source stringlengths 36 80 | text stringlengths 51 500 |
|---|---|
https://en.wikipedia.org/wiki/Google_Sheets#5 | nd Safari web browsers.[15] Users can access all spreadsheets, among other files, collectively through the Google Drive website. In June 2014, Google rolled out a dedicated website homepage for Sheets that contain only files created with Sheets.[16] In 2014, Google launched a dedicated mobile app for Sheets on the Andr... |
https://en.wikipedia.org/wiki/Google_Sheets#6 | " interface. While users can read spreadsheets through the mobile websites, users trying to edit will be redirected towards the mobile app to eliminate editing on the mobile web.[20]
Features
[edit]Editing
[edit]Collaboration and revision history
[edit]Google Sheets serves as a collaborative tool for cooperative editin... |
https://en.wikipedia.org/wiki/Google_Sheets#7 | racter-by-character changes as other collaborators make edits. Changes are automatically saved to Google's servers and a revision history is automatically kept so past edits may be viewed and reverted to.[22] An editor's current position is represented with an editor-specific color/cursor, so if another editor happens ... |
https://en.wikipedia.org/wiki/Google_Sheets#8 | cuss edits. The revision history allows users to see the additions made to a document, with each author distinguished by color. Only adjacent revisions can be compared and users cannot control how frequently revisions are saved. Files can be exported to a user's local computer in a variety of formats such as PDF and Of... |
https://en.wikipedia.org/wiki/Google_Sheets#9 | ve suite in September 2016, "Explore" enables additional functionality through machine learning.[25][26][27] In Google Sheets, Explore enables users to ask questions, such as "How many units were sold on Black Friday?" and Explore will return the answer, without requiring formula knowledge from the user. In June 2017, ... |
https://en.wikipedia.org/wiki/Google_Sheets#10 | t in December to feature machine learning capable of automatically creating pivot tables.[30][31] In October 2016, Google announced the addition of "Action items" to Sheets. If a user assigns a task within a Sheet, the service will intelligently assign that action to the designated user. Google states this will make it... |
https://en.wikipedia.org/wiki/Google_Sheets#11 | ontaining tasks assigned to them will be highlighted with a badge.[32] In March 2014, Google introduced add-ons; new tools from third-party developers that add more features for Google Sheets.[33]
Offline editing
[edit]To view and edit spreadsheets offline on a computer, users need to be using the Chromium-based web br... |
https://en.wikipedia.org/wiki/Google_Sheets#12 | heets and other Drive suite files on the Google Drive website.[34] The Android and iOS apps natively support offline editing.[35][36]
Files
[edit]Supported file formats and limits
[edit]Files in the following formats can be viewed and converted to the Sheets format: .xls (if newer than Microsoft Office 95), .xlsx, .xls... |
https://en.wikipedia.org/wiki/Google_Sheets#13 | space
[edit]The Sheets app and the rest of the Google Docs Editors suite are free to use for individuals, but Sheets is also available as part of the business-centered Google Workspace (formerly G Suite) service by Google, which is a monthly subscription that enables additional business-focused functionality.[40]
Integ... |
https://en.wikipedia.org/wiki/Google_Sheets#14 | ion with Wikipedia.[42]
Other functionality
[edit]A simple find and replace tool is available. The service includes a web clipboard tool that allows users to copy and paste content between Google Sheets and Google Docs, Google Slides, and Google Drawings. The web clipboard can also be used for copying and pasting conte... |
https://en.wikipedia.org/wiki/Google_Sheets#15 | the Google Chrome web browser called Office editing for Docs, Sheets and Slides that enables users to view and edit Microsoft Excel documents on Google Chrome, via the Google Sheets app. The extension can be used for opening Excel files stored on the computer using Chrome, as well as for opening files encountered on th... |
https://en.wikipedia.org/wiki/Google_Sheets#16 | ChromeOS by default.[44] As of June 2019, this extension is no longer required since the functionality exists natively.[45]
Google Cloud Connect was a plug-in for Microsoft Office 2003, 2007, and 2010 that could automatically store and synchronize any Excel document to Google Sheets (before the introduction of Drive). ... |
https://en.wikipedia.org/wiki/Google_Sheets#17 | ted offline and synchronized later when online. Google Cloud Connect maintained previous Microsoft Excel document versions and allowed multiple users to collaborate by working on the same document at the same time.[46][47] However, Google Cloud Connect has been discontinued as of April 30, 2013, as, according to Google... |
https://en.wikipedia.org/wiki/Google_Sheets#18 | Google sheets 'fixes' this bug by increasing all dates before March 1, 1900, so entering "0" and formatting it as a date returns December 30, 1899. On the other hand. Excel interprets "0" as meaning December 31, 1899, which is formatted to read January 0, 1900.
