BIAS 2022 – 6-й Международный авиасалон в Бахрейне состоится 09-11 ноября 2022 г., Бахрейн, Манама.
Что такое биас
BIAS 2022 – 6-й Международный авиасалон в Бахрейне состоится 09-11 ноября 2022 г., Бахрейн, Манама. Find out what is the full meaning of BIAS on. Примеры употребления. Биас — это любимый участник из музыкальной группы, коллектива (чаще всего K-pop). Программная система БИАС предназначена для сбора, хранения и предоставления web-доступа к информации, представляющей собой.
Who is the Least Biased News Source? Simplifying the News Bias Chart
Смещение(bias) — это явление, которое искажает результат алгоритма в пользу или против изначального замысла. Их успех — это результат их усилий, трудолюбия и непрерывного стремления к совершенству. Что такое «биас»? Connecting decision makers to a dynamic network of information, people and ideas, Bloomberg quickly and accurately delivers business and financial information, news and insight around the world. это систематическое искажение или предубеждение, которое может влиять на принятие решений или оценку ситуации. Ну это может быть: Биас, Антон — немецкий политик, социал-демократ Биас, Фанни — артистка балета, солистка Парижской Оперы с 1807 по 1825 год. Общая лексика: тенденциозная подача новостей, тенденциозное освещение новостей.
Is the BBC News Biased…?
The understanding of bias in artificial intelligence (AI) involves recognising various definitions within the AI context. Learn how undertaking a business impact analysis might help your organization overcome the effects of an unexpected interruption to critical business systems. это источник равномерного напряжения, подаваемого на решетку с целью того, чтобы она отталкивала электроды, то есть она должна быть более отрицательная, чем катод.
Bias Reporting FAQ
Learn how undertaking a business impact analysis might help your organization overcome the effects of an unexpected interruption to critical business systems. Так что же такое MAD, Bias и MAPE? Bias (англ. – смещение) демонстрирует на сколько и в какую сторону прогноз продаж отклоняется от фактической потребности. Bias News. WASHINGTON (AP) — White House orders Cabinet heads to notify when they can't perform duties as it reviews policies after Austin's illness. The concept of bias is the lack of internal validity or incorrect assessment of the association between an exposure and an effect in the target population in which the statistic estimated has an expectation that does not equal the true value. Biased news articles, whether driven by political agendas, sensationalism, or other motives, can shape public opinion and influence perceptions. Despite a few issues, Media Bias/Fact Check does often correct those errors within a reasonable amount of time, which is commendable.
Selcaday, лайтстики, биасы. Что это такое? Рассказываем в материале RTVI
Recency bias can lead investors to put too much emphasis on recent events, potentially leading to short-term decisions that may negatively affect their long-term financial plans. Ну это может быть: Биас, Антон — немецкий политик, социал-демократ Биас, Фанни — артистка балета, солистка Парижской Оперы с 1807 по 1825 год. Лирическое отступление: p-hacking и publication bias.
Examples Of Biased News Articles
The nastiness makes a bigger impact on your brain. Cacioppo, Ph. The bias is so automatic that Cacioppo can detect it at the earliest stage of cortical information processing.
In his studies, Cacioppo showed volunteers pictures known to amuse positive feelings such as a Ferrari or a pizza , negative feelings like a mutilated face or dead cat or neutral feelings a plate, a hair dryer. Meanwhile, he recorded event-related brain potentials, or electrical activity of the cortex that reflects the magnitude of information processing taking place. The brain, Cacioppo says, reacts more strongly to stimuli it deems negative.
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. Shilling is illegal in some places, but legal in others.
The brain, Cacioppo says, reacts more strongly to stimuli it deems negative. Thus, our attitudes are more influenced by downbeat news.
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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. Addressing Data Distribution Shift in Model Deployment for Reliable Performance In model deployment, data distribution shift poses a significant challenge, as it reflects discrepancies between the training and real-world data. Models trained on one distribution may experience declining performance when deployed in environments with different data distributions. Covariate shift, the most common type of data distribution shift, occurs when changes in input distribution occur due to shifting independent variables, while the output distribution remains stable.
This can result from factors such as changes in hardware, imaging protocols, postprocessing software, or patient demographics. Continuous monitoring is essential to detect and address covariate shift, ensuring model performance remains reliable in real-world scenarios. Mitigating Social Bias in AI Models for Equitable Healthcare Applications Social bias can permeate throughout the development of AI models, leading to biassed decision-making and potentially unequal impacts on patients. If not addressed during model development, statistical bias can persist and influence future iterations, perpetuating biassed decision-making processes.
These, in response to world events, have continued a trajectory of leftist or rightist leanings in various news platforms. The 1960s and 1970s changed reporting and politics in huge ways. Political bias was rife, with scathing editorials and reporters who made no secret of their involvement with protests and social movements.
