News that carries a bias usually comes with positive news from a state news organization or policies that are financed by the state leadership. Проверьте онлайн для BIAS, значения BIAS и другие аббревиатура, акроним, и синонимы. Что такое биас. Биас, или систематическая ошибка, в контексте принятия решений означает предвзятость или неправильное искажение результатов, вызванное некорректным восприятием, предубеждениями или неправильным моделированием данных.
"Fake News," Lies and Propaganda: How to Sort Fact from Fiction
Происхождение: bias— звучит как "бАес", но среди фанатов к-поп более распространен неправильный вариант произношения — "биас". Лирическое отступление: p-hacking и publication bias. Quam Bene Non Quantum: Bias in a Family of Quantum Random Number. Bias instability measures the amount that a sensor output will drift during operation over time and at a steady temperature.
CNN staff say network’s pro-Israel slant amounts to ‘journalistic malpractice’
Find out what is the full meaning of BIAS on. Смещение(bias) — это явление, которое искажает результат алгоритма в пользу или против изначального замысла. Американский производитель звукового программного обеспечения компания BIAS Inc объявила о прекращении своей деятельности. Find out what is the full meaning of BIAS on.
Methods & sources
- How investors’ behavioural biases affect investment decisions - Mazars - United Kingdom
- K-pop словарик: 12 выражений, которые поймут только истинные фанаты
- Evaluating News: Biased News
- Our Approach to Media Bias
- Selcaday, лайтстики, биасы. Что это такое? Рассказываем в материале RTVI
Article content
- Блог про HR-аналитику: Bias как тренд HR-аналитики
- Media bias - Wikipedia
- BIAS 2022 – 6-й Международный авиасалон в Бахрейне
- BBC presenter confesses broadcaster ignores complaints of bias
Why is the resolution of the European Parliament called biased?
США подтвержденных заказов и обязательств Объявлены инвестиции в авиационную промышленность Бахрейна в размере 93,4 млн. Формат нового мероприятия не совсем обычен — это комплекс и 40 шале и никаких выставочных павильонов. Участники выставки будут располагаться в шале, оснащенных по последнему слову техники и с соответствующим уровнем сервиса.
There are a wide range of sorts of attribution biases, such as the ultimate attribution error , fundamental attribution error , actor-observer bias , and self-serving bias.
People also tend to interpret ambiguous evidence as supporting their existing position. Biased search, interpretation and memory have been invoked to explain attitude polarization when a disagreement becomes more extreme even though the different parties are exposed to the same evidence , belief perseverance when beliefs persist after the evidence for them is shown to be false , the irrational primacy effect a greater reliance on information encountered early in a series and illusory correlation when people falsely perceive an association between two events or situations. Confirmation biases contribute to overconfidence in personal beliefs and can maintain or strengthen beliefs in the face of contrary evidence.
Poor decisions due to these biases have been found in political and organizational contexts. It is an influence over how people organize, perceive, and communicate about reality. For political purposes, framing often presents facts in such a way that implicates a problem that is in need of a solution.
Members of political parties attempt to frame issues in a way that makes a solution favoring their own political leaning appear as the most appropriate course of action for the situation at hand. Numerous such biases exist, concerning cultural norms for color, location of body parts, mate selection , concepts of justice , linguistic and logical validity, acceptability of evidence , and taboos. Ordinary people may tend to imagine other people as basically the same, not significantly more or less valuable, probably attached emotionally to different groups and different land.
If the observer likes one aspect of something, they will have a positive predisposition toward everything about it. Studies have demonstrated that this bias can affect behavior in the workplace , [61] in interpersonal relationships , [62] playing sports , [63] and in consumer decisions.
The current baseline or status quo is taken as a reference point, and any change from that baseline is perceived as a loss. Status quo bias should be distinguished from a rational preference for the status quo ante, as when the current state of affairs is objectively superior to the available alternatives, or when imperfect information is a significant problem. A large body of evidence, however, shows that status quo bias frequently affects human decision-making. The potential conflict is autonomous of actual improper actions , it can be found and intentionally defused before corruption , or the appearance of corruption, happens. Political campaign contributions in the form of cash are considered criminal acts of bribery in some countries, while in the United States they are legal provided they adhere to election law. Tipping is considered bribery in some societies, but not others.
