Explainability, Trust, Responsible AI Flashcards
What is the main goal of Explainable AI (XAI)?
The goal of Explainable AI is to make the decision-making process of AI systems understandable and transparent to human users. It aims to provide clear explanations for why an AI model arrived at a specific outcome, building trust in AI systems.
What is the “black box problem” in AI?
The “black box problem” refers to the opacity of many AI systems, especially deep learning models, where even the developers or engineers cannot fully explain how the model arrives at its decision. This lack of transparency makes it difficult to trust and audit AI models, particularly in high-stakes situations.
What are the three main components of Explainable AI?
- Prediction Accuracy – Ensures that the AI model’s predictions are reliable and match known outcomes in training data.
- Traceability – Allows us to track how individual decisions were made, often by observing the behavior of neural networks (e.g., using DeepLIFT).
- Decision Understanding – Provides human users with understandable explanations of AI’s decisions, often in the form of dashboards or visualizations.
What is Local Interpretable Model-agnostic Explanations (LIME)?
LIME is a technique in Explainable AI that provides local explanations for individual predictions made by an AI model. It approximates the complex model around a specific instance, allowing humans to understand why a particular decision was made. LIME helps in understanding black-box models by simplifying them locally around each prediction.
e.g. select an instance in the data set and then display as explanations:
(1) the model’s predictions
(2) features contributions
(3) the actual value for each feature
What is DeepLIFT?
DeepLIFT (Deep Learning Important Features) is a traceability technique that compares the activation of each neuron in a neural network to its reference neuron. This helps to track which features of the input data contributed most to the model’s decision, enhancing traceability and transparency.
Why is Explainable AI important for trust?
Explainable AI builds trust by providing users with clear, understandable reasons for AI decisions. This transparency enables users to rely on AI systems, particularly in critical fields like healthcare, finance, and criminal justice, where decisions can significantly impact lives.
What role does Explainable AI play in responsible AI?
Explainable AI is a key component of responsible AI, which involves ensuring that AI systems are developed and deployed ethically. XAI helps to identify biases, ensure fairness, and maintain transparency in AI decisions, which are essential for accountability and regulatory compliance.
How does Explainable AI help mitigate AI biases?
By making AI decisions more transparent, XAI allows stakeholders to identify and correct biases in AI models. If an AI model is found to disproportionately favor certain groups or outcomes, explainability helps to trace the root causes and refine the model to ensure fairness.
What is performance drift in AI, and why is it a concern?
Performance drift refers to the degradation in the performance of an AI model over time, often caused by differences between the training data and real-world data (i.e., data drift). This can lead to incorrect predictions and decisions. XAI can help monitor performance and trigger alerts when models deviate from expected behavior.
How does Explainable AI support regulatory compliance?
Regulatory frameworks like the GDPR require that automated decisions be explainable to affected individuals. XAI ensures that organizations can provide clear explanations for decisions made by AI systems, helping them comply with legal and ethical standards.
Why is transparency in AI important for public trust?
Transparency in AI allows the public to understand how AI models operate, what data they use, and how decisions are made. Without transparency, AI systems can be seen as opaque or manipulative, undermining trust. By ensuring transparency, XAI helps increase public confidence in AI technologies.
What is prediction accuracy, and how does it contribute to Explainable AI?
Prediction accuracy is a measure of how well an AI model’s predictions match the true outcomes from training data. High accuracy means the AI system is making reliable decisions. In XAI, prediction accuracy is assessed to ensure that the AI is functioning as expected, which helps establish trust.
What is SHAP (Shapley Additive Explanations), and how is it used in Explainable AI?
SHAP is a method in XAI that assigns a contribution value to each feature (input variable) based on its impact on the model’s output. SHAP values are derived from cooperative game theory, ensuring fair distribution of contributions among features. This helps users understand the relative importance of each feature in driving a model’s decision.
How does DeepLIFT improve traceability in AI?
