What is AI Transparency: A Comprehensive Guide

What is AI Transparency? 

AI transparency involves giving your customers and stakeholders an inside look into the system's architecture. This architecture must be ethical, responsible, safe, and compliant with the law.

With AI transparency, your end user must know how the system works, what data is collected, how they make certain decisions that lead to specific results, and how they protect it.

The Benefits of AI Transparency 

1. Improve Data Accuracy 

The field of AI typically involves models that analyse complex patterns in data and produce relevant predictions. However, the output data is often seen as unreliable, as it’s difficult for people to understand how the learning models arrived at certain conclusions.

A transparent AI process can be beneficial especially in domains like customer service, where insights into how machine learning models decode data and draw conclusions can help humans have confidence in the predictions and better understand the underlying customer needs.

2. Identify and Remove Data Biases 

Every AI system has a certain amount of irreducible complexity that can sometimes lead to bias, which in turn can potentially affect consumers and users. 

While fixing bias and fairness issues in AI systems is no small feat, it is not impossible with AI transparency.

By pinpointing the origin of the data set, you can figure out what is causing a bias. Data from specific demographics or time periods might skew the results.

Additionally, having data from under-represented groups in the training model—such as people who might be prone to discrimination or stigmatisation due to their financial, social, or health-related conditions—can help you reduce bias in the system.

3. Increase End-User's Trust  

Transparent models support trustworthy ML development and provide answers to user questions like "how," "when," "what," etc.

This transparency is extremely beneficial for building both customers and users's confidence in AI systems, as revealing the underlying reasoning and decision-making of the process makes it easier for them to understand the model's behaviour and predictions.

Regulations and Standards of AI Transparency 

A good number of organisations and governing bodies have a defined set of principles that act as a guiding force for AI implementation and beyond. This include:

1. GDPR

The General Data Protection Regulation, or GDPR, is a security law passed by the European Union in 2018 to protect EU citizens data. 

While GDPR's primary focus is on data protection, it still significantly influences AI practices in the EU — especially how AI systems handle personal data.

It states security requirements such as:

• Provide transparency on how data is collected and where it goes. 
• Establish a data protection design with industry best practices such as authentication, encryption, and internal training.
• Gain consent from users before processing their data. 
• Ensure customers have the right to access, modify, and erase their data.

2. EU Artificial Intelligence Act

A few days earlier, the world's first major AI regulation came into force, the EU AI Act.

Aligned with GDPR principles, the aim of this act is to regulate the use of artificial intelligence within the European Union and ensure inclusive, transparent, and environmentally friendly AI development.

In this regulation, AI systems are classified into four categories based on their risk levels: unacceptable, high, limited, and minimal. Higher-risk categories face stricter requirements.

1. Unacceptable risk: AI applications that manipulate human behaviour are outright banned due to their inherent risks to fundamental rights. Examples include:

• Social scoring systems that rank citizens based on their behaviour and personal data.
• Real-time biometric identification in public spaces
• AI systems that encourage dangerous behaviour among minors and vulnerable groups.

2. High-risk: These are AI systems that are subject to risk assessments before being placed on the market. To be compliant with the law, the system must be trained on high-quality data to minimise the risk of biases and share the detailed documentation of models with authorities.
3. Limited Risk: Chatbots and generative AI tools must clearly disclose to users that they are interacting with a machine. 
4. Minimal Risk: These systems face no obligations under the AI Act due to their minimal or no risk to individuals or society. Examples include AI-enabled recommender systems and spam filters.

3. OECD AI Principles 

The Organisation for Economic Co-operation and Development (OECD) is governed by a Council made up of representatives from 37 countries. The core aim of this regulation is for AI to be developed in accordance with human-centred values such as social justice, consumer rights, and commercial fairness.

It states the following transparency requirements:

• AI systems should be designed and operated in a way that makes it easy for end users to understand how the system works.

• Companies and individuals responsible for AI systems should be accountable for their actions and decisions.

• Systems should perform consistently as intended and be resilient to adversarial attacks.

• Avoid any biases or discrimination against individuals or groups.

AI Transparency Best Practices 

Some open-source toolkits and methods that can be adopted by anyone in creating transparent models, include:

1. Data Cards

Data cards are structured summaries developed by Google to track and document different elements of a machine learning dataset as it develops. This includes the data set's origins, development, and intent.

This summary helps you provide explanations of AI processes and rationales that shape the model — which ultimately explains the outputs.

2. Model Cards

Data cards and model cards are similar, but they are not the same. Model cards are short pieces of documentation that help users get more in-depth information about the model in use.

This card collects essential facts about a model's characteristics, such as intended use cases, and organises them in a structured way.

While the ideal audience for model cards varies according to the purpose of the AI system, typically they are created for policymakers, regulatory bodies, and any researcher who wants more information about the model in question.

3. Use Pre-trained Models

Pretrained models are deep learning models that have been trained on a massive dataset.

The reason why pretrained models are useful for AI transparency is because they often come with extensive documentation and research papers explaining their architecture, training process, and performance. This helps the end user understand how the model works and its limitations.

There are a wealth of pre-trained models available online that can save you valuable time, energy, and resources, including:

• GitHub
• Kaggle
• TensorFlow Hub
• APIs from cloud platforms like AWS, Google Cloud
• Startup machine learning models such as Scale AI, Hugging Face, and Primer.ai.

Pro Tip: Although using pre-trained models offers several benefits, they may not be directly applicable to every industry's needs and can be tricky to customise. To address this, consider using a baseline model. Learn more below.

4. Establish a Strong Baseline Model

Baseline models are simple models that act as a basis for evaluating the performance of more complex models. 

When creating a strong baseline model, consider both business and technical needs, validate the data engineering process, and test the deployment pipelines.

By using baseline models as a reference point, you can gain key knowledge about the efficiency of your new models and whether they have the potential to progress over time.

Rounding Up

When implementing an AI system, there’s more to consider than ease of use, pricing, and a strong product concept.

Make sure your system complies with industry regulations, aligns with both your business and customers' core values, and minimises environmental impact. Only then can you develop a product that your customers fall in love with and a business that thrives over the long term.