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Изворен канал @pythonotes · Post #418 · 9 мар.

Оператор pipe позволяет писать более компактный код, реализуя логику объединения данных (Union). Важно помнить, что его поведение зависит от контекста. Побитовые операции (логическое OR) result = 5 | 3 # 5 (0101) | 3 (0011) = 7 (0111) Самое главное - не путать с оператором or, это другое! Объединение множеств set_a = {1, 2, 3} set_b = {3, 4, 5} set_c = set_a | set_b # {1, 2, 3, 4, 5} set_c |= {5, 6} # {1, 2, 3, 4, 5, 6} Слияние словарей dict_1 = {"a": 1, "b": 2} dict_2 = {"b": 3, "c": 4} merged = dict_1 | dict_2 # {'a': 1, 'b': 3, 'c': 4} merged |= {"d": 5} # {'a': 1, 'b': 3, 'c': 4, 'd': 5} Аннотации типов, заменяет Union def process_data(value: int | str) -> None: print(value) Допустимо использовать в isinstance или issubclass isinstance(3, int | float) # True Паттерн-матчинг status_code = 404 match status_code: case 200 | 201 | 204: print("OK") case 400 | 404 | 500: print("ERROR") Для использования в своих классах требуется переопределить метод __or__ Так же нашел библиотеку pipe которая добавляет еще много возможностей. Рекомендую ознакомиться ;) #basic

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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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