Opening the Black Box: How to Create AI Transparency and Explainability to Build Trust

Can we really trust Artificial Intelligence?

Let’s face it. AI has trust issues.

AI is rapidly permeating our lives. But perhaps even more rapidly permeating, are fears about AI. Fears that are largely due to a lack of transparency as to how AI works. These concerns are evident in questions people are asking about AI, currently flooding the internet.

• How can we trust AI not to kill us in a self-driving car accident or by misdiagnosing a health condition through AI-driven medical technology?
• Will AI deliver racially biased sentences in our judicial system, pass over good job candidates in our HR systems, or unfairly deny financial services to those who need them?
• Will AI invade our privacy, spy on us, or reveal sensitive information that might be weaponized against us?
• Will AI manipulate public opinion, increase social echo chambers, create educational biases, accelerate disinformation, and destroy democracy itself?
• Will AI-driven marketing become so powerful we will lose all ability to make our own decisions?
• Will militarized AI attack the innocent? Will robots start their own wars, or just decide humans are ridiculous and eliminate us altogether on the basis of a cold and murderous logic?

People are asking questions about AI which require answers if we are to increase public trust in AI, and help educate the public to play a strong role in taking advantage of what AI has to offer while empowering them to help steward an ethical and human-centered evolution of the technology.

This is where AI transparency and explainability comes in.

What is AI Transparency? What is AI Explainability? What’s the difference?

AI transparency and explainability, while interconnected, serve distinct purposes in the realm of artificial intelligence.

What is AI Transparency?

AI transparency is about the visibility and openness of an AI system’s design, data, and operation. It involves disclosing detailed information about the AI’s development process, data used for training, functioning mechanisms, and deployment process.

AI transparency addresses challenges such as potential biases, ethical dilemmas, and compliance with regulatory standards, emphasizing the need for AI systems to be not only technically proficient but also understandable and interpretable by humans.

What is AI Explainability?

AI explainability, focuses on making the complex decisions and outputs of an AI system understandable to users, regardless of their technical expertise.

With transparency, an engineer who understands the impact of training data on an AI model designed for the financial industry. But will your grandmother understand why she was denied a loan by a financial system and what she can do about it? Maybe she can if your grandmother is an engineer. But if not, explainability can help her navigate a technology-driven real-world situation in a human, intuitive manner.

An Example of How AI Transparency and Explainability Work Together

Consider a financial institution using an AI system for credit scoring.

AI Transparency: The institution ensures transparency by disclosing how the AI model was developed, the types of data it was trained on (e.g., credit history, income levels), and the overall mechanism by which it evaluates creditworthiness. This transparency allows for an understanding of what data the AI uses and its general operational principles.

AI Explainability: When an applicant receives a credit score from this AI system, explainability comes into play. The institution provides a clear, understandable explanation to the applicant about why a particular credit score was assigned. For instance, the AI might highlight that the score was influenced by factors such as recent payment history or debt-to-income ratio, explained in a way that a non-expert can comprehend.

In this use case, transparency allows stakeholders to know what goes into the AI system and how it generally functions, while explainability allows the end-users, the credit applicants, to understand the specific reasoning behind their individual credit scores. Both elements working together ensure that the AI system is not only open about its processes but also accessible in its decision-making.

9 Reasons AI is Hard to Understand

AI decision-making involves sophisticated algorithms processing vast datasets to make predictions or choices. These algorithms, particularly in machine learning and deep learning, use statistical methods to find patterns and make decisions based on data inputs.

While this enables efficiency and scalability, it also introduces complexity that can be hard for non-experts to wrap their minds around.

For example, when an AI system evaluates a loan application, it might consider thousands of variables in a non-linear way that a human loan officer wouldn’t, making the rationale behind its decision less intuitive.

Here are features of AI that can pose significant barriers to easy understanding for non-experts.

Complex Algorithms

AI often uses intricate algorithms, especially in machine learning and deep learning, that are difficult for non-experts to comprehend.

