Model explainability refers to the ability to understand and interpret the predictions made by a machine learning model.
It aims to provide insights into how a model arrives at its conclusions
Explainability is important for building trust in AI systems, especially in critical domains like healthcare and finance.
It helps in identifying biases, errors, and limitations in the model's predictions.
Interpretable models can aid in compliance with legal and ethical regulations
Techniques for model explainability can vary depending on the model type and complexity.
Feature importance analysis is a common method used for explainability.
Rule-based explanations, such as decision trees or rule lists, can provide a human-readable representation of the model's decision-making process.
Model explainability inahusu uwezo wa kuelewa na kutafsiri utabiri uliofanywa na mfano wa kujifunza mashine. "Kama mfano, ""kuelezea"" ni muhimu kwa kujenga uaminifu katika mifumo ya AI, haswa katika maeneo muhimu kama huduma za afya na fedha." Inasaidia katika kutambua upendeleo, makosa, na mapungufu katika utabiri wa mfano. Mifano inayoweza kutafsiriwa inaweza kusaidia katika kufuata kanuni za kisheria na za maadili Mbinu za kuelezea mfano zinaweza kutofautiana kulingana na aina ya mfano na utata. Feature umuhimu uchambuzi ni njia ya kawaida kutumika kwa ajili ya explainability. Maelezo ya msingi wa sheria, kama vile miti ya uamuzi au orodha ya sheria, inaweza kutoa uwakilishi unaosomeka kwa binadamu wa mchakato wa kufanya maamuzi ya mfano.