This can be expressed in evaluation of others, in allocation of resources, and in many other ways. Cronyism is favoritism of long-standing friends, especially by appointing them to positions of authority, regardless of their qualifications. Lobbying is often spoken of with contempt , the implication is that people with inordinate socioeconomic power are corrupting the law in order to serve their own interests. This can lead to all sides in a debate looking to sway the issue by means of lobbyists. Main articles: Industry self-regulation and Regulatory capture Self-regulation is the process whereby an organization monitors its own adherence to legal, ethical, or safety standards, rather than have an outside, independent agency such as a third party entity monitor and enforce those standards. If any organization, such as a corporation or government bureaucracy, is asked to eliminate unethical behavior within their own group, it may be in their interest in the short run to eliminate the appearance of unethical behavior, rather than the behavior itself.
However, they point out dozens of cases where his claims are false. Besides promoting pseudoscience, Biased. News is an extreme right-wing biased source that frequently promotes false or misleading information regarding vaccines, alternative health, and government conspiracies. For more information, read our review on Natural News. Actor who played law enforcement sniper was recorded walking around carrying rifle by the magazine.
Biased.News – Bias and Credibility
| RBC Defeats Ex-Branch Manager’s Racial Bias, Retaliation Suit | English 111 - Research Guides at CUNY Lehman. |
| Bias in AI: What it is, Types, Examples & 6 Ways to Fix it in 2024 | As new global compliance regulations are introduced, Beamery releases its AI Explainability Statement and accompanying third-party AI bias audit results. |
| Биас — Что это значит? Сленг | | Проверьте онлайн для BIAS, значения BIAS и другие аббревиатура, акроним, и синонимы. |
| Media bias - Wikipedia | ГК «БИАС» занимается вопросами обеспечения и контроля температуры и влажности при хранении и транспортировке термозависимой продукции. |
Что такое Вижуал
- Years of pressure
- BIAS 2022 | Российские Беспилотники
- BBC presenter confesses broadcaster ignores complaints of bias — RT UK News
- ООО «БИАС» | Банк России
- Examples Of Biased News Articles
- Результаты аудита Hybe показали, что Мин Хи Чжин действительно планировала захватить власть
Термины и определения, слова и фразы к-поп или сленг к-поперов и дорамщиков
Class imbalance is a common issue, especially in datasets for rare diseases or conditions. Overrepresentation of certain classes, such as positive cases in medical imaging studies, can lead to biassed model performance. Similarly, sampling bias, where certain demographic groups are underrepresented in the training data, can exacerbate disparities. Data labelling introduces its own set of biases. Annotator bias arises from annotators projecting their own experiences and biases onto the labelling task.
This can result in inconsistencies in labelling, even with standard guidelines. Automated labelling processes using natural language processing tools can also introduce bias if not carefully monitored. Label ambiguity, where multiple conflicting labels exist for the same data, further complicates the issue. Additionally, label bias occurs when the available labels do not fully represent the diversity of the data, leading to incomplete or biassed model training.
Care must be taken when using publicly available datasets, as they may contain unknown biases in labelling schemas. Overall, understanding and addressing these various sources of bias is essential for developing fair and reliable AI models for medical imaging. Guarding Against Bias in AI Model Development In model development, preventing data leakage is crucial during data splitting to ensure accurate evaluation and generalisation. Data leakage occurs when information not available at prediction time is included in the training dataset, such as overlapping training and test data.
This can lead to falsely inflated performance during evaluation and poor generalisation to new data. Data duplication and missing data are common causes of leakage, as redundant or global statistics may unintentionally influence model training. Improper feature engineering can also introduce bias by skewing the representation of features in the training dataset. For instance, improper image cropping may lead to over- or underrepresentation of certain features, affecting model predictions.
For example, a mammogram model trained on cropped images of easily identifiable findings may struggle with regions of higher breast density or marginal areas, impacting its performance. Proper feature selection and transformation are essential to enhance model performance and avoid biassed development. Model Evaluation: Choosing Appropriate Metrics and Conducting Subgroup Analysis In model evaluation, selecting appropriate performance metrics is crucial to accurately assess model effectiveness. Metrics such as accuracy may be misleading in the context of class imbalance, making the F1 score a better choice for evaluating performance.