Launched in December 2022, Simple ML is the Google's add-o... |
https://en.wikipedia.org/wiki/Google_Sheets#19 | lides". Google Play. Retrieved May 19, 2025.
- "Google Sheets". Google Play. Retrieved May 19, 2025.
- ^
- "Google Docs 1.25.192.03". APKMirror. May 14, 2025. Retrieved May 19, 2025.
- "Google Slides 1.25.192.01". APKMirror. May 7, 2025. Retrieved May 19, 2025.
- "Google Sheets 1.25.192.01". APKMirror. May 7, 2025. Ret... |
https://en.wikipedia.org/wiki/Google_Sheets#20 | - "Google Sheets". App Store. Retrieved May 19, 2025.
- ^ Hill, Ian (June 18, 2013). "18 New Languages for Drive, Docs, Sheets, and Slides". Google Drive Blog. Retrieved October 29, 2016.
- ^ "Office editing makes it easier to work with Office files in Docs, Sheets, and Slides". G Suite Updates Blog. Retrieved August 1... |
https://en.wikipedia.org/wiki/Google_Sheets#21 | y. Retrieved October 15, 2021.
- ^ Dawson, Christopher (October 30, 2010). "Google's 40 acquisitions in 2010: What about integration?". ZDNet. CBS Interactive. Retrieved June 1, 2017.
- ^ Rochelle, Jonathan (June 6, 2006). "It's nice to share". Official Google Blog. Retrieved October 29, 2016.
- ^ "Google Announces lim... |
https://en.wikipedia.org/wiki/Google_Sheets#22 | adsheets". October 11, 2006. Retrieved October 29, 2016.
- ^ Jackson, Rob (March 5, 2010). "Google Buys DocVerse For Office Collaboration: Chrome, Android & Wave Implications?". Phandroid. Retrieved October 20, 2016.
- ^ Belomestnykh, Olga (April 15, 2010). "A rebuilt, more real-time Google documents". Google Drive Blo... |
https://en.wikipedia.org/wiki/Google_Sheets#23 | Google Blog. Retrieved October 30, 2016.
- ^ Sawers, Paul (October 23, 2012). "Google Drive apps renamed "Docs, Sheets and Slides", now available in the Chrome Web Store". The Next Web. Retrieved October 30, 2016.
- ^ "System requirements and browsers". Docs editors Help. Retrieved December 16, 2016.
- ^ "Dedicated des... |
https://en.wikipedia.org/wiki/Google_Sheets#24 | (April 30, 2014). "New mobile apps for Docs, Sheets and Slides—work offline and on the go". Official Google Blog. Retrieved December 16, 2016.
- ^ Tabone, Ryan (June 25, 2014). "Work with any file, on any device, any time with new Docs, Sheets, and Slides". Google Drive Blog. Retrieved December 16, 2016.
- ^ "New Googl... |
https://en.wikipedia.org/wiki/Google_Sheets#25 | oogle Docs, Sheets, and Slides viewers on the mobile web". G Suite Updates. July 27, 2015. Retrieved December 16, 2016.
- ^ Meyer, David (August 20, 2009). "Google Apps Script gets green light". CNet. Archived from the original on August 10, 2012. Retrieved March 26, 2011.
- ^ "See the history of changes made to a file... |
https://en.wikipedia.org/wiki/Google_Sheets#26 | ieved August 12, 2019.
- ^ "Google sheet for organizational access".
- ^ Ranjan, Ritcha (September 29, 2016). "Explore in Docs, Sheets and Slides makes work a breeze — and makes you look good, too". Google Docs Blog. Retrieved December 16, 2016.
- ^ Novet, Jordan (September 29, 2016). "Google updates Calendar, Drive, D... |
https://en.wikipedia.org/wiki/Google_Sheets#27 | r 30, 2016). "Google wants to better challenge Microsoft Office with these new features". TechRadar. Future plc. Retrieved December 16, 2016.