DeepLIFT (Deep Learning Important Features) improves traceability by comparing the activation levels of neurons in the network against a reference point. This helps to understand how the features of the input data influenced the final decision made by the AI model.
What is a decision understanding dashboard, and how does it help users?
A decision understanding dashboard is a visual tool used in Explainable AI to show how key input features contributed to a model’s decision. For example, it may display the factors that led an AI system to flag a transaction as fraudulent, helping users understand the logic behind the AI’s decision.
Why is human understanding a critical part of Explainable AI?
Human understanding is essential because it ensures that AI systems are not only technically accurate but also interpretable to non-experts. Without decision understanding tools like dashboards or visualizations, users would have no way of knowing why an AI made a certain decision, which undermines trust and accountability.
What is automation bias, and why is it a problem in AI systems?
Automation bias refers to the tendency of humans to over-rely on automated systems, even when those systems make errors. This can lead to critical mistakes in fields like healthcare or finance, where AI recommendations might be accepted without questioning their accuracy.
What is algorithm aversion bias, and how does it affect trust in AI?
Algorithm aversion bias is the tendency of people to distrust automated systems, even when those systems are more accurate than humans. This bias can cause people to reject AI systems, leading to missed opportunities or suboptimal decisions, especially in domains like healthcare where AI can enhance diagnostic accuracy.
How can we achieve the “Goldilocks” level of trust in AI?
The “Goldilocks” level of trust refers to finding the right balance between too much and too little trust in AI systems. This can be achieved by educating users on how AI works, addressing both automation bias (overtrust) and algorithm aversion (distrust), and fostering an environment where AI is seen as a supportive tool rather than an infallible decision-maker.
Why is it important to balance trust in AI, particularly in healthcare?
In healthcare, balancing trust is crucial because overreliance on AI could lead to errors, such as misdiagnoses, while undertrust could result in AI’s potential being underutilized, such as missing out on valuable insights. Understanding the strengths and limitations of AI is vital to ensuring that it is used effectively.
How do biases in data impact AI systems?
AI systems are trained on historical data, and if that data contains biases (e.g., racial, gender, or age biases), the AI model will likely learn and reproduce those biases. This can result in discriminatory outcomes, such as biased hiring practices or unfair loan approvals.
Why is it essential to have human oversight in AI decision-making?
Human oversight ensures that AI decisions are checked and validated, especially in high-stakes domains like criminal justice or healthcare. A “human-in-the-loop” system helps to correct mistakes and ensures that AI recommendations are aligned with human values and ethical standards.
What are some potential risks if AI is not transparent?
Without transparency, AI systems could make biased or harmful decisions that go unnoticed, leading to legal, ethical, and societal consequences. For example, an opaque AI system used for credit scoring might unintentionally discriminate against certain groups, but without transparency, it would be difficult to identify and address the issue.
How does Explainable AI help with regulatory compliance?
Explainable AI helps organizations comply with regulations like GDPR and CCPA, which require that AI systems’ decisions be explainable to users. By providing clear explanations of decisions, XAI ensures that organizations meet the legal requirements of transparency and accountability.
Why is explainability alone not enough to ensure trust in AI?
While explainability helps to understand AI decisions, it does not address underlying issues like bias, fairness, and accountability. Additional mechanisms such as regular audits, bias detection tools, and compliance checks are needed to ensure AI systems are trustworthy and ethical.
What additional frameworks are needed alongside Explainable AI?
AI systems should be subject to accountability frameworks, including independent audits, ethical guidelines, and fairness evaluations. These frameworks help ensure that AI systems are not only transparent but also ethically sound and legally compliant.
What role do independent audits play in AI trust?
Independent audits provide an external review of AI systems, ensuring they meet ethical and regulatory standards. Auditors can assess whether AI systems are operating fairly, without bias, and in line with societal values, helping to mitigate risks and improve accountability.
