AI Transparency: A Step-By-Step Guide for Your Business

Both small startups and big companies are now using AI systems to analyze data, personalize marketing strategies, streamline supply chains, and automate repetitive tasks.

In 2022, nearly 35% of businesses implemented AI technology, marking a 4% increase from 2021. The adoption rate is predicted to climb even higher by the end of 2023.

IBM

The more businesses are going to use AI, the more impact it will have on people’s daily lives, extending to critical decisions, like treatment recommendations or participants’ selection for clinical trials of cancer drugs. This calls for heightened responsibility and higher levels of transparency in technology. In this step-by-step guide, we’ll explain the benefits of transparent AI, reveal potential barriers to understanding its decision-making, and suggest proven ways to enhance transparency.

Transparent AI is Explainable AI

AI transparency is achieved when algorithms can be communicated and explained. Yet, it is not about sharing algorithms online or publishing lines of code. The goal is to explain why a specific decision is made rather than simply showcase what is happening under the hood. When a technology makes an error, businesses need humans to make judgments. Therefore, it is important to be able to grasp the context in which the AI model functions as well as possible implications of the outcomes.

The level of transparency must be positively correlated with the impact of AI-driven technology. The more impact the algorithm has on people’s lives, the more essential it is that all ethical concerns are tackled, and decisions are explained. For instance, an algorithm to send personalized emails to schoolteachers does not require the same level of examination as messages sent to healthcare providers (HCPs).

When developing a new feature for our advanced content experience platform to enable pharma marketers to assess content tailored for HCPs, we fully understood the significant impact our AI model would have. Therefore, it was essential for our company to adhere to the highest AI transparency standards.

More specifically, we made sure that users could access the current MLR rules used by the algorithms for the prediction of content approval. Our team made our engine show the set of standards along with corresponding comments for the content pieces that are not likely to be approved. This not only increased the chances of initial content approval but also enhanced user trust, as they saw the specific criteria for why content was flagged for further review. That kind of transparency helped us make pharmaceutical companies rely on our solution without crippling fear of failing such an important stage in the marketing process as MLR review. 

Key benefits of transparent AI for your business operations

Why would a business want its critical AI systems to be transparent? Whether you build your AI-powered product or make use of ready-made solutions, it is crucial for you to understand what is happening inside the tool’s black box for a few compelling reasons. Having a meaningful explanation of how the solution gets to a decision builds trust. This is, in fact, one of the main reasons why we reveal the data source used to train our product. When clients understand that AI decision-making is grounded in their unique data sets, they tend to place more trust in certain solutions.

AI-based models, much like humans who develop them, are prone to bias. Failure to understand the underlying algorithms can lead to these biases going unnoticed, threatening business health, compromising customers’ safety, or promoting unethical behaviors. For a company, it can have disastrous consequences potentially resulting in losses of millions of dollars and, most significantly, serious reputational damage. Dealing with the breach of customer trust is an arduous process, often spanning many years.

Some heavily regulated industries, like pharma and life sciences, have model transparency as a crucial step for obtaining legal approval before a solution can be deployed.  Ensuring transparent AI systems helps businesses meet a range of compliance laws and regulations, such as General Data Protection Regulation (GDPR) or the Algorithmic Accountability Act (AAA). This not only allows them to minimize chances of legal and financial ramifications associated with biased AI but also shows a company’s commitment to adhere to ethical and socially responsible practices.

Main challenges in understanding AI decision-making

The first step to greater AI transparency is identifying key barriers to understanding AI decisions. Without further ado, let’s tackle some of them.

Unexplainable algorithms

While some tools are relatively easy to interpret, like planning algorithms or semantic reasoning, there is a range of AI data-driven technologies, where explaining a connection between input and output is considerably more challenging. Advanced models, such as machine learning (ML), are often described as black boxes with billions of different parameters, which makes it nearly impossible to pinpoint how a particular input led to a specific output result.

Poor visibility into training data

AI tools may inherit biases from data used to train them. If the training data does not represent real-world data, it will taint the accuracy of the AI model. In light of this, businesses need to raise the following important queries:

• What is the source of the training data?
• What are the functionalities upon which the model was trained?
• What methods were used to rectify the data?
• Can we have access to this data?

Without clear answers to these questions, businesses have limited visibility into inner model’s processes and cannot have full confidence in its safety.

Lack of understanding of data selection methods

If a company gains access to the full set of data, would it mean that the model is transparent enough to be used? Not always. Even when businesses get access to gigabytes or terabytes of training data, it does not necessarily suggest that they understand what aspects of data were utilized to create a given model. What if data scientists decided to implement data augmentation approaches and added data, which were not included in the training data set? What if ML engineers selected particular data or features from the data set? To guarantee higher levels of transparency, it is important to be able to use the same selection methods on the training data to understand what data was excluded and what data was included.

Effective ways to enhance AI transparency

In general, there are three common ways to increase transparency of your AI solution: ensuring the model’s technical correctness, checking training data for biases, and using technology to validate AI algorithms.

Ensuring technical correctness

To make sure the AI tool is technically correct, businesses must carry out a range of appropriate tests and deliver thorough documentation, including detailed description of the architecture and performance metrics. The software developers who built the system should be able to explain how they addressed the problem, why a specific technology was selected, and what data was used. Team members must audit or replicate the development process, if necessary.

The ATARC AI Ethics and Responsible AI working group has suggested the document that allows model developers to evaluate their algorithms based on five factors of transparency, such as algorithm explainability, reduction of data set bias, methods of data selection, identification of data sources, and model versioning method. Engineers can assign points for each of these factors. For example, if a system scores a 1 for algorithmic explainability, it means a model is a black box, whereas a 5 rating for training data transparency means full access to data sets is provided.

This approach is just one of the examples of possible model transparency assessments. Regardless of whether you’ll adopt this specific method, it is essential to make this self-assessment a part of the model release. Still, despite obvious benefits, like developers’ accountability for their choices in the model design, this approach has not escaped some drawbacks. Self-assessment may introduce subjectivity and variability in the review process, as different engineers may interpret transparency factors in a different way.

Checking data for biases

Beware of hidden biases in the training data, as they may directly impact the system’s output. With that being said, it is essential to check if some groups are under-represented, and you need to take corrective action to remedy that. Suppose your content experience platform was fed historical data that mainly included preferences of young male healthcare providers. As a result, the given AI model may struggle to recommend relevant content to women or older professionals.

AI models cannot identify biases in training data, which is why you’ll need to rely on your employees who understand the context in which this data has been gathered. Therefore, bias mitigation can be a time-consuming endeavor that requires continuous scrutiny.

Using technology to validate the model

Advanced AI algorithms must be validated to allow businesses to understand what is happening inside the models. Today, there are a range of tools available to help companies take a closer look inside the AI’s black box” helping them detect biases in training data and explain the model’s decision-making to both customers and employees. The main trade-off of these solutions, however, is that they may not be universally applicable to all AI models.

While each of these methods contributes to AI transparency, it is worth considering their combination for a more holistic and well-rounded solution. By blending these approaches, businesses can uncover the room for improvement that might otherwise remain hidden when using them in isolation.

Towards greater transparency

Businesses cannot place trust in any technology or a third-party source without a comprehensive understanding of its inner workings. One of the reasons why they might fear AI models is because they can be incredibly hard to explain. If a company lacks information about whether the training data was adequately cleansed or checked for bias, they might presume that the model’s output could be skewed as well. Therefore, a question of accountability in AI naturally comes into play. Businesses using AI systems need to keep in mind the ethical, legal, and financial aspects of their operations to ensure that they not only leverage the AI’s potential but also safeguard against the potential ramifications.