Precision and recall, components of the F1 score, offer insights into positive predictive value and sensitivity, respectively, which are essential for understanding model performance across different classes or conditions. Subgroup analysis is also vital for assessing model performance across demographic or geographic categories. Evaluating models based solely on aggregate performance can mask disparities between subgroups, potentially leading to biassed outcomes in specific populations. Conducting subgroup analysis helps identify and address poor performance in certain groups, ensuring model generalizability and equitable effectiveness across diverse populations.
По вопросам дополнительной информации о составлении и утверждении Отчета необходимо обращаться посредством заполнения электронной формы обращения в разделе Службы поддержки Портала cbias. Информация о консультантах размещена в личных кабинетах учреждений на Портале cbias. Обращаем внимание, что руководитель федерального государственного учреждения несет персональную ответственность за достоверность представленных в Отчете сведений. Загрузить ещё.
Дорамы выпускаются в различных жанрах — романтика, комедия, детективы, ужасы, боевики, исторические и т. Длительность стандартного сезона для дорам — три месяца. Количество серий колеблется от 16 до 20 серий. Мемберы Это участники музыкальной группы от слова member. Кстати, мемберов в группе могут распределять относительно года рождения: это называется годовыми линиями. Например, айдолы 1990 года рождения будут называться 90 line, остальные — по аналогии. Нуна Это «старшая сестренка». Так парни обращаются к девушкам и подругам, которые немного старше них.
Class imbalance is a common issue, especially in datasets for rare diseases or conditions. Overrepresentation of certain classes, such as positive cases in medical imaging studies, can lead to biassed model performance. Similarly, sampling bias, where certain demographic groups are underrepresented in the training data, can exacerbate disparities. Data labelling introduces its own set of biases. Annotator bias arises from annotators projecting their own experiences and biases onto the labelling task. This can result in inconsistencies in labelling, even with standard guidelines. Automated labelling processes using natural language processing tools can also introduce bias if not carefully monitored. Label ambiguity, where multiple conflicting labels exist for the same data, further complicates the issue. Additionally, label bias occurs when the available labels do not fully represent the diversity of the data, leading to incomplete or biassed model training. Care must be taken when using publicly available datasets, as they may contain unknown biases in labelling schemas. Overall, understanding and addressing these various sources of bias is essential for developing fair and reliable AI models for medical imaging. Guarding Against Bias in AI Model Development In model development, preventing data leakage is crucial during data splitting to ensure accurate evaluation and generalisation. Data leakage occurs when information not available at prediction time is included in the training dataset, such as overlapping training and test data. This can lead to falsely inflated performance during evaluation and poor generalisation to new data. Data duplication and missing data are common causes of leakage, as redundant or global statistics may unintentionally influence model training. Improper feature engineering can also introduce bias by skewing the representation of features in the training dataset. For instance, improper image cropping may lead to over- or underrepresentation of certain features, affecting model predictions. For example, a mammogram model trained on cropped images of easily identifiable findings may struggle with regions of higher breast density or marginal areas, impacting its performance. Proper feature selection and transformation are essential to enhance model performance and avoid biassed development. Model Evaluation: Choosing Appropriate Metrics and Conducting Subgroup Analysis In model evaluation, selecting appropriate performance metrics is crucial to accurately assess model effectiveness. Metrics such as accuracy may be misleading in the context of class imbalance, making the F1 score a better choice for evaluating performance. Precision and recall, components of the F1 score, offer insights into positive predictive value and sensitivity, respectively, which are essential for understanding model performance across different classes or conditions. Subgroup analysis is also vital for assessing model performance across demographic or geographic categories. Evaluating models based solely on aggregate performance can mask disparities between subgroups, potentially leading to biassed outcomes in specific populations. Conducting subgroup analysis helps identify and address poor performance in certain groups, ensuring model generalizability and equitable effectiveness across diverse populations.
