Table of contents
Practical guidance for AI governance
Artificial intelligence (AI) is drastically changing how we live and work. From prioritizing patients for emergency medical care to checking employment and housing eligibility or our social media algorithms, the use of AI is everywhere.
At its core, AI aims to replicate human intelligence in a computer or machine with greater accuracy and speed. And its influence is growing as enterprises across industries deploy the technology across their front, middle and back offices.
But as AI becomes integrated into our everyday life, concerns around its ethical use have taken center stage. For example, rooting out potential biases like gender and race discrimination remains a significant challenge. For the enterprises that fail to act, legal and reputational consequences could follow. Here, we'll review the responsible AI challenges and how to overcome them.
Concerns behind the growing role of AI
The lack of clear ethical guidelines in AI and machine learning (ML) systems may have several unintended consequences for individuals and organizations.
Figure 1: The potential risks of AI
Artificial intelligence is transforming industries, but its rapid adoption brings a new set of risks that organizations must proactively address. As regulatory frameworks and societal expectations evolve, the risks associated with AI systems have become more complex and consequential. Below are the key categories of AI risks that enterprises should consider when designing, deploying, and governing AI solutions:
1. Regulatory non-compliance: With the introduction of comprehensive laws such as the EU AI Act, California AI Transparency Act, and Colorado AI Act, organizations face significant penalties for failing to comply with requirements around transparency, explainability, and risk management. Non-compliance can result in financial penalties, reputational damage, and operational disruptions.
2. Algorithmic discrimination and bias: AI systems can inadvertently perpetuate or amplify biases present in training data, leading to unfair or discriminatory outcomes. New regulations require organizations to conduct regular risk assessments, implement bias mitigation strategies, and provide avenues for affected individuals to appeal decisions.
3. Data privacy and security: AI models often rely on large volumes of sensitive data. Risks include unauthorized access, data leakage, and model inversion attacks. Privacy-by-design principles and robust security controls are now mandatory to protect user data and maintain trust.
4. AI-generated content and misinformation: The rise of generative AI has increased the risk of deepfakes, misinformation, and content authenticity challenges. Organizations must deploy detection tools, ensure proper disclosure of AI-generated content, and monitor for malicious use.
5. Lack of explainability and traceability: Opaque “black box" models make it difficult to understand how decisions are made. Regulatory frameworks now require organizations to implement explainability and traceability mechanisms, enabling audits and providing clear explanations to regulators and stakeholders.
6. Human oversight and emergent behaviors: While human-in-the-loop oversight and red teaming are industry standards for risk identification, they cannot anticipate every edge case or emergent behavior. Continuous monitoring and feedback channels are essential to catch and address unexpected outcomes.
7. Ecosystem and supply chain risks: AI risks extend beyond individual organizations to include third-party models, open-source components, and supply chain vulnerabilities. Responsible AI governance now requires ecosystem-wide collaboration and transparency.
8. Operational and strategic risks: AI missteps can lead to financial losses, reputational harm, and strategic setbacks. Organizations must embed responsible AI principles into their governance frameworks, ensuring that risk management is an ongoing, organization-wide effort.
These risks are all contributing to the global debate around the use of AI, prompting lawmakers to propose strict regulations. A lack of trust has forced AI practitioners to respond with principle-based frameworks and guidelines for the responsible and ethical use of AI.
Unfortunately, these recommendations carry many challenges (figure 2). For example, some enterprises have developed insufficient or overly stringent guidelines, making it difficult for AI practitioners to manage ethics across the board. Or even worse, other organizations have inadequate risk controls because they don't have the expertise to create responsible AI principles.
Figure 2: Challenges in responsible AI/ ML development
A new approach: The responsible AI center of excellence
To develop AI solutions that are fair, trustworthy, and accountable, enterprises are building AI centers of excellence that act as ethics boards. By connecting diverse groups of AI custodians – who can oversee the development of artificial intelligence from start to finish – enterprises can proactively prevent issues down the road. Put simply, teams of people with different experiences, perspectives, and skill sets can catch biases upfront compared to homogenous groups.
Thankfully, with a practical framework in place (figure 3), AI practitioners can establish ethics-as-a-service to assist and guide organizations through AI implementation and development.
Figure 3: Responsible AI in practice
The four pillars of a responsible AI framework
Developing a comprehensive framework is the first and most crucial stage of a responsible AI journey, and it usually consists of four pillars:
1. Governance body
Enterprise leaders must establish an AI/ML governance body (figure 4) of internal and external decision makers to oversee the responsible use of AI continuously. In addition, C-suite executives should prioritize AI governance – other business leaders can then be held accountable for developing governance policies alongside regular audits.
Figure 4: Responsible AI governance body
2. Guiding principles
Transparency and trust should form the core principles of AI. We've identified seven characteristics to help enterprise leaders form a strong foundation for safe, reliable, and non-discriminatory AI/ML solutions:
- Domain-specific business metrics evaluation
- Fairness and legal compliance
- Interpretability and explainability
- Mitigation of changes in data patterns
- Reliability and safety
- Privacy and security
- Autonomy and accountability
- Traceability
Similarly, a governance body can help enforce these principles throughout the development of AI technology (Figure 5).
Figure 5: Matrix of AI governance body
3. Realization methodology
AI solutions will inevitably touch multiple areas of the organization. As a result, enterprise leaders must account for and involve all stakeholders, from data scientists to customers. This process also requires having clear risk controls and a framework for responsible AI (figure 6).
Figure 6: Realization methodology for Genpact's responsible AI framework
4. Implementation overview
The inherent complexities in developing AI algorithms make interpreting models challenging. However, enterprise leaders can embed responsible AI considerations throughout the process to help mitigate potential biases and follow best practices (figure 7).
Figure 7: Genpact's responsible AI framework implementation
case study
A global bank puts responsible AI theory into practice
A bank wanted to streamline loan approvals while removing potential biases from its loan review process. As a first step, Genpact improved the bank's data management and reporting systems. Then, with a robust data taxonomy in place, we applied our responsible AI framework. For example, removing variables like gender and education increased the probability of reaching a fair decision. We also improved the quality of the reports, helping employees enhance transparency by showing the data behind their decisions. Finally, we developed a monitoring system that could alert the AI ethics board of any potential issues. The success of this project has led the bank to deploy similar AI ethics models across the organization.
Embracing the responsible AI opportunity
Customers, employees, partners, and investors increasingly expect organizations to prioritize AI ethics to build safe and reliable products. With a responsible AI framework in place, organizations can continue innovating, building trust, and increasing compliance. These benefits will help organizations sustain long-term growth, improve competitive advantage, and create value for all.