Biased news articles, whether driven by political agendas, sensationalism, or other motives, can shape public opinion and influence perceptions. Примеры употребления. Биас — это любимый участник из музыкальной группы, коллектива (чаще всего K-pop). Tags: Pew Research Center Media Bias Political Bias Bias in News. Примеры употребления. Биас — это любимый участник из музыкальной группы, коллектива (чаще всего K-pop).
UiT The Arctic University of Norway
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. Main article: Bias statistics Statistical bias is a systematic tendency in the process of data collection, which results in lopsided, misleading results. This can occur in any of a number of ways, in the way the sample is selected, or in the way data are collected.
Main article: Forecast bias A forecast bias is when there are consistent differences between results and the forecasts of those quantities; that is: forecasts may have an overall tendency to be too high or too low. It is usually controlled using a double-blind system , and was an important reason for the development of double-blind experiments. Reporting bias and social desirability bias edit Main articles: Reporting bias and Social desirability bias In epidemiology and empirical research , reporting bias is defined as "selective revealing or suppression of information" of undesirable behavior by subjects [88] or researchers.
This can propagate, as each instance reinforces the status quo, and later experimenters justify their own reporting bias by observing that previous experimenters reported different results. Social desirability bias is a bias within social science research where survey respondents can tend to answer questions in a manner that will be viewed positively by others. This bias interferes with the interpretation of average tendencies as well as individual differences.
The inclination represents a major issue with self-report questionnaires; of special concern are self-reports of abilities, personalities , sexual behavior , and drug use.
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.
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. AI models may inadvertently make predictions on sensitive attributes such as patient race, age, sex, and ethnicity, even if these attributes were thought to be de-identified. While explainable AI techniques offer some insight into the features informing model predictions, specific features contributing to the prediction of sensitive attributes may remain unidentified. This lack of transparency can amplify clinical bias present in the data used for training, potentially leading to unintended consequences. For instance, models may infer demographic information and health factors from medical images to predict healthcare costs or treatment outcomes.
While these models may have positive applications, they could also be exploited to deny care to high-risk individuals or perpetuate existing disparities in healthcare access and treatment. Addressing biassed model development requires thorough research into the context of the clinical problem being addressed. This includes examining disparities in access to imaging modalities, standards of patient referral, and follow-up adherence. Understanding and mitigating these biases are essential to ensure equitable and effective AI applications in healthcare. Privilege bias may arise, where unequal access to AI solutions leads to certain demographics being excluded from benefiting equally. This can result in biassed training datasets for future model iterations, limiting their applicability to underrepresented populations. Automation bias exacerbates existing social bias by favouring automated recommendations over contrary evidence, leading to errors in interpretation and decision-making.
In clinical settings, this bias may manifest as omission errors, where incorrect AI results are overlooked, or commission errors, where incorrect results are accepted despite contrary evidence. Radiology, with its high-volume and time-constrained environment, is particularly vulnerable to automation bias. Inexperienced practitioners and resource-constrained health systems are at higher risk of overreliance on AI solutions, potentially leading to erroneous clinical decisions based on biased model outputs. The acceptance of incorrect AI results contributes to a feedback loop, perpetuating errors in future model iterations. Certain patient populations, especially those in resource-constrained settings, are disproportionately affected by automation bias due to reliance on AI solutions in the absence of expert review.
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. AI models may inadvertently make predictions on sensitive attributes such as patient race, age, sex, and ethnicity, even if these attributes were thought to be de-identified. While explainable AI techniques offer some insight into the features informing model predictions, specific features contributing to the prediction of sensitive attributes may remain unidentified. This lack of transparency can amplify clinical bias present in the data used for training, potentially leading to unintended consequences. For instance, models may infer demographic information and health factors from medical images to predict healthcare costs or treatment outcomes. While these models may have positive applications, they could also be exploited to deny care to high-risk individuals or perpetuate existing disparities in healthcare access and treatment.
Addressing biassed model development requires thorough research into the context of the clinical problem being addressed. This includes examining disparities in access to imaging modalities, standards of patient referral, and follow-up adherence. Understanding and mitigating these biases are essential to ensure equitable and effective AI applications in healthcare.
