AI Ethics and Governance

How to Keep Your AI Ethical

Artificial intelligence (AI) brings many proven and sustainable benefits to finance and other business operations and provides a strong opportunity for companies that master it to gain a competitive advantage. But AI also introduces new risks to compliance and brand reputation that finance professionals must learn to recognize and manage.

Keeping AI use ethical and fair requires a skillful blend of management and data science. Even then, risk is constant because business conditions, regulations, public sentiment and AI performance are all continually changing. These changes create the possibility for bias to develop, which is an ongoing risk to ethical AI and threat to compliance, especially with the myriad of inconsistencies around the world.

Gartner predicts that 15% of application leaders will face board-level investigations into AI failures by 2022 (Top 5 Priorities for Managing AI Risk Within Gartner’s MOST Framework, January 2021).

The Need for Ethical AI Management

Data scientists build AI models to implement an enterprise’s artificial intelligence initiatives. On average, enterprises have about 300 models in production, according to the State of ModelOps Report. Once models are deployed, they require continuous monitoring and management to ensure they perform as designed. Unlike traditional enterprise software, AI models require distinct skills, governance policies, and management tools.

AI models are constantly exposed to risk due to shifts in data, demographics, and business conditions. These shifts can introduce unintended bias, raising ethical, regulatory, and compliance concerns. Organizations that fail to maintain AI principles and best practices risk reputational damage, legal liabilities, and business inefficiencies.

Understanding AI Model Risks

Organizations must recognize that AI model operations are not static.

Risks evolve over time due to:

• Changes in input data (e.g., consumer behaviors or economic conditions).
• Unintentional biases emerging from model drift.
• Ethical concerns related to fairness and discrimination.
• Regulatory changes that require compliance updates.

Ethical AI failures have already affected major corporations, highlighting the importance of active model governance. According to a 2021 study, 80% of executives cited risk management and compliance challenges as barriers to AI adoption.

Problems Can Develop at Any Time

Data scientists do not intentionally develop biased models, yet ethical concerns frequently arise post-deployment. Even slight deviations from intended use can introduce risks. Bias may not appear immediately, but as data distributions shift, unfair outcomes may emerge.

One of the key challenges in ethical AI is that no single solution ensures fairness. Policies, governance frameworks, and monitoring tools must work in tandem to maintain AI integrity. Without this holistic approach, organizations struggle to track and mitigate emerging biases.

Developing an Ethical AI Framework

A structured approach to ethical AI includes:

1. Creating AI policies – Establishing governance structures and ethical guidelines.
2. Embedding fairness in model development – Ensuring transparency and accountability from the start.
3. Monitoring and testing AI models – Continuously assessing outputs for fairness.
4. Implementing orchestrated remediation – Acting on identified biases and inconsistencies.

By integrating these components, organizations can safeguard against AI misuse and improve accountability.

Use People and Policies to Set an Ethical Foundation

Ethical AI is not solely a technical challenge—it requires strong organizational leadership.

Reports from Harvard Business Review and McKinsey emphasize the importance of human oversight in ethical AI implementation.

Organizations should establish AI governance teams that include:

• Business leaders to align AI use with corporate values.
• Legal and compliance teams to ensure adherence to regulations.
• Diverse stakeholders to provide broad perspectives on ethical concerns.

A collaborative approach ensures that AI models align with organizational ethics and societal expectations.

Enforce Policies Throughout the Model’s Life Cycle

For AI policies to be effective, they must be enforced consistently across all AI models.

However, enterprises face challenges due to:

• Multiple teams developing AI models independently.
• Diverse data sources with varying levels of bias.
• AI models deployed across different cloud and on-premise environments.

Modern ModelOps solutions enable organizations to embed policy enforcement directly into AI management systems. This ensures that fairness checks, approvals, and audits are conducted throughout the model’s life cycle.

Preventing Bias in Model Development

Bias can be introduced in multiple ways, including through model design and data selection. Organizations can mitigate these risks by:

• Using diverse and representative training data.
• Conducting fairness audits before deployment.
• Implementing AI bias detection tools like Aequitas and other ethical AI toolkits.

Continually Test and Monitor AI Models

To maintain fairness, AI models must be continuously monitored.

Ethical AI performance depends on real-time observability and corrective actions. Model monitoring involves:

• Tracking key performance indicators (KPIs) related to fairness and bias.
• Comparing outputs against predefined fairness thresholds.
• Automating retraining and remediation workflows when biases emerge.
• Conducting ethical fairness testing and archiving results for audits.

Without ongoing monitoring, even well-designed models can become biased over time.

Orchestrated Remediation: Fixing Ethical AI Issues

Detecting bias is only the first step—organizations must also act on findings. Automated remediation ensures that ethical concerns are addressed proactively. Effective AI governance includes:

• Identifying and flagging problematic models.
• Initiating independent fairness reviews.
• Retesting and retraining AI models as needed.
• Implementing fallback procedures to mitigate AI risks.

By integrating remediation into AI governance, organizations can prevent reputational and legal consequences.

Conclusion: A Holistic Approach to Ethical AI

AI models evolve alongside business and societal changes. Maintaining ethical AI requires a combination of governance policies, model monitoring, and remediation processes. Organizations that take a holistic approach—incorporating human oversight, fairness assessments, and automated governance—will be better positioned to ensure ethical and unbiased AI use.

By prioritizing transparency, accountability, and proactive risk management, businesses can safeguard AI integrity while fostering innovation.

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