By Nathan Rayens, Data Scientist, Data Surge
As we continue our series on responsible artificial intelligence (AI), we’re exploring each key element of Responsible AI in greater depth. Today, we’ll focus on how to offer end users transparent AI products, and more importantly, discuss why transparency is crucial in the fastchanging world of AI.
Our recurring disclaimer is that machine learning techniques are often grouped with AI in popular forums, so this article will do the same.
Defining Transparency in AI. What Does it Look Like?
Before we proceed, it’s important to clarify what we mean by transparency in AI development. In simple terms, transparency involves offering users a clear, behind-the-scenes look at how our AI systems work, so they aren’t left confused by their interactions with these systems. This starts with fundamental steps, such as informing users they are engaging with an AI tool and acknowledging that AI can make mistakes. Beyond that, transparency involves auditing our data to address biases and prevent malpractice. Perhaps the greatest challenge is explaining why an AI system behaves the way it does, whether based on its rules or data patterns.
Ultimately, transparency is about being a responsible guardian of user experience and safety by maintaining open communication. AI is radically changing the way we operate and it is completely reshaping our tools, but transparency ensures we don’t lose our way as we navigate this uncharted territory.
Why Transparency?
Designing or modifying your system to ensure transparency and explainability can often require significant effort. In fast-paced development environments, these tasks might feel like obstacles delaying your team’s progress. However, at Data Surge, we believe that strong customer relationships are built on trust. While it may take extra work to design or modify an AI system for transparency, the benefits are clear: customers deserve a reliable partner, and your commitment to transparency will earn their trust and loyalty.
Beyond customer responsibility, you may soon have no choice but to implement these changes. Governments and regulatory bodies, including those in the EU and the United States, have already introduced legislation mandating AI transparency. While the specifics of enforcement are still being finalized, it’s certain that these regulations are here to stay. Taking steps now to ensure your AI products comply with these standards will save you the extra effort of catching up later.
Steps to Incorporate Transparency into AI Systems
Having established the significance of transparency from both trust and compliance standpoints, let’s explore ways to enhance transparency in both new and existing AI products. Each section will outline practical, actionable steps to help improve AI transparency.
Gather Requirements on Compliance and Expectation for Transparency
Background: Current regulations often categorize AI systems based on their use case and audience. For instance, the EU AI Act requires products that interact directly with humans to inform users that they are engaging with AI. On the other hand, for generative tasks like image creation, the same legislation mandates machine-readable labels indicating that the content is AI-generated. With these distinctions in mind, you can assess what level of transparency is appropriate for your specific product.
Action Item: Leverage this understanding of transparency requirements to evaluate the current state of your solutions and determine what steps are necessary to ensure compliance. For example, you might need to add an “AI-generated” watermark to newly created images or implement a modal window that informs users they are about to interact with an AI decision-making service before proceeding
Investigate Your Data Sources and Record Lineage
Background: The foundation of any AI system is the data it was trained on and the data it processes. One of the biggest risks in AI development is using biased or inappropriate data in your models. To avoid these pitfalls, it’s essential to know where your data comes from and the
intended purpose of that data. Although this level of detail isn’t commonly shared with end users, being mindful of your data sources helps to foster trust because you can truthfully convey knowledge and control of your system.
Action Item: Review your AI system’s data pipeline, particularly around model training, and document the data sources. Additionally, note any accommodations made for data handling, sensitivity protocols, and security measures. Assess the transformations or modifications applied to the data to prepare it for model interaction. These details are not only useful internally but can also support compliance and transparency efforts. Where relevant, consider sharing with users how your models are built on carefully selected and responsibly managed data sources, especially in cases involving human interactions.
Consider the Explainability of Your Systems – Pick the Least Convoluted Solution
Background: To enhance transparency in your systems, it’s crucial to prioritize explainable models over more complex ones whenever possible. Often, various modeling approaches can achieve acceptable performance through different methods. For instance, you might have the option to choose between a straightforward rules engine and a highly complex deep neural network (DNN). While the DNN might deliver superior performance metrics, it comes with the significant drawback of reduced explainability. Specifically, without further work, it will be nearly impossible to determine which input data features are influencing the final outcomes as you move away from training.