- ^ Lardinois, Frederic (June 1, 2017). "Google Sheets now uses machine learning to help you visualize your data". TechCrunch. AOL. Retrieved June 1, 2017.
- ^ Carman, Ashley (Ju... |
https://en.wikipedia.org/wiki/Google_Sheets#28 | ed June 1, 2017.
- ^ Miller, Ron (December 6, 2017). "Latest Google Sheets release helps automate pivot table creation". TechCrunch. Oath Inc. Retrieved December 14, 2017.
- ^ Gagliordi, Natalie (December 6, 2017). "Google brings new AI, machine learning features to Sheets". ZDNet. CBS Interactive. Retrieved December 1... |
https://en.wikipedia.org/wiki/Google_Sheets#29 | rieved December 14, 2016.
- ^ Gupta, Saurabh (March 11, 2014). "Bring a little something extra to Docs and Sheets with add-ons". Google Drive Blog. Retrieved October 30, 2016.
- ^ "Work on Google files offline". Drive Help. Retrieved January 14, 2017.
- ^ "Work on Google files offline". Drive Help. Retrieved January 14... |
https://en.wikipedia.org/wiki/Google_Sheets#30 | p. Retrieved October 30, 2016.
- ^ "Files you can store in Google Drive". Drive Help. Retrieved November 1, 2019.
- ^ "Insert or delete images or videos". Docs editors Help. Retrieved October 22, 2016.
- ^ "G Suite - Choose a Plan". Retrieved October 30, 2016.
- ^ "Google Spreadsheets | Charts". Google Developers. Retr... |
https://en.wikipedia.org/wiki/Google_Sheets#31 | ges". Retrieved October 30, 2016.
- ^ "Office Editing for Docs, Sheets & Slides". Chrome Web Store. Retrieved October 30, 2016.
- ^ "Remove the Office compatibility app". G Suite Admin Help. Retrieved August 12, 2019.
- ^ Sinha, Shan (February 24, 2011). "Google Cloud Connect for Microsoft Office available to all". Goo... |
https://en.wikipedia.org/wiki/Google_Sheets#32 | ffice". Mashable. Retrieved October 30, 2016.
- ^ "Migrate from Google Cloud Connect to Google Drive". Apps Documentation and Support. Archived from the original on March 17, 2013. Retrieved October 30, 2016.
- ^ "Google Unveils a New Machine Learning Add-on for Google Sheets, Called Simple ML for Sheets, Which Allows ... |
https://en.wikipedia.org/wiki/Google_Sheets#33 | eadsheet. Sourcetable Inc., 2024. Retrieved 2024-11-14. |
https://en.wikipedia.org/wiki/Google_Sites#0 | Google Sites
Google Sites is a structured wiki and web page creation tool included as part of the free, web-based Google Docs Editors suite offered by Google. The service includes Google Docs, Google Sheets, Google Slides, Google Drawings, Google Forms, and Google Keep. Google Sites is only available on the web.
Histor... |
https://en.wikipedia.org/wiki/Google_Sites#1 | re.[citation needed] It was targeted mainly at small-sized and medium-sized businesses. The company was founded by Joe Kraus and Graham Spencer, co-founders of Excite.
In February 2006, JotSpot was named part of Business 2.0, "Next Net 25",[1] and in May 2006, it was honored as one of InfoWorld's "15 Start-ups to Watch... |
https://en.wikipedia.org/wiki/Google_Sites#2 | oogle Page Creator (also known as "Google Pages") to Google Sites servers in 2007. On February 28, 2008, Google Sites was unveiled using the JotSpot technology.[4] The service was free, but users needed a domain name, which Google offered for $10. However, as of May 21, 2008, Google Sites became available for free, sep... |
https://en.wikipedia.org/wiki/Google_Sites#3 | ites platform, named the New Google Sites,[6][7] along with transition schedule from Classic Google Sites.[8] The new Google Sites does not use JotSpot technology.
In August 2020, the new Google Sites became the default option for website creation, while in November 2021, all websites made with classic Google Sites wer... |
https://en.wikipedia.org/wiki/Google_Sites#4 | n Turkey after it was alleged that one of the pages contained an insult of Turkey's founder, Mustafa Kemal Atatürk. In 2012, the European Court of Human Rights (ECHR) ruled the blockage a breach of Article 10 of the European Convention on Human Rights (Yildirim v Turkey, 2012).[10] The blockage was lifted in 2014.[11]
... |
https://en.wikipedia.org/wiki/Google_Sites#5 | 8.
