Interpreting the Met hods that Interpret Language Models
AI is increasingly capable of making important decisions, but explaining why an AI model arrives a t a particular decision proves to be less straightforward. Research b y computational linguist Jonathan Kamp shows that different explanato ry methods can provide different explanations for exactly the same AI decision. Moreover, these differences are not random: they often fol low fixed patterns.
Artificial intelligence (AI) is in creasingly being deployed for important applications, such as text ev aluation, medical support, and detecting harmful online content. Ther efore, methods have been developed to attempt to explain the 'reasoni ng' of an AI.
Kamp shows that this explanation is not always re liable. The differences between explanatory methods depend on the cho sen method, the type of AI model, and the task the model performs. Fu rthermore, AI models can sometimes reach the correct outcome based on incorrect clues in a text.
Do Not Trust Blindly
By mapping the systematic patterns and biases in AI explanat ions, it becomes clearer when an explanation is reliable or, converse ly, less reliable. The main conclusion is therefore that we should no t simply trust AI explanations, but evaluate them critically.
T he results are important for developers, companies, and users of AI s ystems who want to make reliable decisions. AI models must not only p erform well but also be able to reliably explain why they make certai n choices.
This is becoming increasingly relevant now that larg e language models such as chatbots are being used more frequently in education, healthcare, and information provision. For example, with a n AI assistant providing medical information, it is possible to check which sources and text fragments influence the decision. This allows users to better assess whether the information is reliable.
Mo re information on the thesis
DESCRIPTION: Interpreting the Methods that Interpret Language Models < h3>The Same AI, Four Different Explanations: Research Reveals Hidden Patterns Behind AI ExplanationsAI is in creasingly capable of making important decisions, but explaining why an AI model arrives at a particular decision proves to be less straig htforward. Research by computational linguist Jonathan Kamp shows tha t different explanatory methods can provide different explanations fo r exactly the same AI decision. Moreover, these differences are not r andom: they often follow fixed patterns.Artificial intellige nce (AI) is increasingly being deployed for important applications, s uch as text evaluation, medical support, and detecting harmful online content. Therefore, methods have been developed to attempt to explai n the 'reasoning' of an AI.Kamp shows that this explanation is not al ways reliable. The differences between explanatory methods depend on the chosen method, the type of AI model, and the task the model perfo rms. Furthermore, AI models can sometimes reach the correct outcome b ased on incorrect clues in a text.Do Not Trust BlindlyBy mapping the systematic patterns and biases in AI explanations, i t becomes clearer when an explanation is reliable or, conversely, les s reliable. The main conclusion is therefore that we should not simpl y trust AI explanations, but evaluate them critically.The results are important for developers, companies, and users of AI systems who wan t to make reliable decisions. AI models must not only perform well bu t also be able to reliably explain why they make certain choices.This is becoming increasingly relevant now that large language models suc h as chatbots are being used more frequently in education, healthcare , and information provision. For example, with an AI assistant provid ing medical information, it is possible to check which sources and te xt fragments influence the decision. This allows users to better asse ss whether the information is reliable.More information on the thesis END:VEVENT END:VCALENDAR