{"success":true,"data":{"query":"Comps Context","limit":10,"count":6,"sources":["wiki_artificial_intelligence.hat","wiki_real_estate.hat","web_1779060046.hat"],"synced":[],"results":[{"source":"wiki_artificial_intelligence.hat","text":"Algorithmic bias and fairness\nMachine learning applications can be biased if they learn from biased data. The developers may not be aware that the bias exists. Discriminatory behavior by some LLMs can be observed in their output. Bias can be introduced by the way training data is selected and by the way a model is deployed. If a biased algorithm is used to make decisions that can seriously harm people (as it can in medicine, finance, recruitment, housing or policing) then the algorithm may cause discrimination. The field of fairness studies how to prevent harms from algorithmic biases.\nOn 28 June 2015, Google Photos's new image labeling feature mistakenly identified Jacky Alcine and a friend as \"gorillas\" because they were black. The system was trained on a dataset that contained very few images of black people, a problem called \"sample size disparity\". Google \"fixed\" this problem by preventing the system from labelling anything as a \"gorilla\". Eight years later, in 2023, Google Photos still could not identify a gorilla, and neither could similar products from Apple, Facebook, Microsoft and Amazon.\nCOMPAS is a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. In 2016, Julia Angwin at ProPublica discovered that COMPAS exhibited racial bias, despite the fact that the program was not told the races of the defendants. Although the error rate for both whites and blacks was calibrated equal at exactly 61%, the errors for each race were different—the system consistently overestimated the chance that a black person would re-offend and would underestimate the chance that a white person would not re-offend. In 2017, several researchers showed that it was mathematically impossible for COMPAS to accommodate all possible measures of fairness when the base rates of re-offense were different for whites and blacks in the data.\nA program can make biased decisions even if the data does not explicitly mention a problematic feature (such as \"race\" or \"gender\"). The feature will correlate with other features (like \"address\", \"shopping history\" or \"first name\"), and the program will make the same decisions based on these features as it would on \"race\" or \"gender\". Moritz Hardt said \"the most robust fact in this research area is that fairness through blindness doesn't work.\"\nCriticism of COMPAS highlighted that machine learning models are designed to make \"predictions\" that are only valid if we assume that the future will resemble the past. If they are trained on data that includes the results of racist decisions in the past, machine learning models must predict that racist decisions will be made in the future. If an application then uses these predictions as recommendations, some of these \"recommendations\" will likely be racist. Thus, machine learning is not well suited to help make decisions in areas where there is hope that the future will be better than the past. It is descriptive rather than prescriptive.\nBias and unfairness may go undetected because the developers are overwhelmingly white and male: among AI engineers, about 4% are black and 20% are women.\nThere are various conflicting definitions and mathematical models of fairness. These notions depend on ethical assumptions, and are influenced by beliefs about society. One broad category is distributive fairness, which focuses on the outcomes, often identifying groups and seeking to compensate for statistical disparities. Representational fairness tries to ensure that AI systems do not reinforce negative stereotypes or render certain groups invisible. Procedural fairness focuses on the decision process rather than the outcome. The most relevant notions of fairness may depend on the context, notably the type of AI application and the stakeholders. The subjectivity in the notions of bias and fairness makes it difficult for companies to operationalize them. Having access to sensitive attributes such as race or gender is also considered by many AI ethicists to be necessary in order to compensate for biases, but it may conflict with anti-discrimination laws.\nAt the 2022 ACM Conference on Fairness, Accountability, and Transparency a paper reported that a CLIP‑based (Contrastive Language-Image Pre-training) robotic system reproduced harmful gender‑ and race‑linked stereotypes in a simulated manipulation task. The authors recommended robot‑learning methods which physically manifest such harms be \"paused, reworked, or even wound down when appropriate, until outcomes can be proven safe, effective, and just.\"","score":53.16641805025819,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"In the research community\nIn many cases, even AI researchers anthropomorphize AI systems in some capacity. Among the most extreme and well-publicized of these instances occurred in 2022, when engineer Blake Lemoine publicly claimed that Google's LLM LaMDA was conscious. Lemoine published the transcript of a conversation he had had with LaMDA regarding self identity and morality which he claimed was evidence of its sentience; he asserted that LaMDA was \"a person\" as defined by the United States Constitution and compared its mental capability to that of a 7- or 8-year-old. Lemoine's claims were widely dismissed by the scientific community and by Google itself, which described Lemoine's conclusions as \"wholly unfounded\" and fired him on the grounds that he had violated policies \"to safeguard product information\".\nIt is much more common that AI researchers unintentionally imply humanness of AI through the ordinary use of anthropomorphic language to describe nonhuman agents. This kind of language, which Daniel Dennett coined the \"intentional stance\", is very common in everyday life in a variety of different contexts (e.g., \"My computer doesn't want to turn on today\"). For AI agents that may actually appear to very closely replicate some human abilities, however, the casual use of such anthropomorphic language in research has been scrutinized for being potentially misleading to the public. As early as 1976, Drew McDermott criticized the research community for the use of \"wishful mnemonics\", where AIs were referred to with terms like \"understand\" and \"learn\". In the LLM era, these criticisms have further intensified, with the negative effects of AI anthropomorphism in the public posing an especially salient danger given the elevated accessibility of modern AI.