Articles about "Explanations"
Table of Contents
Explanations help people understand why something happened, especially in the context of artificial intelligence (AI). When AI makes decisions or predictions, it is important for users to know how the AI reached those conclusions.
Types of Explanations
AI can provide different types of explanations. Some focus on what could have happened if things were different. These are called counterfactual explanations. For example, if a loan was denied, a counterfactual explanation might say, "If you had a higher income, you would have been approved."
Important Features of Explanations
Good explanations in AI should have certain qualities:
- Minimality: They should be as simple as possible.
- Actionability: They should guide users on what can be done next.
- Stability: They should be consistent across similar cases.
- Diversity: They should offer different viewpoints or scenarios.
- Plausibility: They should make sense to the user.
- Discriminative Power: They should clearly show why one decision was made over another.
Combining Explanations
To create better explanations, multiple simpler explanation methods can be combined. This way, even if one method has weaknesses, others can help fill the gaps. This combined approach can work with many types of AI models and can handle different kinds of data.