Let’s say you’re a loan officer — a customer-facing role in a highly regulated industry. If you tell a loan applicant they don’t qualify for funding, citing your firm’s fancy AI algorithm, do they trust that you’re doing the right thing? Maybe they’ve heard of cases in which AI decision-making processes were biased, and they’re skeptical.

Their reaction could depend on how transparent your company’s AI systems are. From model development to the interpretation of AI decisions, transparency around AI’s inner workings can change everything.

When people know why an artificial intelligence system has come to a conclusion, even if the decision isn’t favorable for them, they’re naturally likely to feel reassured.

What is AI transparency?

The concept of AI transparency emerged in the 1970s, when automated systems were unveiled to rate people’s creditworthiness.

AI transparency — making computational results explainable in a way that people can grasp — implies a clear viewing of what’s taking place. With AI technology, transparency amounts to openness: being able to understand how a machine-learning algorithm is processing or has processed information and being able to explain it in a way people can comprehend.

What logic was applied? What inputs and outputs were involved? Knowing what’s influenced an algorithm’s workings is aptly known as algorithmic transparency.

How and why an algorithm arrived at a decision about a loan is an example of algorithmic transparency, and available details might include:

• Whether AI was used to make the decision
• How the AI tool was produced and put into use
• The role the AI played in the decision process
• How its workings were monitored and fine-tuned

Why AI transparency is critical

Without transparency, trust can be lost, and that’s a huge deal. So experts in the business, government, and consumer realms agree that when it comes to AI system output, transparency isn’t optional; it’s required for building trust.

Being able to see how AI generates output reframes it from an independent agent making decisions to a tool used by humans.

According to AI transparency experts Reid Blackman and Beena Ammanath, building transparency into AI systems lowers the risk of error and misuse, distributes responsibility, and allows for internal and external oversight.

Cultivating and maintaining customer trust has always been a priority, but now, with the advent of generative AI and large language models (LLMs), it’s taking on new significance.

Knowledge is power: everyone from developers to CEOs to consumers needs visibility on how AI is “thinking” in order to get the whole picture. And transparency is not just essential for protecting customer trust, it’s a foundational requirement for business success.

A business must understand how each phase of AI processing impacts output and make adjustments as needed, such as by compensating for bias in conversational AI. It must also be able to ensure accountability.

Components of AI transparency

Interpretability: What the internal parameters mean

AI models remember anywhere from a handful to billions of numbers that are involved in the complex math AI performs. Those numbers, to some extent, can represent “concepts” learned, and the exact combination of those numbers defines the model’s behavior.

But how easily can a human understand the complex math an AI model performs? This is interpretability: how well the AI internal parameters map to human concepts.

The goal of interpretability is to figure out what the individual parameters do. The more that parameters can be mapped to human concepts, the more interpretable the AI can become.

Explainability: How the output relates to the input

Treating AI as a tool that needs to explain itself instead of an unquestionable, independent agent is explainable AI. However, unlike interpretability, explainability doesn’t require knowing what each parameter does; it’s focused on disclosing why a decision was reached in a way that humans understand.

How might this work, for example, with someone denied a loan or not admitted to college? The response letter could disclose the AI input (e.g., credit history) used in the calculations and note which type of input had the most significant impact on the decision.

If they can point to how the model was trained and performed according to expectations, they can potentially ward off criticism.

Accountability: Who gets the credit or blame

It’s natural to expect people to be accountable for their actions. With algorithms, that prospect is murky; how do you hold a machine accountable? You can’t.

However, the humans involved in building, training, and operating an AI system can be held accountable if it can be proven that their contributions directly caused an unfavorable outcome.

But having someone unambiguously accountable is also complicated, as nobody wants to assume that liability

Benefits of transparent AI systems

Ensuring trust is, of course, the overarching benefit of being transparent with AI. Trustworthiness encompasses a variety of advantages as well:

• Ensuring fairness: If the algorithmic process is explainable, potential concerns about bias can be caught and addressed before they become problems
• Satisfied customers, whether they’re consumers impacted by a healthcare decision or shoppers on an ecommerce site
• Improved efficiency by being able to see patterns in the deployment of an algorithm
• Legal defense in the event of a lawsuit

Challenges of making AI transparent

First, the good news: with advance planning, transparency as part of a responsible AI practices ecosystem is thought to be achievable.