\nIn some cases, the use of anthropomorphic language for AI is not unintentional, but is willfully used by researchers in order to promote better understanding of the brain – the idea being that, as AI can be functionally similar in some ways to the human brain, we may gain new insights and ideas from treating AI as a kind of model of the brain's workings. In particular, deep neuronal networks (DNNs) are often explicitly compared to the human brain, and significant advances in DNN research have stirred considerable enthusiasm about the ability of AI to emulate the human abilities. Caution has been urged in this domain as well, however; the use of anthropomorphic language can mask important differences that fundamentally distinguish AI from human intelligence. When it comes to DNNs, for example, it has been pointed out that they are still structurally quite different from the human brain, with much of what we know about human neurons not having been incorporated. It has also been argued that DNNs are less efficient and less durable in generating correct outputs than the human brain, given that they require significantly more training data than the brain and can sometimes be easily \"fooled\" by perturbations in input data. Given these fundamental differences, research focuses toward making AI as similar as possible to biological intelligence (which may be promoted by using anthropomorphic language) could hinder future AI development by limiting the proliferation of new theoretical and operational frameworks.","score":53.16641805025819,"links":[]},{"source":"wiki_artificial_intelligence.hat","text":"Human factors\nIn addition to AI factors contributing to anthropomorphizing, there are various features surrounding the user (i.e., the human interacting with the AI) that also play a role. The process of anthropomorphizing is very natural for humans and is ubiquitous across many different contexts. Epley et al. argue for a model with three psychological determinants that govern human tendencies to anthropomorphize. The first of those factors is elicited agent knowledge - the accessibility and applicability of knowledge about humans and the self, or the degree to which humans make inferences about other entities based on their own experience of being human. Individuals who tend to do this will anthropomorphize more; this explains why children anthropomorphize more than adults, since they lack complex models of nonhumans and rely heavily on self-based reasoning. The second factor in the model is effectance motivation – the need for humans to predict and reduce uncertainty in the environment. Anthropomorphizing can help people make sense of unpredictable phenomena by explaining them through intentional or human-like causes. Subsequent research has confirmed that individuals who express a need for order/closure and discomfort toward ambiguity tend to anthropomorphize more, possibly resulting from resolution of cognitive dissonance – human-like AIs may be highly ambiguous stimuli, and individuals who dislike ambiguity may be highly motivated to resolve the ambiguity by treating the AIs as more human. Finally, the third factor in the model is sociality motivation - the human need for social connection. People who feel chronically lonely or isolated may be increasingly likely to project human qualities onto non-human entities to satisfy their social needs.\nResearch has shown that, in general, anthropomorphic tendencies vary based on norms, experience, education, cognitive reasoning styles, and attachment. Users who are highly agreeable, for example, tend to be more susceptible to anthropomorphizing, as do individuals who are high in extraversion. Individuals with attachment anxiety have been shown to more often anthropomorphize AI. Young children are very prone to anthropomorphic attributions, but this propensity tends to decrease as children develop. Anthropomorphizing also tends to decrease with increased education and experience with technology.\nAdditionally, some effects have been shown in research to be dependent on culture. For example, a negative correlation was found between loneliness and anthropomorphizing in Chinese individuals, compared to the positive link found in Western cultures. This has been interpreted as possibly a result of differing drives for anthropomorphizing - people from Western cultures may anthropomorphize primarily as a means to counteract loneliness from a failure to cope with their social world, while people from East Asian cultures may already view nonhuman agents as part of their social world and anthropomorphize as a means of social exploration. Research has also shown that people tend to attribute more mental abilities and report more psychological closeness to robots that are presented as having the same cultural background as them.","score":53.16641805025819,"links":[]},{"source":"wiki_real_estate.hat","text":"The activity-based office\nThe activity-based office concept is said to increase productivity through the stimulation of interaction and communication while retaining employee satisfaction and reducing the accommodation costs. Although some research has gone into understanding the added value, there is still a need for sound data on the relationship between office design, its intentions and the actual use after implementation.\nThe concept of activity-based workplace has been implemented in organisations as a solution to improve office space efficiency. However, the question of whether or not office workers' comfort or productivity are compromised in the pursuit of space efficiency has not been fully investigated. There are obstacles and issues of concern when practicing the activity-based office concept. A study carried out in activity-based workplace settings reports that employees without an assigned desk complain of desk shortages, difficulty finding colleagues which limits immediate collaboration, wasted time finding and setting up a workstation, and limited ability to adjust or personalise workstations to meet individual ergonomic needs. Another study suggest the impact of office design on occupants' satisfaction, perceived productivity and health, pointing towards reduced time workers spent seated in ABW offices\nThe most recent study released in 2020 by Veldhoen + Company, the founders of Activity Based Working, was the biggest global research project on Activity Based Working. The research