New World Media With the dawn of television, new media created a monopolistic hold on public attention. This had a two-fold effect of catapulting reporters to movie star status and further polarizing average citizens. 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.
View image in fullscreen Mark Thompson. Another journalist in a different bureau said that they too saw pushback.
By the time these reports go through Jerusalem and make it to TV or the homepage, critical changes — from the introduction of imprecise language to an ignorance of crucial stories — ensure that nearly every report, no matter how damning, relieves Israel of wrongdoing. Others speculate that they are being kept away by senior editors. Thompson then said he wanted viewers to understand what Hamas is, what it stands for and what it was trying to achieve with the attack.
Some of those listening thought that a laudable journalistic goal. But they said that in time it became clear he had more specific expectations for how journalists should cover the group. In late October, as the Palestinian death toll rose sharply from Israeli bombing with more than 2,700 children killed according to the Gaza health ministry, and as Israel prepared for its ground invasion, a set of guidelines landed in CNN staff inboxes.
Italics in the original. CNN staff members said the memo solidified a framework for stories in which the Hamas massacre was used to implicitly justify Israeli actions, and that other context or history was often unwelcome or marginalised. CNN staff said that edict was laid down by Thompson at an earlier editorial meeting.
That position was reiterated in another instruction on 23 October that reports must not show Hamas recordings of the release of two Israeli hostages, Nurit Cooper and Yocheved Lifshitz. CNN staffers said there is nothing inherently wrong with the requirement given the huge sensitivity of covering Israel and Palestine, and the aggressive nature of Israeli authorities and well-organised pro-Israel groups in seeking to influence coverage. But some feel that a measure that was originally intended to maintain standards has become a tool of self-censorship to avoid controversy.
Девочки ждут, что их лайкнут и ответят им», — отметила Баскакова. Поклонница k-pop Елена рассказала, что фанаты ее любимого коллектива BTS устраивают такой флешмоб в особенные дни. Например, в день рождения группы, фанклуба или из-за выхода новой песни, альбома. По ее словам, способов поддерживать группу очень много. Один из самых популярных — покупка мерча. Она выполнена в дизайне каждой конкретной группы.
Фанаты на концертах держат их и показывают свою принадлежность к фанклубу», — объяснила аналитик. Участники фанклубов также помогают раскручивать новые треки и альбомы группы.
The Bad News Bias
Величина этого напряжения зависит от ваших новых ламп и от схемы усилителя. Таким образом, настройка биаса означает, что ваш усилитель работает в оптимальном режиме, что касается как и ламп, так и самой схемы усилителя. Первый описан в самом начале статьи — это фиксированный биас. Фиксированный биас, подразумевает одно и то же отрицательное напряжение, подаваемое на решетку управляющую сетку лампы. Если же вы видите регулятор напряжения в виде маленького потенциометра, это тоже фиксированный биас, потому что вы настраиваете с его помощью какую-то одну определенную величину напряжения. Однако большинство компаний применяет в схеме своих усилителей технические решения, позволяющие использовать самые разные лампы с различными параметрами. Еще один способ настройки — это катодный биас. Его принцип заключается не в постоянном напряжении, подаваемом на решетку.
Вместо этого между катодом и землёй помещается резистор с большим сопротивлением. Это позволяет стабилизировать напряжение в лампе. Сама схема довольно сложная, поэтому описывать мы ее не будем. Но если вам интересно, можете поискать в сети статьи про «Cathode bias». Фиксированный биас, как правило, используется в мощных усилителях, а катодный — в маломощных. Автоматическое смещение обычно получается в результате протекания тока через резистор, включенный между катодом лампы и общим проводником схемы т. Настройка тока смещения необходима для правильной работы усилителя с теми параметрами, которые задал для него производитель.
Именно его правильная работа и даст вам тот самый звук, ради которого вы амп и покупали. Вдобавок ко всему, правильный режим работы ламп продлевает им жизнь. Лампы Существует 2 режима неправильной работы ламп — горячий недостаточное напряжение смещения, лампа пропускает больше электронов, чем нужно и быстро перегревается и холодный слишком сильное напряжение смещения, всё наоборот. В горячем режиме сигнал начинает перегружаться раньше, чем обычно, мощность усилителя падает, звук менее объёмный, лампа быстро перегревается и изнашивается. Побочный эффект горячего режима — усилитель звучит громче, кажется что он лучше пробивает, но при этом теряет в объёме. Надо понимать, что это может быть едва заметно. В холодном режиме усилитель звучит стерильно, звук быстро затихает, и усилитель попросту не реализует весь свой проектный звуковой потенциал.
Особенно это заметно на малой громкости — звук тонкий, зудящий, вялый и безжизненный. Этот режим также снижает срок службы ламп, но не так радикально как горячий. Многие известные гитаристы прошлого сознательно разгоняли свои ампы до пределов, лампы в загнанном режиме работали по 6-7 часов и умирали — но благодаря этому мы слышим звуки их гитар, которые стали легендой. Увы, не всем такая роскошь в экспериментах не по карману. Вслед за умершими лампами вполне может слететь и еще N-ное количество элементов схемы.