When Harvard Business Review tested various AI models on representative datasets, they discovered that 70% of the time “there was no tradeoff between accuracy and explainability: A more-explainable model could be used without sacrificing accuracy.”

But while AI transparency may be technically achievable to varying degrees, if transparency isn’t prioritized at inception, say developers, the toothpaste will be difficult to put back in the tube later. And, depending on the application, even with the best intentions, there may still be some formidable obstacles.

AI use cases vary wildly in terms of how much information can be understood about systems’ inner workings. In addition, “transparency” is a broad concept with no single agreed-on definition.

Like other aspects of AI use, it’s an actively evolving discipline, and that complicates well-meaning pursuits such as setting and enforcing standards.

Challenges with AI transparency include:

• Some machine-learning algorithms are cloaked in secrecy. What’s going on in the black box is impossible to illuminate because it relies on random external factors.
• Certain AI applications must remain opaque in order to protect proprietary operations.
• Some explanations still lack transparency. Why? While computer programming is usually a predictable process that boils down to simple math, meaning it’s technically “transparent,” that doesn’t mean laypeople (and even some engineers) understand it. The numerical language neural networks “speak” may conform to transparency guidelines, but its output is like gibberish to humans and needs to be “translated.”
• There are no uniform transparency requirements for information disclosure and training data, so companies may not act consistently, and AI models could be treated differently.
• Some data (e.g., healthcare details) may need to be shared in order to ensure transparency; making AI more transparent could violate privacy laws and basic ethics.
• Complex algorithms require more effort to document and explain AI activity. Keeping an AI model transparent as it evolves (for instance, when it’s trained on a new dataset) can also be taxing.

AI transparency regulations

While AI development has certainly been moving along, laws that govern the technology's transparency, accountability, and other “ethical” aspects are still in various stages of evolution.

Deciding on global standards for transparency is like herding cats, as companies, developers, ethics proponents, and policymakers must all weigh in and agree on initiatives.

A few comprehensive laws requiring AI systems to be transparent for legal and ethical reasons have been formulated, but globally, legal guidance meant to govern AI is inconsistent.

Current AI-related legislation includes:

• The international OECD (Organisation for Economic Co-operation and Development) has a set of principles for AI use
• The European Union has the GDPR (General Data Protection Regulation), which requires companies to give consumers explanations for how AI has made decisions that affect them
• The AI Act is “the first comprehensive regulation on AI by a major regulator anywhere” (guidance)
• The American GAO (Government Accountability Office) has a framework for transparency with AI-produced results
• The CCPA (California Consumer Privacy Act) dictates that people have a right to know inferences made about them by AI systems, as well as the data used

What’s next for AI regulations? MIT Technology Review anticipates that “the first sweeping AI laws” will go into effect soon.

AI transparency best practices

Following best practices for AI transparency in its current state can promote trust among businesses, developers, and customers.

Below are some strategies companies can apply to start making their AI machine-learning model processes more transparent:

• “Use simpler models” advises MOOC, and combine them with more-sophisticated models. “While the sophisticated model allows the system to do more complex computations, the simpler model can be used to provide transparency.”
• Create models with transparency built in from inception, which means decisions will be interpretable by humans if needed.
• Track and rigorously document all changes and updates made in your data and algorithms so the process can be audited if ever needed.
• Publish transparency reports on a regular basis, noting implications your stakeholders should be aware of.
• To track dependencies between inputs and outputs, do a test by changing some inputs and seeing if the results change.
• Clearly communicate with stakeholders about your data practices and how you’re working to avoid bias.
• Proactively let customers know the ways their personal data is being used with easy-to-understand explanations.
• To reduce the chances of bias, be open about how you train your models and get feedback.
• Identify and remove inherent bias, keeping good records.
• Openly share and publicize the ways you’re working to ensure fairness, disclosing the types of data your AI models are processing.
• Discuss the justification behind your data selection so people can grasp the model’s capacity.

Build trust with transparent search

Search is one area where it’s critical for a business to understand how an AI system impacts the results people see.

With Algolia, search relevance computations are transparent: you’ll know what our AI features do, where and how they impact the user experience, and how they work alongside other features.

Your marketers will see how search results are ranked based on personalization and relevance factors; they can then assess and manually adjust the results.

Check out the advantages of smart search with an Algolia demo.