- ^ Gruman, Galen (May 15, 2006). "JotSpot delivers enterprise wikis and mashups". InfoWorld. Retrieved February 29, 2008.
- ^ Spot on – Google Blog, November 1, 2006
- ^ Auchard, Eric (February 28, 2008). "Google offers team Web site publishing service". Yahoo! News. Archived from the original on March 2, 2008. Ret... |
https://en.wikipedia.org/wiki/Google_Sites#6 | redesigned Google Sites goes live". TechCrunch. Retrieved January 11, 2018.
- ^ "Google Apps for Work – Email, Collaboration Tools And More". apps.google.com. Archived from the original on September 28, 2016. Retrieved June 20, 2016.
- ^ "An update on the classic Google Sites deprecation timeline". G Suite Updates Blog... |
https://en.wikipedia.org/wiki/Google_Sites#7 | 023.
- ^ 1 Crown Office Row (January 16, 2013). "Turkish block on Google site breached Article 10 rights, rules Strasbourg". UK Human Rights Blog. Retrieved June 15, 2013.
{{cite web}}
: CS1 maint: numeric names: authors list (link) - ^ "Google Transparency Report – Turkey, Google Sites". Retrieved October 4, 2013. |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#0 | Fairness (machine learning)
Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions made by such models after a learning process may be considered unfair if they were based on variables considered sensitive (e.g., gender,... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#1 | controversial. In general, fairness and bias are considered relevant when the decision process impacts people's lives.
Since machine-made decisions may be skewed by a range of factors, they might be considered unfair with respect to certain groups or individuals. An example could be the way social media sites deliver p... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#2 | 16 there has been a sharp increase in research into the topic.[1] This increase could be partly attributed to an influential report by ProPublica that claimed that the COMPAS software, widely used in US courts to predict recidivism, was racially biased.[2] One topic of research and discussion is the definition of fairn... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#3 | ult to judge machine learning models.[3] Other research topics include the origins of bias, the types of bias, and methods to reduce bias.[4]
In recent years tech companies have made tools and manuals on how to detect and reduce bias in machine learning. IBM has tools for Python and R with several algorithms to reduce ... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#4 | ing.[7][8] Facebook have reported their use of a tool, Fairness Flow, to detect bias in their AI.[9] However, critics have argued that the company's efforts are insufficient, reporting little use of the tool by employees as it cannot be used for all their programs and even when it can, use of the tool is optional.[10]
... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#5 | predates by several decades the rather recent debate on fairness in machine learning.[11] In fact, a vivid discussion of this topic by the scientific community flourished during the mid-1960s and 1970s, mostly as a result of the American civil rights movement and, in particular, of the passage of the U.S. Civil Rights ... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#6 | fairness left little room for clarity on when one notion of fairness may be preferable to another.
Language Bias
[edit]Language bias refers a type of statistical sampling bias tied to the language of a query that leads to "a systematic deviation in sampling information that prevents it from accurately representing the ... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#7 | e language models, as they are predominately trained on English-language data, often present the Anglo-American views as truth, while systematically downplaying non-English perspectives as irrelevant, wrong, or noise. When queried with political ideologies like "What is liberalism?", ChatGPT, as it was trained on Engli... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#8 | equally valid aspects like "opposes state intervention in personal and economic life" from the dominant Vietnamese perspective and "limitation of government power" from the prevalent Chinese perspective are absent. Similarly, other political perspectives embedded in Japanese, Korean, French, and German corpora are abse... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#9 | es.[12]
Gender Bias
[edit]Gender bias refers to the tendency of these models to produce outputs that are unfairly prejudiced towards one gender over another. This bias typically arises from the data on which these models are trained. For example, large language models often assign roles and characteristics based on tra... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#10 | ical bias
[edit]Political bias refers to the tendency of algorithms to systematically favor certain political viewpoints, ideologies, or outcomes over others. Language models may also exhibit political biases. Since the training data includes a wide range of political opinions and coverage, the models might generate re... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#11 | 4]
Controversies
[edit]The use of algorithmic decision making in the legal system has been a notable area of use under scrutiny. In 2014, then U.S. Attorney General Eric Holder raised concerns that "risk assessment" methods may be putting undue focus on factors not under a defendant's control, such as their education l... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#12 | likely to be incorrectly labelled as higher risk than white defendants, while making the opposite mistake with white defendants.[2] The creator of COMPAS, Northepointe Inc., disputed the report, claiming their tool is fair and ProPublica made statistical errors,[16] which was subsequently refuted again by ProPublica.[1... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#13 | ound to ignore or mislabel the facial expressions of non-white subjects.[18] In 2015, Google apologized after Google Photos mistakenly labeled a black couple as gorillas. Similarly, Flickr auto-tag feature was found to have labeled some black people as "apes" and "animals".[19] A 2016 international beauty contest judge... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#14 | y of three commercial gender classification algorithms in 2018 found that all three algorithms were generally most accurate when classifying light-skinned males and worst when classifying dark-skinned females.[21] In 2020, an image cropping tool from Twitter was shown to prefer lighter skinned faces.[22] In 2022, the c... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#15 | ch as gender or race.[23][24]
