. XAI is crucial because it can guarantee that AI systems make impartial, fair decisions. Here’s an example of how XAI could be used:
Consider a scenario in which a financial organization uses machine learning algorithms to forecast the likelihood that a loan applicant will default on their loan. In the case of conventional, the bank would not have knowledge of the algorithm’s decision-making process and might not be able to explain it to the loan applicant.
Using XAI, however, the algorithm could explain its choice, enabling the bank to confirm that it was based on reasonable considerations rather than inaccurate or discriminating information. The algorithm might specify, for instance, that it calculated a risk score based on the applicant’s credit score, income and employment history. This level of transparency and explainability can help increase trust in AI systems, improve accountability and ultimately lead to better decision-making.
AI can be an agent for our improvement. The most interesting paper I've seen on this is a study of professional GO player performance before vs. after the introduction of Leela, the open-source version of DeepMind''s AlphaGo. Player performance improved.
Federated learning allows multiple devices to collaborate on a single model without sharing their data with a central server, ensuring privacy concerns are addressed. GANs generate realistic data by pitting two neural networks against each other.