Non-Linearity

Many AI models, such as neural networks, operate in non-linear ways, making it challenging to trace how input data leads to specific outputs.

Volume of Data

AI systems analyze vast amounts of data, and the sheer scale and detail of this data can be overwhelming and obscure understanding.

Lack of Explainability

AI systems frequently lack built-in mechanisms to explain their decisions in human-understandable terms.

Black Box Nature

The internal workings of many AI models are opaque, where the inputs and outputs are known, but the process in between is not visible or understandable.

Dynamic Learning Processes

AI models, particularly those involving machine learning, continually evolve and learn from new data, making their decision-making process a moving target.

Bias in Training Data

AI systems can inadvertently learn and perpetuate biases present in their training data, leading to outcomes that are hard to predict or rationalize.

Interdisciplinary Complexity

Understanding AI fully often requires knowledge spanning computer science, statistics, ethics, and domain-specific knowledge, making it a highly interdisciplinary challenge.

Regulatory and Ethical Ambiguity

The lack of standardized guidelines or ethical frameworks for AI development adds to the complexity and unpredictability of these systems.

The Role of Regulation in Ensuring AI Transparency

Current Regulatory Landscape for AI

In the current landscape, the regulation of AI is a patchwork of regional and national laws, guidelines, and standards.

Jurisdictions like the European Union (EU) have taken proactive steps with regulations such as the General Data Protection Regulation (GDPR), which includes provisions for the right to explanation in automated decision-making. This represents a significant move towards ensuring AI transparency.

Similarly, the United States has seen sector-specific regulations, like those in healthcare (HIPAA) and finance (Dodd-Frank Act), which indirectly impact AI by governing data privacy and financial reporting. However, a comprehensive legal framework specifically for AI is still evolving.

Anticipated AI Regulation Changes and Their Implications for Businesses

As AI technology continues to advance, it’s anticipated that more stringent and specific regulations will be implemented.

These regulations are likely to mandate greater transparency and explainability in AI systems, particularly in high-stakes social and public safety areas such as healthcare, criminal justice, and autonomous vehicles.

Businesses will be required to disclose more about their AI algorithms, data sources, and decision-making processes. This shift will necessitate significant adjustments in how AI systems are designed, developed, and deployed, emphasizing the need for built-in transparency and accountability mechanisms. Compliance with these regulations will not only be a legal necessity but also a competitive advantage in building customer trust.

How Regulation Can Promote Transparency and Accountability in AI

Regulation plays a crucial role in promoting transparency and accountability in AI:

AI Regulations Can Help Set Standards

Regulations can set clear standards for what constitutes transparent and explainable AI. This includes requirements for documentation, data traceability, and user-friendly explanation interfaces. In addition to providing guidance, such standards can also help create a level playing field for business, by establishing what level means.

AI Regulations Can Encourage Ethical Practices

By establishing a legal framework for AI operations, regulations encourage businesses to adopt ethical AI practices. This includes ensuring fairness, avoiding biases, and protecting user privacy.

AI Regulations Can Facilitate Auditability

Regulations can mandate regular audits of AI systems, ensuring they function as intended and adhere to ethical and legal standards. This can involve both internal audits and inspections by external regulatory bodies.

AI Regulations Can Enhance Public Trust

Clear regulatory guidelines help in demystifying AI technologies for the public. Knowing that AI systems are regulated and subject to oversight can significantly enhance public trust in these technologies.

Regulations Drive Innovation in Explainable AI

As regulations push for more transparent AI, there will be an increased demand for innovative solutions in AI explainability. This can lead to advancements in AI technology that are both powerful and user-friendly.

In conclusion, regulation is a key driver in ensuring AI transparency and accountability. As the regulatory landscape evolves, businesses must adapt to meet these new standards, not only to comply with legal requirements but to also foster trust and ethical use of AI technologies. The future of AI is intertwined with regulatory progress, and navigating this landscape will be a crucial challenge for businesses in the AI space.