Other areas where machine learning algorithms are in use that have been shown to be biased include job and loan applications. Amazon has used software to review job applications that was sexist, for example by penalizing resumes that included the word "women".[25] In 2019, Apple's algorith... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#16 | s that shared their finances.[26] Mortgage-approval algorithms in use in the U.S. were shown to be more likely to reject non-white applicants by a report by The Markup in 2021.[27]
Limitations
[edit]Recent works underline the presence of several limitations to the current landscape of fairness in machine learning, part... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#17 | 8][29][30] For instance, the mathematical and quantitative approach to formalize fairness, and the related "de-biasing" approaches, may rely onto too simplistic and easily overlooked assumptions, such as the categorization of individuals into pre-defined social groups. Other delicate aspects are, e.g., the interaction ... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#18 | tion.
Finally, while machine learning models can be designed to adhere to fairness criteria, the ultimate decisions made by human operators may still be influenced by their own biases. This phenomenon occurs when decision-makers accept AI recommendations only when they align with their preexisting prejudices, thereby u... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#19 | a function to predict a discrete characteristic , the target variable, from known characteristics . We model as a discrete random variable which encodes some characteristics contained or implicitly encoded in that we consider as sensitive characteristics (gender, ethnicity, sexual orientation, etc.). We finally denote ... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#20 | ts predictions are not influenced by some of these sensitive variables.[32]
Independence
[edit]We say the random variables satisfy independence if the sensitive characteristics are statistically independent of the prediction , and we write We can also express this notion with the following formula: This means that the ... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#21 | stics .
Yet another equivalent expression for independence can be given using the concept of mutual information between random variables, defined as In this formula, is the entropy of the random variable . Then satisfy independence if .
A possible relaxation of the independence definition include introducing a positive... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#22 | les satisfy separation if the sensitive characteristics are statistically independent of the prediction given the target value , and we write We can also express this notion with the following formula: This means that all the dependence of the decision on the sensitive attribute must be justified by the actual dependen... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#23 | e and the false positive rate are equal (and therefore the false negative rate and the true negative rate are equal) for every value of the sensitive characteristics:
A possible relaxation of the given definitions is to allow the value for the difference between rates to be a positive number lower than a given slack , ... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#24 | at a given level of the probability score) between the predicted cumulative percent negative and predicted cumulative percent positive.
The greater this separation coefficient is at a given score value, the more effective the model is at differentiating between the set of positives and negatives at a particular probabi... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#25 | ds on the modeling approach. For example, if modeling procedure is parametric or semi-parametric, the two-sample K-S test is often used. If the model is derived by heuristic or iterative search methods, the measure of model performance is usually divergence. A third option is the coefficient of separation...The coeffic... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#26 | eflects the separation pattern of a model."
Sufficiency
[edit]We say the random variables satisfy sufficiency if the sensitive characteristics are statistically independent of the target value given the prediction , and we write We can also express this notion with the following formula: This means that the probability... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#27 | e predicted to belong to the same group.
Relationships between definitions
[edit]Finally, we sum up some of the main results that relate the three definitions given above:
- Assuming is binary, if and are not statistically independent, and and are not statistically independent either, then independence and separation c... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#28 | and are not statistically independent, then separation and sufficiency cannot both hold except for rhetorical cases.
It is referred to as total fairness when independence, separation, and sufficiency are all satisfied simultaneously.[34] However, total fairness is not possible to achieve except in specific rhetorical c... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#29 | fairness rely on different metrics, so we will start by defining them. When working with a binary classifier, both the predicted and the actual classes can take two values: positive and negative. Now let us start explaining the different possible relations between predicted and actual outcome:[36]
- True positive (TP):... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#30 | predicted outcome and the actual outcome are assigned to the negative class.
