Responsible AI & ML Development: Fairness, Explainability, and Accountability

Artificial intelligence (AI) and machine learning (ML) systems should be built to avoid bias, adhere to ethics, and remain explainable. Otherwise, they may produce discriminatory, uninterpretable, and potentially harmful results. That’s why sticking to responsible AI and ML principles is more crucial than ever.

We witnessed phenomenal progress in AI and ML in 2023 (with natural language processing and generative AI models leading the way). These technological advances are leveraged across industries—from financial services to the healthcare domain. In other words, AI is participating in decision-making that impacts human lives and business.

The principles of fairness, explainability, and accountability are critical here. They’re about crafting models that generate accurate, understandable, and trustworthy results.

Adoption of these principles remains problematic, though. According to the 2022 Responsible AI Index by the Gradient Institute, only 3% of respondent organizations have maturity with regard to responsible AI. The gap between awareness and action can be explained by this approach's complexity and resource-intensive nature.

Responsible AI development is by no means an easy feat. In this article, we’ll share practical strategies and actionable insights that help build transparent, accurate, and interpretable AI systems.

The Challenges of Building Responsible AI and ML Systems

While many AI systems are effective, they sometimes produce inaccurate results. Adding to the challenge, we can’t understand how specific algorithms came to certain conclusions (the black box problem). This inevitably creates ethical, moral, and legal issues.

These problems are significant obstacles to creating accurate, responsible ML algorithms. But, to get around these issues, let’s first take a closer look at each one.

Data biases in machine learning models

A bias in an ML model is a systematic error due to wrong assumptions, which leads to incorrect predictions.

Most biases are introduced into ML systems by an imbalance in the training data. For example, if you train the model on biased or discriminatory data, it may either be biased against particular groups of people, or if not necessarily biased, but contain characteristics that develop unfair behaviour over time.

Here are just some of the biases that can make it into your system:

• Race. A credit scoring algorithm can identify that people of color had more loan applications denied. This may result in race as a factor that decreases the chances of getting loans.
• Gender. In 2018, Amazon found its recruiting tool was biased against women. The reason is that it was trained on a dataset of resumes mostly from men, giving the idea that men are more qualified for the job.
• Confirmation. Prior expectations can impact the outcomes. For example, a sentiment analysis model trained on positive reviews might ignore negative feedback (or interpret them as positive).
• Geographical. A weather forecasting model trained on the US and Canadian data can be less accurate when predicting weather in parts of the world with vastly different climates.

Biased systems cause substantial fairness-related harm. They can under- or over-represent specific demographics, reinforce stereotypes, and fail to provide quality service.

Lack of transparency and accountability

Sophisticated algorithms make millions of calculations per second to reach conclusions. We must understand what factors influence the decision-making process for responsible ML and AI.

A transparent system clearly communicates what data is used to train the model and how the algorithm utilizes this data. In interpretable models, this information can be provided with technical papers about the algorithm and training data, as well as performance analytics. This allows the data scientists to analyze factors that impact it, assess the accuracy, and reproduce results.

Organizations and individuals should take responsibility for the decisions of AI and ML systems. However, complexity in AI systems, data biases, and lack of policy guidelines blur accountability.

Ethical and legal framework challenges

The diversity of algorithm-based software makes it hard to establish an ethical and legal framework for AI. Currently, there are no universally applied regulations and only a few proposed guidelines. This obviously makes it harder to know what standards to follow for responsible AI development.

Lack of explainability and biases introduce more ethical problems. For instance, AI can assist professionals in fields like healthcare, drug discovery, and epidemiology, but who will be held accountable if an AI system misdiagnoses a patient?

The rapid evolution of AI technology is another factor. In less than a year, ChatGPT—a large language model—evolved from a basic chatbot to a system that writes plausible, but sometimes inaccurate, PhD dissertations in minutes. Or take MidJourney. This generative AI could draw only basic imagery just a year ago and now produces award-winning art.

Both raise ethical and legal concerns. This includes copyright problems (it’s unclear who claims ownership of generated text) and highly realistic deepfakes.

Although significant, these challenges are solvable. In the following sections, we will explore effective practices to enhance AI-powered technologies' fairness, explainability, and accountability.

Best Practices for Fairness in AI and ML

Fairness ensures AI systems are free of prejudice, discrimination, and stereotypes that reduce the accuracy of results. Here are a few core practices organizations can follow to improve it.