Искажение оценки информации в нейромаркетинге: понимание проблемы
Reuters’ fact check section has a Center bias, though there may be some evidence of Lean Left bias, according to a July 2021 Small Group Editorial Review by AllSides editors on the left, cen. это аббревиатура фразы "Being Inspired and Addicted to Someone who doesn't know you", что можно перевести, как «Быть вдохновленным и зависимым от того, кто тебя не знает» А от кого зависимы вы? University of Washington. The understanding of bias in artificial intelligence (AI) involves recognising various definitions within the AI context. «Фанат выбирает фотографию своего биаса (человека из группы, который ему симпатичен — прим.
AI Can ‘Unbias’ Healthcare—But Only If We Work Together To End Data Disparity
Для соблюдения холодовой цепи необходимо наличие как минимум трех составляющих: Современная материальная база, к которой относятся: термоконтейнеры, медицинские холодильники, средства контроля, к которым относятся специальные термометры, термоиндикаторы и терморегистраторы. Чётко разработанный план мероприятий по соблюдению и контролю холодовой цепи со всеми необходимыми документами учета. Самое главное — человеческий фактор. Необходим грамотно подготовленный и ответственный персонал. Все изделия, задействованные в холодовой цепи, должны быть зарегистрированы в Росздравнадзоре в качестве изделий медицинского назначения и соответствующим образом сертифицированы, а термометры для контроля температуры в холодильниках должны быть внесены в реестр средств измерений и проходить периодическую поверку. Что такое инспекционная метка и зачем она нужна? Сколько раз нажмёте — столько меток будет на графике в таблице , привязанных по календарному времени к моменту нажатия.
Это очень удобная функция, например, для разграничения зон ответственности при транспортировке лекарственных средств. В каждом пункте перегрузки и временного хранения могут формироваться такие метки с целью последующего наглядного анализа момента нарушения холодовой цепи, и установления причины кто виноват?
Вполне естественно, что первыми на возможные пагубные последствия AI bias обратили внимание философствующие защитники «Азиломарских принципов искусственного интеллекта» [7]. Среди этих 23 положений есть совершенно здравые с 1 по 18 , но другие с 19 по 23 , принятые под влиянием Илона Маска , Рея Курцвейла и покойного Стивена Хокинга носят, скажем так, общеразговорный характер. Они распространяются в область сверхразума и сингулярности, которыми регулярно и безответственно пугают наивное народонаселение.
Возникают естественные вопросы — откуда взялась AI bias и что с этой предвзятостью делать? Справедливо допустить, что предвзятость ИИ не вызвана какими-то собственными свойствами моделей, а является прямым следствием двух других типов предвзятостей — хорошо известной когнитивной и менее известной алгоритмической. В процессе обучения сети они складываются в цепочку и в итоге возникает третье звено — AI bias. Трехзвенная цепочка предвзятостей: Разработчики, создающие системы глубинного обучения являются обладателями когнитивных предвзятостей. Они с неизбежностью переносят эти предвзятости в разрабатываемые ими системы и создают алгоритмические предвзятости.
В процессе эксплуатации системы демонстрируют AI bias. Начнем с когнитивных. Разработчики систем на принципах глубинного обучения, как и все остальные представители человеческой расы, являются носителями той или иной когнитивной пристрастности cognitive bias. У каждого человека есть свой жизненный путь, накопленный опыт, поэтому он не в состоянии быть носителем абсолютной объективности. Индивидуальная пристрастность является неизбежной чертой любой личности.
Психологи стали изучать когнитивную пристрастность как самостоятельное явление в семидесятых годах ХХ века, в отечественной психологической литературе ее принято называть когнитивным искажением. Некоторые из них выполняют адаптивную функцию, поскольку они способствуют более эффективным действиям или более быстрым решениям. Другие, по-видимому, происходят из отсутствия соответствующих навыков мышления или из-за неуместного применения навыков, бывших адаптивными в других условиях» [8]. Существует также сложившиеся направления как когнитивная психология и когнитивно-бихевиоральная терапия КБТ. На февраль 2019 года выделено порядка 200 типов различных когнитивных искажений.