Best Practices for Building a Transparent and Explainable AI

Implementing AI transparency and explainability, involves a multifaceted approach, incorporating various techniques and methodologies.

However, the goal of these techniques is not simply transparency and explainability, the goal is trust and trustworthiness. Consequently, practices that prevent issues, such as ethical data practices, good data hygiene and data preparation practices are as important to implement as practices that create explainable transparency to the types of data used.

Let’s take a look at these ethical AI, transparency, and explainability techniques, as applied across three industries: finance, customer service, and healthcare, along with their relevance at different stages of the AI product lifecycle.

AI Transparency Techniques

Open Data and Proprietary Data Description

Use and disclose publicly available datasets or provide detailed information about proprietary datasets used in training AI models.

• Finance: In credit scoring models, using open datasets can help in benchmarking the model’s performance against known standards during the data collection and model training phases.
• Customer Service: For AI chatbots, openly sharing the types of customer interaction data used for training can build trust during the model training and deployment stages.
• Healthcare: In diagnostic AI tools, using open clinical trial data for model training enhances credibility and allows for external validation.

AI Model Documentation

Maintain comprehensive documentation of AI models, including development processes, model architectures, and training methodologies.

• Finance: Documenting the development of fraud detection models, from initial design concepts to deployment, aids in transparency and compliance checks.
• Customer Service: Keeping detailed records of how customer feedback influences chatbot responses helps in refining the model during the maintenance phase.
• Healthcare: Thorough documentation of AI-driven diagnostic tools, including algorithm changes and updates, ensures ongoing clinical compliance.

Algorithm Auditing

Conduct regular audits of AI algorithms to assess their functioning, biases, and impact.

• Finance: Regular audits of trading algorithms can assess their market impact, ensuring fairness and regulatory compliance.
• Customer Service: Auditing chatbots for biases, especially in language understanding, is crucial in the deployment and review phases.
• Healthcare: Periodic audits of AI diagnostic tools to check for accuracy and biases, particularly post-deployment.

Ethical AI Frameworks

Implement and adhere to ethical guidelines and frameworks in AI development and deployment.

• Finance: Applying ethical frameworks in the development of investment algorithms to ensure they align with sustainability and social responsibility standards.
• Customer Service: Ensuring AI chatbots adhere to ethical guidelines in handling sensitive customer data throughout the lifecycle.
• Healthcare: Embedding ethical considerations in AI clinical decision support systems, particularly during design and testing.

Stakeholder Engagement:

Involve various stakeholders, including users, ethicists, and industry experts, in the AI development process for diverse perspectives and feedback.

• Finance: Involving stakeholders in the development of AI credit risk models to understand diverse credit needs and risks.
• Customer Service: Gathering feedback from both customers and service agents in designing and refining AI chatbots.
• Healthcare: Engaging with healthcare professionals and patients in the development of AI diagnostic tools to ensure practical relevance and usability.

Regulatory Compliance

Ensure compliance with relevant laws and regulations governing AI in different jurisdictions.

• Finance: Ensuring AI trading algorithms comply with financial regulations throughout their development and deployment.
• Customer Service: Adhering to privacy laws in AI systems that handle customer data, from collection to processing stages.
• Healthcare: Complying with healthcare regulations, such as HIPAA, throughout the AI product lifecycle, especially during data handling and model deployment.

Data Provenance:

Track and document the origins, transformations, and usage of data throughout the AI model’s lifecycle.

• Finance: Tracking the origin and changes of financial data used in AI investment models, especially during data collection and preprocessing.
• Customer Service: Maintaining a clear record of data sources and transformations for AI-powered recommendation systems.
• Healthcare: Documenting the sources and modifications of patient data used in AI-driven diagnostic tools, crucial during data collection and processing stages.

AI Explainability Techniques

Explainability by Design:

Integrate explainability considerations from the onset of AI system design, ensuring that the system is built with the capability to provide understandable explanations.