Action Item: Review your architecture and model selections to determine if your business needs can be met with a more inherently explainable model. If so, start with simpler, more interpretable models and benchmark their performance. Compare any potential reductions in metrics with the added benefits of improved transparency in decision-making. At Data Surge, we understand that not all use cases can be addressed by the simplest models, but being deliberate in your complexity audit process can significantly enhance your ability to explain AI behavior to both you and your customers.
Employ Explainability Metrics Where Simple Solutions are Inappropriate (and in general!)
Background: As previously noted, not every model can simply replace another. It’s unrealistic to expect that the sophisticated capabilities of advanced image and text processing systems can be duplicated by a basic rules engine. When a complex AI system is necessary, it’s still
important to apply industry-standard techniques for enhancing transparency. Methods such as SHAP and LIME can be used with any model to provide some basic insight into its decisions (i.e., “These 6 input features are associated with negative responses”). Additionally, feature importance can be assessed using permutation techniques or, less frequently, counterfactuals, helping us understand which data components the AI system relies on most.
Action Item: Identify the metrics and techniques that are suitable for your AI system and explore how to integrate them effectively. Develop a method for monitoring and documenting these metrics to provide business value and support iterative improvements, even if they are not reported to end users. While incorporating these techniques may increase computational costs, we believe that being able to identify and explain key characteristics is crucial for transparency and represents sound scientific practice.
Go to the Source – Discover What Aspects of Transparency Customers Are Wanting
Background: User feedback is a crucial tool for understanding how your solutions are perceived from an external perspective. When the AI experience falls short, users are often eager to point out what’s missing or what went wrong. For instance, if a chatbot provides an incorrect answer, users may become frustrated, particularly if they cannot discern the source of the presented information or verify its accuracy. As AI providers, it is our responsibility to address these concerns by creating mechanisms to reveal how and why that chatbot made its decision.
Action Item: Actively and passively gather user feedback. Provide opportunities for customers to share their experiences directly within the AI system when possible. Additionally, take all complaints and escalations seriously and document them thoroughly. Addressing visible issues
will not only resolve individual concerns but also significantly contribute to building trust with your users.
Share What you Learn
Background: Throughout this blog, we’ve highlighted the significance of sharing your findings whenever possible. This section emphasizes its importance for building trust. While regulatory requirements might only mandate informing users that they are interacting with an AI system, providing additional insights—such as the sources of data or the features used in decisionmaking processes—can help clarify why the results are as they are.
Action Item: Assess your willingness to share information from your AI transparency efforts. While it may not be feasible to disclose every technical detail to end users, sharing as much as possible can significantly enhance confidence and trust. For example, demonstrating your commitment to proper data handling can strengthen your users’ belief in the fairness of your models.
Execute Your AI Transparency Plan and Continuously Refine It!
Background: Keeping the previous steps in mind, you can start sharing your findings with regulatory agencies and/or end users as appropriate. As requirements evolve and your relationship with customers shifts, it is crucial to continuously update and refine your transparency measures in tandem with the maturation of your AI system.
Action Item: Regularly review and update the previous steps based on your company’s resources, market segment, and requirements. Transparency is an ongoing process, and there will always be opportunities for improvement.
Conclusion
Transparency is a crucial aspect of responsible AI, serving as the means to build trust with our customers and ensuring that we use technical advancements ethically. It is vital for all companies involved in AI to evaluate their commitment to transparency and make genuine improvements where needed. By openly sharing and adhering to our transparency initiatives, we collectively enhance our responsibility to make AI a positive and secure experience for everyone.
Please reach out with any questions or feedback. At Data Surge, we are always eager to connect and learn more about how you are navigating the AI landscape!