Bad News Bias
Смещение(bias) — это явление, которое искажает результат алгоритма в пользу или против изначального замысла. Biased news articles, whether driven by political agendas, sensationalism, or other motives, can shape public opinion and influence perceptions. Did the Associated Press, the venerable American agency that is one of the world’s biggest news providers, collaborate with the Nazis during World War II? К итогам минувшего Международного авиасалона в Бахрейне (BIAS) в 2018 можно отнести: Более 5 млрд. долл. Программная система БИАС предназначена для сбора, хранения и предоставления web-доступа к информации, представляющей собой.
How investors’ behavioural biases affect investment decisions
| Ground News - Media Bias | A bias incident targets a person based upon any of the protected categories identified in The College of New Jersey Policy Prohibiting Discrimination in the Workplace/Educational Environment. |
| Что такое BIAS и зачем он ламповому усилителю? | 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. |
| Биас — что это значит | An analysis of 102 news sources measuring their bias, reliability, traffic, and other factors. |
Что такое биасы
Таким образом регулируется число электронов, которые проникают сквозь решетку. Напряжение смещения настраивается для того, чтобы лампы работали в оптимальном режиме. 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.
Ну и что теперь? Есть два самых популярных типа настройки биаса. Первый мы уже описали в самом начале статьи - это фиксированный биас.
Когда я употребляю слово "фиксированный", это означает, что на решетку в лампе подаётся одно и то же отрицательное напряжение всегда. 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. Некоторые производители, например Mesa Boogie, упростили задачу для пользователей, убрав этот потенциометр из схемы.
Таким образом мы ничего регулировать не можем, а можем только покупать лампы у Mesa Boogie. Они отбирают их по своим параметрам. Усилители работают в оптимальном режиме и все счастливы.
Однако большинство компаний этого не делает, позволяя использовать самые разные лампы с различными параметрами. Это не означает, что лампы Mesa Boogie - самые лучшие, они просто подобраны под их усилители. Другой 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". Одно небольшое замечание: фиксированный биас как правило используется в мощных усилителях, а катодный - в маломощных.
Bias, звук и лампы Настройка биаса важна не только для того, чтобы ваш усилитель работал правильно, она также явно сказывается на его звучании и на сроке службы ваших ламп. Настроив оптимальное напряжение смещения, вы получаете максимально долго работающие лампы, а также максимально круто звучащий усилитель. Разве могут быть какие-то сомнения в необходимости такой настройки?
Осталось еще несколько спорных моментов, которые стоит прояснить.
For Wikipedia s current events page, see Portal:Current events. For other uses, see News disambiguation.
Journalism News … Wikipedia Bias — This article is about different ways the term bias is used. For other uses, see Bias disambiguation.
Bias through selection and omission An editor can express bias by choosing whether or not to use a specific news story. Within a story, some details can be ignored, others can be included to give readers or viewers a different opinion about the events reported.
Only by comparing news reports from a wide variety of sources can this type of bias be observed. Bias through placement Where a story is placed influences what a person thinks about its importance. Stories on the front page of the newspaper are thought to be more important than stories buried in the back. Many television and radio newscasts run stories that draw ratings first and leave the less appealing for later.
Coverage of the Republican National Convention begins on page 26.
Иногда в БИАСе можно наткнуться на ваши социальные сети, но для их поиска есть другой сервис, ведь вы можете сидеть с фейковой страницы. Если вы проживаете в многоквартирном доме, то в базе можно будет найти стационарные телефоны соседей если они у них есть и звонить им, требуя передать вам информацию о задолженности. Цель коллектора — не уведомить вас о долге, о котором вы и так знаете. Его цель — оповестить ваше окружение о нем, чтобы вы испытали максимальный дискомфорт от данной ситуации и быстрее вернули деньги.
Биас — что это значит
Demand-side incentives are often not related to distortion. Competition can still affect the welfare and treatment of consumers, but it is not very effective in changing bias compared to the supply side. Mass media skew news driven by viewership and profits, leading to the media bias. And readers are also easily attracted to lurid news, although they may be biased and not true enough. Also, the information in biased reports also influences the decision-making of the readers. Their findings suggest that the New York Times produce biased weather forecast results depending on the region in which the Giants play. When they played at home in Manhattan, reports of sunny days predicting increased. From this study, Raymond and Taylor found that bias pattern in New York Times weather forecasts was consistent with demand-driven bias. The rise of social media has undermined the economic model of traditional media. The number of people who rely upon social media has increased and the number who rely on print news has decreased.
