The Role of Policy-Driven Solutions in Ethical Decisioning

Having the right policies in places can help combat bias in AI/ML decisioning. Follow these practices to craft an ethical automated decisioning solution.

Picture this: You’re navigating a vast ocean of big data, harnessing the power of artificial intelligence/machine learning (AI/ML) to make critical decisions for your business. But lurking beneath the surface are hidden biases threatening to sway your decisions, leading you astray. How do you steer the ship to ensure your AI-driven decision-making remains ethical and unbiased? This is where a policy-driven solution comes in.

It’s not enough to automate decisions—you have to ensure that these decisions are fair and accurate, with an extremely low degree of bias. The way to do that is by establishing a rule or policy-making process. We can view this as setting procedures and standards that govern how data is collected, analyzed and used to make decisions. This ensures that all decisions are made in a consistent and transparent manner and that any potential biases are identified and addressed.

Defining Policy-Driven Solutions

These are systems built with guardrails to ensure automated decisions are fair, ethical and aligned with company values. Instead of just optimizing for accuracy or efficiency, these solutions are designed from the start to limit the risk of bias in big data. As AI gets widespread integration, technology must reflect principles of equal opportunity, transparency and accountability. A policy-driven system does this by:

• Defining clear rules and constraints upfront based on laws, ethics and social impact. For example, a policy could be “never make a hiring recommendation based on gender, ethnicity or other protected attributes."
• Choosing or generating training data that represents all groups fairly. If certain populations are underrepresented in the data, the model can’t learn to serve them well.
• Continuously monitoring models for signs of unfairness or unwanted behavior and making corrections as needed. The policies and constraints are enforced throughout the AI lifecycle.
• Explaining the reasons behind automated decisions in a transparent way. This helps address concerns about black box systems and builds trust that the AI is behaving as intended.
• Giving human experts oversight and control over the most sensitive or impactful predictions or actions. People remain in the loop for high-stakes decisions.

Why is This Significant?

The rules-based system mimics the expert-level thinking of humans in solving difficult problems. It works in a way that tells the system how to act based on certain conditions based on specific rules. These rules are written as “if-then” statements. So if a certain condition is met, then the system knows what action to take.

This explains its significance in providing a structured, transparent and accountable framework for AI and ML systems. Companies should be able to trace and explain how decisions are made and how they interact with each other, especially in complex processes where a single decision can have far-reaching consequences.

Benefits of Policy-Driven Solutions in AI/ML

Risk Avoidance

By implementing strict policies around data use, collection and algorithmic decision-making, companies can avoid many of the risks associated with AI and big data. Policies help ensure sensitive data is handled properly and decisions are made fairly, reducing the chance of legal issues or public backlash.

Customer Appeal

Customers today want to know how their data is being used and that AI systems are fair and unbiased. Research has proven that ethical brands boost customer loyalty and satisfaction. Adopting a policy-driven approach shows customers you value transparency, accountability and ethical data use. This can build trust in your brand and products.

Regulatory Compliance

Laws like GDPR give people more control over their personal data. A policy-driven solution will help companies comply with regulations by providing a framework for responsible data use and algorithmic accountability. This reduces the risk of penalties and legal consequences for non-compliance.

Exploring Untapped Market Opportunities

By focusing on ethical practices, businesses can cater to audiences that prioritize sustainability, social responsibility or other ethical considerations. This allows companies to differentiate themselves from competitors and tap into niche markets with specific values and preferences, to increase their market share and drive growth.

Practical Ways to Build Fairness into AI Systems

According to Arjun Narayan, Ex-Google Lead and Head of Global Trust and Safety at SmartNews:

“Machine learning algorithms heavily depend on diverse training data to generate accurate outputs for individuals or objects, including chatbots. The quality of the training data directly influences the accuracy of responses to user queries. However, algorithmic bias is a significant concern, often stemming from incomplete or unrepresentative training data, a common issue in machine learning.

Open-source models like ChatGPT, Stable Diffusion, etc. undergo pre-training using extensive publicly available data, which introduces the potential for biases present in that data to be incorporated. It is essential to acknowledge these datasets may perpetuate historical biases deeply ingrained in society, leading to prejudices against specific groups.

Detecting and mitigating biases in algorithms requires a combination of strategies, as bias mitigation is an ongoing process. It involves technical measures, human oversight, and a commitment to continuous improvement.”

He suggests strategies that include:

• Data analysis: Thoroughly analyze the dataset for patterns, imbalances and disproportionate representations. This can involve examining the distribution of different features, identifying missing data and comparing the characteristics of the dataset to those of the target population.
• External audits: Seek unbiased perspectives from external experts or auditors to review the dataset for potential biases. These professionals can identify areas of concern that internal teams may have overlooked or have become desensitized to.
• Diverse representation: Ensure the dataset includes diverse demographics, backgrounds and viewpoints. This helps to create a more balanced dataset that reflects the complexities and nuances of real-world situations.
• Feedback and user testing: Gather input from diverse users to identify biases or skewed outcomes. This usually implies soliciting feedback from individuals who represent different demographics, backgrounds and perspectives.
• Comparison with benchmarks: Assess the dataset against established benchmarks to uncover biases.
• Bias detection tools: Use specialized automated tools like fairness metrics or algorithmic auditing frameworks. These tools are designed to identify and measure biases in data or model outputs, allowing organizations to quantify potential issues and take corrective action.
• Ongoing monitoring: Continuously analyze the dataset throughout development and deployment to identify and address biases. This ongoing process helps ensure that AI systems remain fair and unbiased as new data is introduced or as the target population evolves.

Concluding Thoughts

Policy-driven solutions act as a compass, guiding AI and machine learning systems through complex decision-making processes while keeping them aligned with the core values and principles of fairness, transparency and accountability.

Furthermore, these policy-driven solutions help maintain trust between businesses, customers and other stakeholders. By instilling confidence that AI and machine learning systems are adhering to ethical guidelines, we can foster an environment where technological advancements and innovation flourish without compromising our fundamental values.

Progress Corticon is a powerful business rules management system (BRMS) that can help organizations create faster and more accurate decisions. With Corticon, businesses can automate policy-driven decisions based on “human-readable” regulations that can be easily debated with stakeholders. This means that rules and policies are not buried in lines of code, making them accessible and reviewable by users.

Try Corticon today.