set out to understand the measurable impact of Activity Based Working and the drivers of success in Activity Based Working transitions. The research project was started in July 2019, and was impacted by the COVID-19 pandemic in 2020. The report included 32,369 responses spanning 11 countries, and explored questions used in Leesman Index surveys providing valuable context to understanding office workers' behaviour - with the opportunity to explore what factors would be most important as organisations transition to a post-pandemic return to the office. The data tells us not only what type of workplace to return to, but also how to do so.","score":45.103059609765616,"links":[]},{"source":"web_1779060046.hat","text":"API Reference Copy page Copy Built-in React Hooks Hooks let you use different React features from your components. You can either use the built-in Hooks or combine them to build your own. This page lists all built-in Hooks in React. State Hooks State lets a component “remember” information like user input. For example, a form component can use state to store the input value, while an image gallery component can use state to store the selected image index. To add state to a component, use one of these Hooks: useState declares a state variable that you can update directly. useReducer declares a state variable with the update logic inside a reducer function. function ImageGallery ( ) { const [ index , setIndex ] = useState ( 0 ) ; // ... Context Hooks Context lets a component receive information from distant parents without passing it as props. For example, your app’s top-level component can pass the current UI theme to all components below, no matter how deep. useContext reads and subscribes to a context. function Button ( ) { const theme = useContext ( ThemeContext ) ; // ... Ref Hooks Refs let a component hold some information that isn’t used for rendering, like a DOM node or a timeout ID. Unlike with state, updating a ref does not re-render your component. Refs are an “escape hatch” from the React paradigm. They are useful when you need to work with non-React systems, such as the built-in browser APIs. useRef declares a ref. You can hold any value in it, but most often it’s used to hold a DOM node. useImperativeHandle lets you customize the ref exposed by your component. This is rarely used. function Form ( ) { const inputRef = useRef ( null ) ; // ... Effect Hooks Effects let a component connect to and synchronize with external systems. This includes dealing with network, browser DOM, animations, widgets written using a different UI library, and other non-React code. useEffect connects a component to an external system. function ChatRoom ( { roomId } ) { useEffect ( ( ) => { const connection = createConnection ( roomId ) ; connection . connect ( ) ; return ( ) => connection . disconnect ( ) ; } , [ roomId ] ) ; // ... Effects are an “escape hatch” from the React paradigm. Don’t use Effects to orchestrate the data flow of your application. If you’re not interacting with an external system, you might not need an Effect. There are two rarely used variations of useEffect with differences in timing: useLayoutEffect fires before the browser repaints the screen. You can measure layout here. useInsertionEffect fires before React makes changes to the DOM. Libraries can insert dynamic CSS here. You can also separate events from Effects: useEffectEvent creates a non-reactive event to fire from any Effect hook. Performance Hooks A common way to optimize re-rendering performance is to skip unnecessary work. For example, you can tell React to reuse a cached calculation or to skip a re-render if the data has not changed since the previous render. To skip calculations and unnecessary re-rendering, use one of these Hooks: useMemo lets you cache the result of an expensive calculation. useCallback lets you cache a function definition before passing it down to an optimized component. function TodoList ( { todos , tab , theme } ) { const visibleTodos = useMemo ( ( ) => filterTodos ( todos , tab ) , [ todos , tab ] ) ; // ... } Sometimes, you can’t skip re-rendering because the screen actually needs to update. In that case, you can improve performance by separating blocking updates that must be synchronous (like typing into an input) from non-blocking updates which don’t need to block the user interface (like updating a chart). To prioritize rendering, use one of these Hooks: useTransition lets you mark a state transition as non-blocking and allow other updates to interrupt it. useDeferredValue lets you defer updating a non-critical part of the UI and let other parts update first. Other Hooks These Hooks are mostly useful to library authors and aren’t commonly used in the application code. useDebugValue lets you customize the label React DevTools displays for your custom Hook. useId lets a component associate a unique ID with itself. Typically used with accessibility APIs. useSyncExternalStore lets a component subscribe to an external store. useActionState allows you to manage state of actions. Your own Hooks You can also define your own custom Hooks as JavaScript functions. Previous Overview Next useActionState","score":27.12400931168676,"links":[]},{"source":"web_1779060046.hat","text":"API Reference Copy page Copy Built-in React APIs In addition to Hooks and Components , the react package exports a few other APIs that are useful for defining components. This page lists all the remaining modern React APIs. createContext lets you define and provide context to the child components. Used with useContext . lazy lets you defer loading a component’s code until it’s rendered for the first time. memo lets your component skip re-renders with same props. Used with useMemo and useCallback . startTransition lets you mark a state update as non-urgent. Similar to useTransition . act lets you wrap renders and interactions in tests to ensure updates have processed before making assertions. Resource APIs Resources can be accessed by a component without having them as part of their state. For example, a component can read a message from a Promise or read styling information from a context. To read a value from a resource, use this API: use lets you read the value of a resource like a Promise or context . function MessageComponent ( { messagePromise } ) { const message = use ( messagePromise ) ; const theme = use ( ThemeContext ) ; // ... } Previous <ViewTransition> Next act","score":14.28100232792169,"links":[]}]},"metadata":{},"timestamp":"2026-07-08T22:48:24.215Z"}