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Изворен канал @pythonotes · Post #183 · 23 ное.

Python + bash Если вам часто требуется запускать shell команды из Python-кода, какой способ вы используете? Самый низкоуровневый это функция os.system(), либо os.popen(). Рекомендованный способ это subprocess.call(). Но это всё еще достаточно неудобно. Советую обратить своё внимание на очень крутую библиотеку sh. Что она умеет? 🔸 удобный синтаксис вызова команд как функций # os import os os.system("tar cvf demo.tar ~/") # subprocess import subprocess subprocess.call(['tar', 'cvf', 'demo.tar', '~/']) # sh import sh sh.tar('cvf', 'demo.tar', "~/") 🔸 простое создание функции-алиаса для длинной команды fn = sh.lsof.bake('-i', '-P', '-n') output = sh.grep(fn(), 'LISTEN') в этом примере также задействован пайпинг 🔸 удобный вызов команд от sudo with sh.contrib.sudo: print(ls("/root")) Такой запрос спросит пароль. Чтобы это работало нужно соответствующим способом настроить юзера. А вот вариант с вводом пароля через код. password = "secret" sudo = sh.sudo.bake("-S", _in=password+"\n") print(sudo.ls("/root")) Это не все фишки. Больше интересных примеров смотрите в документации. Специально для Windows💀 юзеров #libs#linux

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AI & Law

@ai_and_law · Post #295 · 26.04.2024 г., 07:04

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Lost in Translation: AI Explanations Biased Toward Western Cultures? A new study reveals a potential blind spot in AI development: cultural bias in explanations provided by AI systems. As AI plays an increasingly prominent role in decision-making (hiring, healthcare), explainable AI is crucial for user trust and understanding. Explainable AI systems aim to make complex AI models easier to understand by generating explanations for their outputs. The study analyzed over 200 explainable AI user studies, finding a significant bias towards explaining AI decisions in ways preferred by Western populations: Western cultures tend to favor internalist explanations, focusing on the AI's "thinking" or beliefs. Conversely, collectivist cultures might prefer externalist explanations, referencing rules or social norms influencing the AI's output. This bias could lead to: ✅ Reduced trust in AI systems from non-Western users who receive explanations that don't resonate with their cultural background. ✅ Exclusion of valuable populations from the benefits of explainable AI. 94% of studies reviewed showed no awareness of potential cultural variations in explanation preferences. 48% of studies didn't report the cultural background of participants. Studies sampling non-Western populations were scarce (8.4%). Even studies reporting cultural background often generalized findings to broader populations without considering cultural differences. As AI impacts people worldwide, AI systems need to cater to diverse cultural understandings of explanation. #AI#ExplainableAI#Culture#Bias

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