Messages are prioritized and rewarded based on their virality and shareability rather than their truth, [47] promoting radical, shocking click-bait content. Some of the main concerns with social media lie with the spread of deliberately false information and the spread of hate and extremism. Social scientist experts explain the growth of misinformation and hate as a result of the increase in echo chambers. Because social media is tailored to your interests and your selected friends, it is an easy outlet for political echo chambers. GCF Global encourages online users to avoid echo chambers by interacting with different people and perspectives along with avoiding the temptation of confirmation bias.
Therefore, maintaining a diverse AI team can help you mitigate unwanted AI biases. A data-centric approach to AI development can also help minimize bias in AI systems.
Tools to reduce bias AI Fairness 360 IBM released an open-source library to detect and mitigate biases in unsupervised learning algorithms that currently has 34 contributors as of September 2020 on Github. The library is called AI Fairness 360 and it enables AI programmers to test biases in models and datasets with a comprehensive set of metrics. What are some examples of AI bias? Eliminating selected accents in call centers Bay Area startup Sanas developed an AI-based accent translation system to make call center workers from around the world sound more familiar to American customers. However, by 2015, Amazon realized that their new AI recruiting system was not rating candidates fairly and it showed bias against women. Amazon had used historical data from the last 10-years to train their AI model. Racial bias in healthcare risk algorithm A health care risk-prediction algorithm that is used on more than 200 million U.
The algorithm was designed to predict which patients would likely need extra medical care, however, then it is revealed that the algorithm was producing faulty results that favor white patients over black patients. This was a bad interpretation of historical data because income and race are highly correlated metrics and making assumptions based on only one variable of correlated metrics led the algorithm to provide inaccurate results. Bias in Facebook ads There are numerous examples of human bias and we see that happening in tech platforms. Since data on tech platforms is later used to train machine learning models, these biases lead to biased machine learning models. In 2019, Facebook was allowing its advertisers to intentionally target adverts according to gender, race, and religion. For instance, women were prioritized in job adverts for roles in nursing or secretarial work, whereas job ads for janitors and taxi drivers had been mostly shown to men, in particular men from minority backgrounds.
Так он без труда находят вашу прошлую работу и, соответственно, ваших бывших коллег, не говоря уже о родственниках и даже знакомых, с которыми вы "сто лет" не общаетесь. Иногда в БИАСе можно наткнуться на ваши социальные сети, но для их поиска есть другой сервис, ведь вы можете сидеть с фейковой страницы. Если вы проживаете в многоквартирном доме, то в базе можно будет найти стационарные телефоны соседей если они у них есть и звонить им, требуя передать вам информацию о задолженности. Цель коллектора — не уведомить вас о долге, о котором вы и так знаете.
Давай я попробую угадать твоего биаса в BTS? Может так я смогу помочь тебе с выбором биаса, а ты, взамен, поможешь мне. Мой биас вся семёрка — неделька!!!
А ведь это, чёрт побеги, правильно!!! Ким Намджун Думаю, что в твоих фаворитах ходит именно этот милый парень. Он умен, красив и просто прекрасен.
Лидер группы и горячий мужчина. Идеальный муж, любовник и просто человек. Понимаю твой выбор.
Ты любитель золота. Этот юнец твой Ад и Рай, твоя сладость и боль. Он тот, кто мотивирует тебя день ото дня, а также тот, кто учит тебя все время идти только вперед.
Он человек дела и тебе это нравится в нем, а еще, твоя тайная мечта — его бедра. Хороший выбор. Его энергетика всегда служит тебе крутой зарядкой на целый день.
Его гибкое тело, позитивный настрой и точные цели в жизни — вдохновляют. Ты знаешь, что этот человек всегда будет способен вытащить тебя из негативных мыслей, именно поэтому он твой биас. Упс…Что-то пошло не так!
Твой биас — вся семерка! Это невероятно, но иначе и быть не может. Как же возможно выбрать кого-то одного?
Выдохни, это нормально. Биас-неделька тоже биас :З. Это же сам Мин Юнги!