Collect and prepare data

AI usually behaves unfairly because of the training data. So, the first thing to do is to make sure you’re training your system on high-quality datasets. To achieve this, companies should:

• Gather data that accurately reflect the target audience. This ensures that the decisions will be applicable and fair across all demographics.
• Properly label the data. Mislabeling introduces biases in your model. For instance, if an email classification dataset has inaccurately labeled “spam” tags, the ML will miscategorize incoming messages.
• Validate the reliability of the data. Developers should check the data and its attributes for errors, inconsistencies, missing values, and duplicates that may affect long-term predictions.
• Document data sources and origins. It helps to trace the model's behavior back to the training data to explain ML system behavior and suggest new ways to improve the algorithm.
• Pre-process and filter datasets. Data scientists should remove inaccurate or inconsistent information. If specific demographics are underrepresented, you can artificially increase their size by synthesizing data (augmentation).

Following these practices minimizes the chances of biases during model training.

Develop a scalable ML model

Biases can crop up due to decisions made by data scientists and engineers during the design and development phase. Therefore, creating a responsible AI and ML system requires following strict development standards.

We recommend using model architectures vetted by data science communities (such as logistic regression, random forest, and deep learning networks). You should also adhere to secure coding standards to improve the application’s reliability and scalability.

Collaborative DevOps practices, like a shared code repository and an automated CI/CD pipeline, can expedite the development. Services like GitHub help engineers, data scientists, and operations teams work together, track changes, and maintain high-quality code.

Train and assess the model

Training the ML model responsibly necessitates rigorous testing. Identifying bias involves considering those who will use the system and those who will be affected by it (directly or indirectly). Here are the core practices at this stage:

• Establish bias testing methodologies to verify the system’s validity toward particular groups of people.
• Determine the fairness metrics to determine success (like prediction accuracy, completeness, user satisfaction, and relevance).
• Be explicit about your priorities, as focusing on fairness in AI systems often involves trade-offs.
• Define an acceptable range for model drift—the change in model accuracy after they are deployed into production.
• Assess the interconnectivity of data streams in your AI and ML systems for further monitoring and troubleshooting.
• Introduce corrective courses of action in the event of detected biases.
• Assess the outputs of model and non-model components that comprise the AI system for comprehensive performance understanding.

The Chief Analytics Officer (CAO) usually develops guidelines for comprehensive due diligence. It’s worth looking at Microsoft’s AI fairness checklist to see if you can adopt some of their audit practices.

Best Practices for Explainability in AI and ML

Explainability allows data scientists (and occasionally engineers, managers, or auditors) to understand how AI came to certain conclusions. Let’s look at the practices that can make your model more transparent.

Prioritize interpretable models

Interpretable (glass-box) models, as the name suggests, make it easier to understand how and why the algorithm reaches conclusions. There are a number of different types of interpretable models, but the most common ones for sophisticated ML systems are decision trees.

A decision tree model is used to predict categorical (e.g., will the customer churn?) and continuous outcomes (e.g., how will the asset price fluctuate?). These trees work by splitting the input data into smaller subsets based on rules derived from the features of the data. This process continues, creating a tree-like structure until each group has only one feature. Data scientists can then identify outcomes for each group based on its characteristics.

The Explainable Boosting Machine (EBM) is immensely helpful in analyzing models based on decision trees. Adding to that, EBM can interpret the model without sacrificing accuracy.

Use interpretability tools

Interpretability tools help visualize and understand the behavior of AI models. In particular, they’re used to review and debug black-box models like neural networks.

There are quite a few frameworks you can use. For example, SHapley Additive exPlanations (SHAP) analyze the different features (clues) contributing to a model's prediction. It quantifies the influence of each feature for every prediction and then monitors how different indications impact the result.

Local Interpretable Model-Agnostic Explanations (LIME) tool can explain individual predictions instead of solely addressing the model's overall behavior. It works by generating approximations of the model surrounding a specific forecast. Then, it explains each feature based on the degree to which they impact the result.

Another interpretability technique is Integrated Gradients (IG). It’s model-agnostic—meaning you can use it for any differentiable model (those that allow calculating how small changes in input impact the outcomes).

A standout strength of the IG technique is its exceptional capability to clarify image-based models. With a clear, quantifiable baseline, this technique will highlight the specific pixels in the original image that most significantly contribute to the model's prediction.

Each tool has its limitations. For example, IG is great for assessing individual examples, but can be computationally expensive for large datasets. Knowing how to combine different interpretability tools will improve the understanding of your ML model.

