled adoption and develop a compelling yet pragmatic vision for their enterprise in partnership with business leadership.

CIOs should prioritize the responsible implementation of AI and navigate ongoing changes within their organization. They must embrace this exciting technology with caution, guardrails, best practices, and governance. More strategically, it is crucial to address the central limit of the system — the absence of visibility into cause-and-effect relationships.

A well-defined AI policy that outlines principles of explainability, fairness, transparency, accountability, and data protection is the foundation of the "responsible use" of AI.

In the realm of AI, trust is foundational for ensuring the effectiveness and ethical integrity of AI systems. Interpretability is crucial for fostering trust in AI. It allows practitioners to understand and interpret the decisions made by AI systems, promoting transparency and accountability. By prioritizing factors, such as prediction accuracy, traceability, and decision understanding, organizations can empower users to confidently utilize AI-driven insights. Neutrality is another essential aspect of trustworthy AI, especially in critical decision-making contexts. Organizations can enhance trust by embracing diverse data sets, employing bias-aware algorithms and mitigation techniques, fostering diverse development teams, and implementing ethical review processes to promote equitable outcomes and mitigate biases. Resilience, transparency, and privacy are also key components in building trust in AI systems. This means resilience against adverse conditions and malicious attacks, transparency for users to evaluate AI functionality and understand its capabilities and limitations, and privacy measures to protect individuals’ sensitive data, ensuring compliance with regulations and fostering trust among users.

Organizational Governance — Data Governance and Responsible AI Governance

As per IDC’s 2023 Digital Business Survey in India (with 305 respondents), more than 75% of India organizations are either doing some initial exploration of potential use cases (piloting/proof of concept) or already using GenAI for some use cases.

Before enterprises explore any AI implementation, they must establish a responsible AI policy, build an AI strategy and a road map, design an intelligence architecture, and map the skills required for success. Two critical aspects of organizational governance are data governance and responsible AI governance.

Data Governance: The Data Protection and Data Privacy (DPDP) Bill that has been recently passed by Indian Parliament enforces robust data governance, emphasizing quality, security, and consent management and having penalties for non-compliance. Upcoming guidelines will elaborate on consent mechanisms and data lineage management, ensuring transparency.

Responsible AI Governance: In an era dominated by ML, Python-based work, and prediction models, responsible AI extends beyond traditional frameworks to include ML operations (MLOps) and cloud technologies. It mandates end-to-end governance, guaranteeing transparency and compliance with evolving regulatory frameworks, such as those from the Reserve Bank of India (RBI), Insurance Regulatory and Development Authority of India (IRDAI), and Securities and Exchange Board of India (SEBI).

IDC believes that GenAI is one of the most influential and critical emerging technologies today. IDC has developed a framework that provides enterprises with guidance on how to adopt it. The framework outlines the principles of RAI, encompassing fairness, transparency, data protection, and accountability. It underscores defining roles, ensuring legal compliance, identifying impactful use cases, establishing governance, and providing skills training.

To navigate the dynamic market in India, businesses must prioritize the development of a road map, followed by the implementation of active monitoring, development of rules and policies, and selection of the most impactful use cases to mitigate potential negative business impacts and boost their competitive advantage while fostering trust and confidence among stakeholders. In this journey, offerings from technology solution providers, such as IBM, can help in directing, managing, and monitoring AI activities across enterprises fostering responsible, transparent, and comprehensible AI processes to propel business growth.



Neha Gupta, Senior Research Manager, IDC




IBM Publisher Note: To know more about how you can enable governance in your organization visit our website.


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