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.
Отлично, а теперь расскажем, кто же это такой. Слово «bias» в английском языке означает «любимчик». Поэтому, когда у тебя спрашивают о нем, то хотят узнать, какой участник группы стал для тебя фаворитом. Интересно, что корейцы чаще всего используют свой вариант, который имеет то же значение, но читается как «чуэ» тут сложнее, так что лучше послушать произношение в переводчике! Однако для прямого обращения к человеку его практически никогда не используют. Выражение употребляют в разговоре с кем-либо, когда хотят упомянуть младшенького, о котором идет речь. И совсем не обязательно называть донсэном настоящего брата или сестру — это обращение можно использовать и для друзей. Сюда можно отнести и другие популярные слова, которые делят собеседников по возрасту: «онни» когда девушка младше обращается к девушке постраше , «нуна» когда парень младше обращается к девушке постраше , а также «хён» когда парень младше обращается к парню постарше и «оппа» когда девушка младше обращается к парню постарше.
Bias is an inclination to present or hold a partial perspective at the expense of possibly equally valid alternatives. This includes newspapers, television, radio, and more recently the internet. Those which provide news and information are known as the news media. The member… … Wikipedia News media — Electronic News Gathering trucks and photojournalists gathered outside the Prudential Financial headquarters in Newark, United States in August 2004 following the announcement of evidence of a terrorist threat to it and to buildings in New York… … Wikipedia News broadcasting — Newsbreak redirects here.
How investors’ behavioural biases affect investment decisions
CNN did report on the rolling back of the claims as Israeli officials backtracked, but one staffer said that by then the damage had been done, describing the coverage as a failure of journalism. A CNN spokesperson said the network accurately reported what was being said at the time. Some CNN staff raised similar issues with reporting on Hamas tunnels in Gaza and claims they led to a sprawling command centre under al-Shifa hospital. Insiders say some journalists have pushed back against the restrictions. One pointed to Jomana Karadsheh, a London-based correspondent with a long history of reporting from the Middle East. That has helped keep the full impact of the war on Palestinians off of CNN and other channels while ensuring that there is a continued focus on the Israeli perspective. A CNN spokesperson rejected allegations of bias. Ward acknowledged the challenges in the Washington Post last week.
But others say that the Ukraine war may be part of the problem because editorial standards grew lax as the network and many of its journalists identified clearly with one side — Ukraine — particularly at the beginning of the conflict. One CNN staffer said that Ukraine coverage set a dangerous precedent that has come back to haunt the network because the Israeli-Palestinian conflict is far more divisive and views are much more deeply entrenched. Only this time, the stakes are higher and the consequences much more severe. Another CNN employee said the double standards are glaring. Some say the problem is rooted in years of pressure from the Israeli government and allied groups in the US combined with a fear of losing advertising. The Palestinians have nothing.
Однако, когда более независимое и объективное исследование проводит анализ данных, оказывается, что положительные реакции были незначительны, и большинство участников не проявляли интерес к продукту. В этом случае, информационный биас искажает интерпретацию данных, ведя к ошибочному выводу о привлекательности продукта. Как избежать информационного биаса в нейромаркетинге Избежать информационного биаса в нейромаркетинге важно для создания объективных и надежных исследований и маркетинговых стратегий. Вот несколько методов и рекомендаций: Двойное слепое исследование: используйте метод двойного слепого исследования. В этом случае ни исследователи, ни участники не знают, какие данные исследуются, чтобы исключить предвзятость. Прозрачность данных: важно делиться полными данными и методами исследования, чтобы обеспечить прозрачность. Это позволяет другим исследователям проверить результаты и убедиться в их объективности. Обучение исследователей: исследователи нейромаркетинга должны быть обучены, как распознавать и избегать информационного биаса. Проведение тренингов по этике и объективности может снизить влияние предпочтений.
The only goal for platforms like these is to better inform readers. Ground News is the first platform like this to use not one but three algorithms. With these tools, we can confidently say that you will better understand bias in the news you read. Articles from different news outlets covering the same news event are merged into a single story so subscribers can get all the perspectives in one view. Ground News does not independently rate news organizations on their political bias. All bias data is referenced from third-party independent organizations dedicated to monitoring and rating news publishers along the political spectrum based on published articles and news coverage. For more information and original analysis please visit mediabiasfactcheck. It is present in every news story you read, watch or hear. It does mean that you need a better and more informed way to take in the news each day. Ground News can offer that.
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. Conduct subpopulation analysis that involves calculating model metrics for specific groups in the dataset. This can help determine if the model performance is identical across subpopulations. Monitor the model over time against biases.