- False positive (FP): A case predicted to befall into a positive class assigned in the actual outcome is to the negative one.
- False negative (FN): A case predicted to be in the negative class with an actual outcome is in the positive one.
T... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#31 | n this matrix, columns and rows represent instances of the predicted and the actual cases, respectively.
By using these relations, we can define multiple metrics which can be later used to measure the fairness of an algorithm:
- Positive predicted value (PPV): the fraction of positive cases which were correctly predict... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#32 | ve prediction. It is given by the following formula:
- False discovery rate (FDR): the fraction of positive predictions which were actually negative out of all the positive predictions. It represents the probability of an erroneous positive prediction, and it is given by the following formula:
- Negative predicted valu... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#33 | ability of a correct negative prediction, and it is given by the following formula:
- False omission rate (FOR): the fraction of negative predictions which were actually positive out of all the negative predictions. It represents the probability of an erroneous negative prediction, and it is given by the following form... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#34 | s usually referred to as sensitivity or recall, and it represents the probability of the positive subjects to be classified correctly as such. It is given by the formula:
- False negative rate (FNR): the fraction of positive cases which were incorrectly predicted to be negative out of all the positive cases. It represe... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#35 | negative rate (TNR): the fraction of negative cases which were correctly predicted out of all the negative cases. It represents the probability of the negative subjects to be classified correctly as such, and it is given by the formula:
- False positive rate (FPR): the fraction of negative cases which were incorrectly ... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#36 | ncorrectly as positive ones, and it is given by the formula:
The following criteria can be understood as measures of the three general definitions given at the beginning of this section, namely Independence, Separation and Sufficiency. In the table[32] to the right, we can see the relationships between them.
To define ... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#37 | ed outcome, on predicted and actual outcomes, and definitions based on predicted probabilities and the actual outcome.
We will be working with a binary classifier and the following notation: refers to the score given by the classifier, which is the probability of a certain subject to be in the positive or the negative ... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#38 | positive when is above a certain threshold. represents the actual outcome, that is, the real classification of the individual and, finally, denotes the sensitive attributes of the subjects.
Definitions based on predicted outcome
[edit]The definitions in this section focus on a predicted outcome for various distribution... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#39 | l parity, acceptance rate parity and benchmarking. A classifier satisfies this definition if the subjects in the protected and unprotected groups have equal probability of being assigned to the positive predicted class. This is, if the following formula is satisfied:
- Conditional statistical parity. Basically consists... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#40 | sed on predicted and actual outcomes
[edit]These definitions not only considers the predicted outcome but also compare it to the actual outcome .
- Predictive parity, also referred to as outcome test. A classifier satisfies this definition if the subjects in the protected and unprotected groups have equal PPV. This is,... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#41 | DR, satisfying the formula:
- False positive error rate balance, also referred to as predictive equality. A classifier satisfies this definition if the subjects in the protected and unprotected groups have equal FPR. This is, if the following formula is satisfied:
- Mathematically, if a classifier has equal FPR for bot... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#42 | rtunity. A classifier satisfies this definition if the subjects in the protected and unprotected groups have equal FNR. This is, if the following formula is satisfied:
- Mathematically, if a classifier has equal FNR for both groups, it will also have equal TPR, satisfying the formula:
- Equalized odds, also referred to... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#43 | the protected and unprotected groups have equal TPR and equal FPR, satisfying the formula:
- Conditional use accuracy equality. A classifier satisfies this definition if the subjects in the protected and unprotected groups have equal PPV and equal NPV, satisfying the formula:
- Overall accuracy equality. A classifier s... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#44 | bility of a subject from one class to be assigned to it. This is, if it satisfies the following formula:
- Treatment equality. A classifier satisfies this definition if the subjects in the protected and unprotected groups have an equal ratio of FN and FP, satisfying the formula:
Definitions based on predicted probabili... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#45 | ess, also known as calibration or matching conditional frequencies. A classifier satisfies this definition if individuals with the same predicted probability score have the same probability of being classified in the positive class when they belong to either the protected or the unprotected group:
- Well-calibration is... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#46 | icted probability score they must have the same probability of being classified in the positive class, and this probability must be equal to :
- Balance for positive class. A classifier satisfies this definition if the subjects constituting the positive class from both protected and unprotected groups have equal averag... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#47 | ith positive actual outcome is the same, satisfying the formula:
- Balance for negative class. A classifier satisfies this definition if the subjects constituting the negative class from both protected and unprotected groups have equal average predicted probability score . This means that the expected value of probabil... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#48 | on fairness
[edit]With respect to confusion matrices, independence, separation, and sufficiency require the respective quantities listed below to not have statistically significant difference across sensitive characteristics.[35]
- Independence: (TP + FP) / (TP + FP + FN + TN) (i.e., ).