• Finance: In designing AI systems for assessing credit risk, explainability is integrated from the beginning. The system is structured to not only assess creditworthiness using complex algorithms but also to provide understandable explanations for its decisions. During the initial design phase, the AI is programmed to articulate why a particular credit application is approved or rejected, based on factors like credit history, income, and debts. This approach aids in compliance with financial regulations and builds trust with customers. The end result is a credit risk assessment tool that not only performs its task with high accuracy but also communicates its reasoning in a transparent manner, essential for both customer satisfaction and regulatory adherence.
• Customer Service: For AI chatbots in customer service, explainability by design means these systems are developed to not just respond to queries but also to explain their responses. From the outset, chatbots are equipped with mechanisms to provide reasons behind their suggestions or answers, making interactions more transparent and less frustrating for customers. This approach enhances customer trust and acceptance, as users receive not just answers but also the context or rationale behind them, improving the overall customer service experience.
• Healthcare: In the healthcare industry, AI diagnostic tools are designed with explainability in mind, to provide clear rationales for their medical diagnoses or treatment recommendations. During the development phase, these tools are created to analyze medical data and give not only diagnostic outcomes but also explanations that healthcare professionals can understand and use in their clinical decisions. Such a design ensures that medical practitioners are better equipped to trust and verify the AI’s recommendations, leading to improved patient care and adherence to medical standards.

In each of these industries, “Explainability by Design” ensures that AI systems are not just technically proficient but also user-friendly and transparent, providing clear, understandable explanations integral to their functionality. This approach is key in building user trust and meeting regulatory and ethical standards.

Interpretable Models:

Use inherently interpretable AI models like decision trees, linear regression, or rule-based systems where feasible.

• Finance: Using decision trees for credit scoring to provide clear reasons for credit approvals or rejections, beneficial during model design and deployment.
• Customer Service: Implementing rule-based systems in chatbots to make the logic behind customer responses transparent, useful in design and operational phases.
• Healthcare: Employing linear models in predictive patient care, providing clear insight into risk factors influencing predictions, crucial in design and deployment.

Feature Importance Analysis:

Employ techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to explain the influence of different features on the model’s output.

• Finance: Applying SHAP values in fraud detection models to understand the most influential factors, valuable in the model testing and review stages.
• Customer Service: Using LIME to explain specific chatbot decisions, helping in refining the AI during testing and maintenance.
• Healthcare: Leveraging feature importance tools in disease prediction models to explain which clinical factors are most critical, essential in the testing and operational stages.

Simplified Model Explanations:

Develop simplified versions or approximations of complex models to provide insights into how they make decisions.

• Finance: Creating summary explanations for complex risk assessment models, aiding non-technical stakeholders in understanding during the deployment phase.
• Customer Service: Providing simplified explanations of how AI recommends certain products or responses to customers, useful in deployment and customer interaction.
• Healthcare: Developing straightforward summaries of how AI models diagnose conditions, crucial for clinician and patient understanding during deployment.

Visualization Tools

Utilizing visual aids such as heat maps, decision trees, and graphs can significantly enhance the understanding of how AI models process and interpret data. These tools make complex information more accessible and comprehensible to a broad range of users.

• Finance: Visualization tools are employed to demonstrate the workings of AI trading algorithms. Graphs and heat maps can illustrate how these algorithms analyze market trends and make trading decisions. Primarily beneficial during the model evaluation phase for internal analysis and during stakeholder presentations to provide a clear understanding of the AI’s decision-making process.
• Customer Service: In the realm of AI chatbots, visualization tools like flowcharts or decision trees can depict how customer feedback and queries are processed and how the chatbot learns over time. These tools are particularly useful in the training phase to refine the chatbot’s responses and during stakeholder meetings to demonstrate the chatbot’s learning and adaptation capabilities.
• Healthcare: Heat maps are implemented in medical imaging AI, such as in MRI or CT scan analysis. These maps can highlight areas of interest or concern, aiding medical professionals in understanding the AI’s focus and diagnosis. Heat maps are essential in the operational phase of diagnostic tools, helping healthcare providers to interpret AI findings and integrate them into patient care. They are also useful in the development and testing phases for tuning the AI model’s accuracy.