Monitor and evaluate model performance

Similarly to fairness, organizations should establish metrics to evaluate the interpretability of the model. At the very least, they should include:

• Understandability of the model’s output. A low score could mean only data scientists can make the connection, while a high ranking means that uninitiated developers and managers can interpret the results.
• Interpretability of the inputs. Whether it’s easy to understand how the model comes to conclusions and what factors play a role in the decision-making.
• Performance with new data. A model’s ability to retain its accuracy with the “fresh” data (that it encounters after training).

Logging is critical for transparency. You should implement a snap hosting feature (like within Azure Machine Learning workspaces) that generates artifacts for training runs, outputs, and lineage data. If a bias were to occur, this will help you reverse-engineer its causes.

Best Practices for Accountability in AI and ML

As AI systems become integral to decision-making, there needs to be an ethical and governance framework that spans all stages of the product life cycle. These practices help you align your software with ethical principles and regulatory requirements.

Establish ethical frameworks

To reiterate, the field of AI lacks a universally accepted ethical policy framework. Despite this, you can follow principles outlined by other organizations. For instance, the US Government Accountability Office has proposed an AI accountability framework for federal agencies and other entities. It encompasses four key dimensions:

• Governance: It implores you to establish an oversight framework for the development and use of AI systems.
• Data: Enforcing policies for the accuracy, completeness, and relevance of data used in training AI systems.
• Performance: Setting up metrics to measure AI performance and corrective processes.
• Monitoring: Implementing processes to ensure AI systems align with the mission and objectives of the entity to address potential risks.

On the other hand, Microsoft has proposed a set of responsible AI principles. These principles emphasize accountability, fairness, and explainability, as well as privacy, security, and inclusiveness. They aim to guide the integration of AI services into mainstream products in a responsible and trustworthy way.

By embracing these guidelines and continuously refining them, we can make strides toward achieving greater accountability in AI systems.

Establish organizational governance

A responsible AI and ML ecosystem necessitates comprehensive governance established through internal policies and procedures. These policies should detail different accountability aspects, such as:

• Technical specifications for the systems to comply with data management, privacy, and security laws.
• Human-centric AI design guidelines for users (including clearly defined capabilities and contextually relevant information).
• Correction procedures that enable easy editing, refining, or recovery from errors and ways to dismiss undesirable features.
• Policies that help save user activity with the AI systems to personalize future experiences (like when a company targets customers with marketing based on the interest they express in particular products).

In addition to these policies, organizations should define clear roles and responsibilities for different employees. For example, AI designers (responsible for creating ML models), administrators (audits and governance), and business consumers (they provide feedback about potential bias implications and interpretability issues).

Audit AI and ML systems for shifts

ML models that perform well in trained environments can lose accuracy after deployment (we mentioned the drift phenomenon earlier). That’s because some trained algorithms rely on “spurious correlations”—non-generalizable side effects that happen in new situations.

To ensure accountability for these models, companies should integrate techniques to measure accuracy, error rate, and throughput (efficiency). Common practices include:

• Data splitting—dividing the data into training and test sets to assess model performance.
• Cross-validation—partitioning the data into subsets, training on some while testing on others.
• Measuring precision and recall—offering insights into the balance of correct and incorrect predictions the model makes.
• Reweighting—adjusting the training examples to more closely match the changes in the input data after deployment (covariate shift).
• Label adaptation—trying to estimate the new label distribution to adjust predictions (label shift).
• Non-parametric identification—finding variables that impact the outcome in the data from the source domain and unlabeled data from the target.

It’s critical to continuously assess changes in the operating environment. This will help understand if the AI and ML systems can be scaled or implemented in other business areas.

Conclusion

Addressing data biases, improving transparency, and implementing ethical frameworks can help you build responsible AI and ML models. This is a continuous process that requires you to cover all aspects of the product life cycle:

• Collecting, augmenting, and filtering data for the ML model.
• Monitoring the model for discriminatory, prejudiced, and stereotypical data.
• Implementing governance and ethical frameworks for correct development and use.
• Continuously monitoring the system after deployment to prevent data drifts.
• Refining the model to improve accuracy.

Don’t let the challenges of AI development slow your business down. Instead, embrace the future without the headaches by working with a reliable partner.

At Techstack, we develop custom AI-powered software solutions that prioritize fairness, regulatory compliance, and transparency for data scientists. With our expertise, we can guide you through the intricate landscape of AI development or create customized solutions tailored to your specific needs. Contact us today to embark on a journey toward cutting-edge AI solutions that deliver impactful results.