- Separation: TN / (TN + FP) and... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#49 | edictive value ).
The notion of equal confusion fairness[37] requires the confusion matrix of a given decision system to have the same distribution when computed stratified over all sensitive characteristics.
Social welfare function
[edit]Some scholars have proposed defining algorithmic fairness in terms of a social we... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#50 | accuracy in terms of their benefits to the people affected by the algorithm. It also allows the designer to trade off efficiency and equity in a principled way.[38] Sendhil Mullainathan has stated that algorithm designers should use social welfare functions to recognize absolute gains for disadvantaged groups. For exam... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#51 | ion rates for Blacks, Hispanics, and racial minorities overall, even while keeping the crime rate constant.[39]
Individual fairness criteria
[edit]An important distinction among fairness definitions is the one between group and individual notions.[40][41][36][42] Roughly speaking, while group fairness criteria compare ... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#52 | compare individuals. In words, individual fairness follow the principle that "similar individuals should receive similar treatments".
There is a very intuitive approach to fairness, which usually goes under the name of fairness through unawareness (FTU), or blindness, that prescribes not to explicitly employ sensitive ... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#53 | only for the value of their sensitive attributes would receive the same outcome.
However, in general, FTU is subject to several drawbacks, the main being that it does not take into account possible correlations between sensitive attributes and non-sensitive attributes employed in the decision-making process. For exampl... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#54 | r gender (i.e. a variable highly correlated with gender) and effectively using gender information while at the same time being compliant to the FTU prescription.
The problem of what variables correlated to sensitive ones are fairly employable by a model in the decision-making process is a crucial one, and is relevant f... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#55 | s allow for correlation, but only as far as the labeled target variable "justify" them.
The most general concept of individual fairness was introduced in the pioneer work by Cynthia Dwork and collaborators in 2012[43] and can be thought of as a mathematical translation of the principle that the decision map taking feat... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#56 | ion on the model map. They call this approach fairness through awareness (FTA), precisely as counterpoint to FTU, since they underline the importance of choosing the appropriate target-related distance metric to assess which individuals are similar in specific situations. Again, this problem is very related to the poin... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#57 | ess measures the frequency with which two nearly identical users or applications who differ only in a set of characteristics with respect to which resource allocation must be fair receive identical treatment.[44] [dubious – discuss]
An entire branch of the academic research on fairness metrics is devoted to leverage ca... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#58 | distribution of data may hide different causal relationships among the variables at play, possibly with different interpretations of whether the outcome are affected by some form of bias or not.[32]
Kusner et al.[45] propose to employ counterfactuals, and define a decision-making process counterfactually fair if, for a... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#59 | ical formulation reads:
that is: taken a random individual with sensitive attribute and other features and the same individual if she had , they should have same chance of being accepted. The symbol represents the counterfactual random variable in the scenario where the sensitive attribute is fixed to . The conditionin... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#60 | observation.
Machine learning models are often trained upon data where the outcome depended on the decision made at that time.[46] For example, if a machine learning model has to determine whether an inmate will recidivate and will determine whether the inmate should be released early, the outcome could be dependent on... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#61 | random variable, denotes the outcome given that the decision was taken, and is a sensitive feature.
Plecko and Bareinboim[48] propose a unified framework to deal with causal analysis of fairness. They suggest the use of a Standard Fairness Model, consisting of a causal graph with 4 types of variables:
- sensitive attri... |
https://en.wikipedia.org/wiki/Fairness_%28machine_learning%29#62 | e outcome,
- variables possibly sharing a common cause with (), representing possible spurious (i.e., non causal) effects of the sensitive attributes on the outcome.
Within this framework, Plecko and Bareinboim[48] are therefore able to classify the possible effects that sensitive attributes may have on the outcome. Mo... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.