Пристрастности и предвзятости - это часть человеческой культуры. Любой создаваемый человеком артефакт является носителем тех или иных когнитивных пристрастностей его создателей. Можно привести множество примеров, когда одни и те же действия приобретают в разных этносах собственный характер, показательный пример — пользованием рубанком, в Европе его толкают от себя, а в Японии его тянут на себя. Системы, построенные на принципах глубинного обучения в этом смысле не являются исключением, их разработчики не могут быть свободны от присущих им пристрастностей, поэтому с неизбежностью будут переносить часть своей личности в алгоритмы, порождая, в конечном итоге, AI bias. То есть AI bias не собственное свойство ИИ, о следствие переноса в системы качеств, присущих их авторам.
Существование алгоритмической пристрастности Algorithmic bias нельзя назвать открытием.
Выводы Значение термина «биас» в Корее Сам термин «биас» используется в Корее для описания любимого участника музыкального коллектива. Кроме того, в К-поп есть и другие специальные термины: Макнэ — младший участник группы Визуал — наиболее привлекательный представитель коллектива по мнению продюсеров Кто такой биас в К-поп Биас — это участник группы, который занимает особенное место в сердце конкретного фаната. Также важно понимать, что это может быть не один конкретный участник, а несколько. Однако, как правило, у каждого фаната есть свой основной биас. Что такое биас врекер Биас врекер — участник коллектива, который может занять место биаса в будущем.
This is also in spite of the founder following 16 alt-right accounts on Twitter and being hosted on the alt-right Rebel Media , while other frequent contributors include Toby Young , supporter of eugenics ; and Adam Perkins , supporter of hereditarianism. Quillette included several alt-right figures, KKK members, Proud Boys, and Neo-Nazis in their list of conservatives being oppressed by media. Media Bias Fact Check later updated Quillette on July 19, 2019 and has rated them Questionable based on promotion of racial pseudoscience as well as moving away from right-center to right bias.
BBC presenter confesses broadcaster ignores complaints of bias
Bashir Suleymanli, head of the Institute of Civil Rights, in an interview with the program "Difficult Question" highlighted the longstanding tension between Azerbaijani authorities and human rights advocates. Suleymanli noted that while the government denies any human rights violations or the existence of political prisoners, evidence suggests otherwise. He pointed to ongoing instances of civil society suppression, journalist harassment, and arbitrary arrests as indicative of systemic issues within Azerbaijan. He emphasized that human rights violations are not solely an internal matter but are subject to international dialogue and obligations outlined in international agreements.
Разработка и внедрение IT—решений и сервисов для кредитных организаций, финансовых и страховых компаний Big-data Использование технологий BIG-data, включая технологии сбора, обработки и анализа данных Корпорациям Разработка и внедрение корпоративных информационных систем Разработка инновационного программного обеспечения, автоматизация бизнес процессов, оказание IT- услуг ЕГРЮЛ, ЕГРИП Предоставление сведений из Единого государственного реестра регистрации юридических лиц и ИП, а также дополнительные справки Финансовым организациям Кредитный скоринг и экспертная оценка кредитоспособности заемщика IT - консалтинг Комплексные услуги в области инфраструктуры и информационных систем Службе безопасности Обработка и предоставление данных, хранящихся в публичных источниках по ФЛ, ЮЛ и ИП Помощь с регистрацией как оператора персональных данных в реестре Роскомнадзора В нашем портфеле сервисов есть как оптимальный минимум, так и впечатляющий максимум для оптимизации Ваших бизнес-процессов!
A large body of evidence, however, shows that status quo bias frequently affects human decision-making. The potential conflict is autonomous of actual improper actions , it can be found and intentionally defused before corruption , or the appearance of corruption, happens. Political campaign contributions in the form of cash are considered criminal acts of bribery in some countries, while in the United States they are legal provided they adhere to election law. Tipping is considered bribery in some societies, but not others. This can be expressed in evaluation of others, in allocation of resources, and in many other ways. Cronyism is favoritism of long-standing friends, especially by appointing them to positions of authority, regardless of their qualifications. Lobbying is often spoken of with contempt , the implication is that people with inordinate socioeconomic power are corrupting the law in order to serve their own interests. This can lead to all sides in a debate looking to sway the issue by means of lobbyists. Main articles: Industry self-regulation and Regulatory capture Self-regulation is the process whereby an organization monitors its own adherence to legal, ethical, or safety standards, rather than have an outside, independent agency such as a third party entity monitor and enforce those standards. If any organization, such as a corporation or government bureaucracy, is asked to eliminate unethical behavior within their own group, it may be in their interest in the short run to eliminate the appearance of unethical behavior, rather than the behavior itself. Regulatory capture is a form of political corruption that can occur when a regulatory agency , created to act in the public interest , instead advances the commercial or political concerns of special interest groups that dominate the industry or sector it is charged with regulating. The effectiveness of shilling relies on crowd psychology to encourage other onlookers or audience members to purchase the goods or services or accept the ideas being marketed.