Post-Hoc Explanation Methods:

Implement methods that provide explanations for AI decisions after the fact, especially for complex models like neural networks.

• Finance: In AI-driven trading algorithms, post-hoc explanation methods can be used to explain unusual market predictions or trades. In the post-trade analysis stage, the AI’s decisions can be reviewed and rationalized for compliance and improvement.
• Customer Service: For AI chatbots, post-hoc explanations help in understanding why certain responses were given to customer queries. Applied during the customer interaction review phase, this can aid in refining the chatbot’s responses and training data.
• Healthcare: In predictive diagnostics, post-hoc explanations can clarify why an AI system predicted a particular medical condition. This can be crucial in the post-diagnosis phase, helping medical professionals understand and trust the AI’s recommendations.

Human-in-the-Loop Systems:

Incorporate human oversight in AI decision-making processes to provide intuitive understanding and validation.

• Finance: In credit scoring models, human analysts review and validate AI-generated scores. For example during the model operation phase, ensuring that AI decisions align with human judgment and regulatory standards.
• Customer Service: Customer service representatives oversee and modify AI chatbot interactions when necessary. This can be integrated in real-time during customer interactions, providing a balance between automated and human responses.
• Healthcare: Doctors and medical professionals work alongside AI systems in making diagnostic and treatment decisions. This can be done throughout the patient diagnosis and treatment phases, ensuring that AI aids rather than replaces human medical expertise.

User-Centric Design:

Design explanation interfaces and tools that are user-friendly and tailored to different types of users, from experts to laypersons.

• Finance: Designing AI investment tools with interfaces that, alongside elucidating investment strategies or other affordances, explain how the AI processes data and makes decisions. ith AI investment tools. This could involve interactive elements like clickable icons that provide deeper insights into AI decisions or simple, jargon-free descriptions accompanying complex AI-driven analyses.
• Customer Service: Creating chatbot interfaces that can explain their recommendations or decisions in a simple, user-friendly manner. Additionally, design teams can consider how transparency issues or other AI failure points might impact customer support ladders and ticket routing.
• Healthcare: Developing patient-facing AI tools that provide easy-to-understand explanations of health data and AI-generated health insights. During design and testing, ensure that the tool is accessible and understandable, considering the overlapping or distinct needs of different user roles, such as patients and healthcare providers.

By combining these transparency and explainability techniques, AI systems can be made more open, understandable, and trustworthy, fostering greater acceptance and ethical use of AI technology.

Balancing AI Advancement and Transparency

The need for a transparent approach to AI has never been more critical. The fears and concerns surrounding AI, ranging from privacy invasions to biased decision-making, stem largely from a lack of understanding and transparency, and assurance that ethical AI best practices are being followed. AI transparency and explainability are not mere technicalities but fundamental pillars in building trust in AI systems and the businesses that use them.

AI transparency, which involves open disclosure of AI systems’ design, data, and operations, tackles challenges such as biases and ethical dilemmas. Explainability complements this by making AI decisions understandable to users, regardless of their technical expertise.

As we move forward, it is imperative that businesses and regulators prioritize transparency in AI.

Companies need to integrate transparency and explainability into their AI systems from the ground up, adopting best practices such as open data policies, comprehensive model documentation, regular algorithm audits, and user-centric design.

Regulators should work toward setting standards and guidelines that encourage transparency, ensuring AI developments align with ethical and societal values.

As we harness the power of AI, let’s work together to ensure that it evolves in a manner that is transparent, understandable, and, above all, aligned with human values and interests. The future of AI is a shared responsibility, and a transparent approach is the key to unlocking its vast potential while safeguarding, or even advancing, our societal and ethical ideals.