In 2015, one of the largest e-commerce companies built an automated hiring tool to review and filter resumes based on skill sets and job descriptions. However, this project had to be halted after it was noticed that the hiring tool showed significant racial bias and systematically discriminated against women applying for technical jobs.
This highlighted a critical challenge with AI systems: it's difficult to identify and prevent discriminatory patterns in their outputs without understanding how these models make decisions. This is why we need explainable AI (XAI) techniques and methods that make decisions about AI systems understandable to humans by providing transparency and interpretability.
AI systems are being widely adopted for making critical decisions across finance, law, healthcare, and e-commerce platforms; we must understand their technical architecture and decision making processes. This includes examining how the system generates outputs, the model’s training methodology, and whether the training included appropriately diverse datasets during development.
As organizations adopt AI to improve workflows and decision-making, the over-reliance on these tools or potential for errors increase the associated risks, mainly due to the lack of understanding of how these systems make decisions. For instance, understanding how an AI diagnostic system arrives at a diagnosis or recommends a treatment plan is crucial for patient safety in a medical treatment.
In autonomous driving, we need to understand how the AI navigates complex traffic scenarios to ensure passenger safety and maintain regulatory compliance. In each case, stakeholders and users must understand how the model arrives at decisions, why specific choices are made, and how alternatives are evaluated.
The need for explainability
AI systems, specifically the ones based on neural networks, often work like black boxes that provide output without explaining how the system arrived at a conclusion, which makes it difficult to assess the trustworthiness of their outputs. For example, healthcare providers must understand how an AI diagnostic tool recommends a treatment plan to ensure patient safety. Similarly, in finance, transparency in loan application processing is critical for ensuring fairness and avoiding bias.
Adding to the challenge, many AI models are too complex to interpret, and even for tools designed to explain their decisions, which can have severe consequences. For example, if the healthcare provider can't understand a particular surgery or diagnosis provided by an AI system, it can lead to incorrect treatment, severe injury or death, and compromised patient care. The inability to explain AI's behavior in the finance or legal domain may lead to biased decisions and penalties.
As we can see, explainable AI is critical for understanding the reasoning behind an AI system's decisions. With the increased use of AI systems we see increased concerns about ethical practices, transparency, and algorithmic biases. Data privacy and security regulations, such as the GDPR and CCPA, require enterprises to be transparent about how personal data is processed, stored, and transferred. Newly designed AI laws build on these requirements while focusing on explainability.
The European Union’s AI Act has introduced rights for individuals impacted by high-risk AI systems, which includes access to clear and meaningful information about how AI influences decisions and the key factors involved. It does not demand a comprehensive 'right to explanation' for the users, but these provisions aim to improve explainability to uphold transparency, accountability, and fairness in AI-driven decision-making.
Core Concepts of Explainable AI
Explainable AI frameworks help users and developers understand how AI models make decisions, making the process transparent and interpretable. Explainability is generally classified into two types: global and local.
- In global explainability, we get a bird's eye view of an AI model’s overall behavior. This can help users uncover algorithmic and data-driven biases or systemic errors. Techniques like decision trees, linear regression, and other interpretable models are used to understand and visualize global behavior.
- Local explainability, on the other hand, focuses on specific predictions or decisions. It highlights the factors influencing individual outcomes, providing granular insights.
For example, in a housing price prediction model, global explainability might analyze historical data and current market trends, clarifying the factors that broadly determine home values. Local explainability would offer details about specific predictions, such as how renovations, the number of bedrooms, or location influenced the price of an individual property. These explainability models are critical for building trust in AI systems and uncovering both model behavior and individual predictions.
Explainable AI Techniques
Several widely used techniques can explain AI models and their behavior. Below are some of the most common techniques:
SHAP (SHapley Additive exPlanations):
SHAP measures how much each feature contributes to a prediction. It assigns values to features, with positive SHAP values indicating a positive impact and negative values indicating a negative impact. Based on game theory, SHAP treats each feature as a "player," measuring its contribution to the final outcome. For instance, in housing price prediction, SHAP can highlight how location, school district, or property size influenced the predicted price.
LIME (Local Interpretable Model-agnostic Explanation):
LIME explains individual predictions by approximating the behavior of complex models locally. It’s mainly useful for understanding why specific outcomes occurred. For example, LIME can explain why a particular treatment was recommended in healthcare or why certain items were included in a credit card bill.
Visualization Techniques
Visualization techniques convert complex AI decisions into a visual format that humans can easily understand. These techniques transform data and the entire decision process into graphs and heat maps, making understanding and interpreting AI systems easier.
Some of the visualization methods that are commonly used are:
- Decision trees: Decision trees have flowchart-like structure that can help explain step-by-step decision making. Decision trees are commonly used in banking, healthcare, and customer service domains, where people need to know how certain decisions are made.
- Interactive dashboards: These allow users to explore biases, patterns, and anomalies in AI models.
- Heatmaps and Saliency Maps: Commonly used in image analysis to highlight important areas of focus, such as suspicious regions in X-rays. These maps help humans understand what AI is looking at when it makes decisions.
Challenges and limitations:
Explainable AI faces significant challenges that make this task complex. More accurate and high performing AI systems come with increased complexity, making it difficult to explain these large models. AI tools consume extensive computational power to process billions of parameters. Explaining each step throughout the decision-making process is computationally intensive and can cause resource contention, making it unsustainable.
]Explainable AI solutions might not be practical when quick decisions are needed, as the process requires time to analyze each step. Finding the right balance in information sharing is challenging - too many details can overwhelm systems and users, while too little information misses essential data points. Human biases and perceptions affect how interpretations are consumed, leading people to either over trust or completely distrust AI explanations. Developing frameworks to help users appropriately interpret and challenge AI explanations remains an open question.
Future Directions:
Explainable AI is becoming popular with many new developments focusing on building trust and improving explainability. While earlier systems were limited to static explanations, new dynamic based systems use dialogue or conversational AI for human-tool interactions. This allows users
to ask follow-up questions and better understand decision-making processes. The integration with AI frameworks and explainability tools aims to increase transparency and fairness through bias detection and mitigation. Additionally, self-explainable neural networks can generate high-quality automated explanations without sacrificing performance.
Different industries are developing tailored approaches for their specific contexts. Healthcare and education prioritize visual explanations, data centers require real-time network management and monitoring systems, while financial and legal sectors need regulatory compliant explanatory frameworks. These tailored approaches improve the applicability and trustworthiness of Explainable AI across sectors.