They may not include any verifiable facts or sources. Some stories may include basic verifiable facts, but are written using language that is deliberately inflammatory, leaves out pertinent details or only presents one viewpoint. Misinformation is false or inaccurate information that is mistakenly or inadvertently created or spread; the intent is not to deceive. Claire Wardle of First Draft News has created the helpful visual image below to help us think about the ecosystem of mis- and disinformation. Misinformation and disinformation is produced for a variety of complex reasons: Partisan actors want to influence voters and policy makers for political gain, or to influence public discourse for example, intentionally spreading misinformation about election fraud More clicks means more money. In some cases, stories are designed to provoke an emotional response and placed on certain sites "seeded" in order to entice readers into sharing them widely.
K-pop словарик: 12 выражений, которые поймут только истинные фанаты
As new global compliance regulations are introduced, Beamery releases its AI Explainability Statement and accompanying third-party AI bias audit results. Media bias is the bias or perceived bias of journalists and news producers within the mass media in the selection of events, the stories that are reported, and how they are covered. Bias) (Я слышал, что Биас есть и в Франции).
Is the BBC News Biased…?
Figure 1. Technically, yes. An AI system can be as good as the quality of its input data. If you can clean your training dataset from conscious and unconscious assumptions on race, gender, or other ideological concepts, you are able to build an AI system that makes unbiased data-driven decisions. AI can be as good as data and people are the ones who create data. There are numerous human biases and ongoing identification of new biases is increasing the total number constantly. Therefore, it may not be possible to have a completely unbiased human mind so does AI system.
After all, humans are creating the biased data while humans and human-made algorithms are checking the data to identify and remove biases. What we can do about AI bias is to minimize it by testing data and algorithms and developing AI systems with responsible AI principles in mind. How to fix biases in AI and machine learning algorithms? Firstly, if your data set is complete, you should acknowledge that AI biases can only happen due to the prejudices of humankind and you should focus on removing those prejudices from the data set. However, it is not as easy as it sounds. A naive approach is removing protected classes such as sex or race from data and deleting the labels that make the algorithm biased.
So there are no quick fixes to removing all biases but there are high level recommendations from consultants like McKinsey highlighting the best practices of AI bias minimization: Source: McKinsey Steps to fixing bias in AI systems: Fathom the algorithm and data to assess where the risk of unfairness is high. For instance: Examine the training dataset for whether it is representative and large enough to prevent common biases such as sampling bias.
Now, they not only had parties to align with but also platforms.
The death of four Americans sparked outrage. This became central for the 2016 presidential election; coverage was full of partisan opinion and bias. Blindspot Feed The goal is not to rid the world of all bias but rather to see it for what it is.
Any user, anywhere in the world, can download the Ground News app or plugin and immediately see the news in a brand new way. From over 50,000 sources, we collect daily news stories and deliver them with a color-coded bias rating. There are ways to objectively understand inherent bias in the news.
Bias checkers can accurately rate any news story based on bias. This is done with objective criteria and algorithms. The only goal for platforms like these is to better inform readers.
There is actually very little systematic and representative research on bias in the BBC, the latest proper university research was from between 2007 and 2012 by Cardiff University which showed that conservative views were given more airtime than progressive ones. However this may just be because the government is conservative, and a bog standard news item is to give whatever Tory minister time to talk rubbish, which could alone be enough to skew the difference.
Анастасия КорулинаУченик 201 3 года назад И почему же Вы так считаете? Они вам что-то плохое сделали? Ничего плохого они вам не сделали! Они помогают людям любить жизнь и воспринимать себя таким, каким ты есть на самом деле!