Парень, который сочетает в себе холодок снежных гор и тепло текущей лавы. Самый ленивый, но в то же время самый трудолюбивый парень на свете. Его читка всегда на высоте, а слова бьют в самую душу.
Этот парень стоит твоего внимания. Самый ленивый, но в тоже время самый трудолюбивый парень на свете. Его читка всегда на высоте, а слова всегда бьют в самую душу.
Оченьь жаль что есть такие арми которые не долюбливают участников такой великой группы. Даже не знаю, кто мой биас.. Они все классные.
Стоп, сначала же был Чонгук.. Я всех обожаю Поэтому, они все мои биасы!!!!!! Я была в шоке, когда угадали.
RBC Defeats Ex-Branch Manager’s Racial Bias, Retaliation Suit
В К-поп культуре биасами называют артистов, которые больше всего нравятся какому-то поклоннику, причем у одного человека могут быть несколько биасов. Ну это может быть: Биас, Антон — немецкий политик, социал-демократ Биас, Фанни — артистка балета, солистка Парижской Оперы с 1807 по 1825 год. Find out what is the full meaning of BIAS on. as a treatment for depression: A meta-analysis adjusting for publication bias. In response, the Milli Majlis of Azerbaijan issued a statement denouncing the European Parliament resolution as biased and lacking objectivity. Кроме того, есть такое понятие, как биас врекер (от англ. bias wrecker — громила биаса), это участник группы, который отбивает биаса у фанатов благодаря своей обаятельности или другим качествам.
RBC Defeats Ex-Branch Manager’s Racial Bias, Retaliation Suit
| AI bias (предвзятость искусственного интеллекта) | BIAS designs, implements, and maintains Oracle-based IT services for some of the world's leading organizations. |
| Что такое технология Bias? | Слово "Биас" было заимствовано из английского языка "Bias", и является аббревиатурой от выражения "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». |
Что такое биасы
К итогам минувшего Международного авиасалона в Бахрейне (BIAS) в 2018 можно отнести: Более 5 млрд. долл. Conservatives also complain that the BBC is too progressive and biased against consverative view points. 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. Их успех — это результат их усилий, трудолюбия и непрерывного стремления к совершенству. Что такое «биас»?
Is the BBC News Biased…?
В контексте принятия решений биас может влиять на нашу способность анализировать информацию объективно и приводить к неправильным или несбалансированным результатам. Понимание существования биаса и его влияния может помочь нам развить критическое мышление и принимать более обоснованные решения. Однако необходимо отметить, что биас не всегда негативен.
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. Main article: Bias statistics Statistical bias is a systematic tendency in the process of data collection, which results in lopsided, misleading results.
It can be most entrenched around beliefs and ideas that we are strongly attached to or that provoke a strong emotional response. Actively seek out contrary information.
If you experience or witness a bias incident, please file a report via our online form. Filing a report allows the College to promptly respond to incidents, assess issues with campus climate, and recommend appropriate educational initiatives in response. TCNJ remains deeply committed to addressing bias on campus. The College recognizes that each incident is unique and must be addressed based on the facts and context presented in the specific complaint. All incidents are closely reviewed individually to determine the appropriate response to the report. Examples of some of the categories are race, ethnicity, national origin, sex, gender, gender identify, sexual orientation, marital status, or veteran status. A bias incident occurs where someone believes that they are subject to discrimination, harassment, abuse, bullying, stereotyping, marginalization, or any other form of mistreatment because they identify or are associated with a particular group. Examples of a bias incident are the following: A staff member tells a racist joke. A faculty member makes a sexist comment. A job candidate is not hired because of their age. A student is mocked for having a disability. A student is marginalized for being transgender. A wall is defaced with anti-Semitic graffiti. An international student is verbally harassed because of where she is born. A gay student discovers anti-gay messages on his dorm room door. How do I file a bias report?
CNN staff say network’s pro-Israel slant amounts to ‘journalistic malpractice’
Владелец сайта предпочёл скрыть описание страницы. 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. Despite a few issues, Media Bias/Fact Check does often correct those errors within a reasonable amount of time, which is commendable. Investors possessing this bias run the risk of buying into the market at highs. Tags: Pew Research Center Media Bias Political Bias Bias in News.
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