Athreya, Sakshi

May 26, 2021

3) What features of knowledge impact on its reliability?

Object 1: Trump Tweet


Figure 1: Trump Tweet

Prior American president Donald Trump claimed that in the 2020 elections he won via his public Twitter account. Trump’s highly publicised knowledge claim was proven false by professionals and news institutions, however it was perceived reliable by some or prompted questioning of reliability by others. This object exemplifies how the public’s judgement of the reliability of knowledge claims communicated through technology can be impacted by authority, the existence of multiple sources and absence of fact checking.

While searching for reliability through the dissemination of knowledge, knowers often rely on authority associated with the communicator of knowledge. A claim communicated through social media may not be reliable, however pre-existing knowledge of a claim’s producer can impact the perceived reliability of the tweet. Donald trump was elected the president of United States and his twitter account was a verified account suggesting reliability. While it was officially announced that Biden won the by official sources which provided statistical evidence eg. guardian,Trump’s claim maybe believed as true by readers of both claims, specifically those who support him and thus are emotionally attached to him.

Knowers’ judgements of a single claim’s reliability can be affected by the existence of other supporting or contradictory claims. Fact checking measures reliability through gathering evidence or using external claims to support a claim. Trump’s tweet and knowledge claim was contradicted by leading news outlets who claimed he didn’t win the public vote with official statistical evidence. A scenario of confusion was caused where the public had to chose whether to perceive Trump or the newspapers’ claims as more reliable, regardless of which was actually more reliable. Perceived reliability is influenced by reason such as the newspaper but faith and emotion especially support for Trump also play a factor. The existence of contradictory claims can highlight how perceived reliability is not always dependent upon rational evidence or supporting claims, and further how the judgements can change in different contexts and communication mediums. Despite empowering individuals, this is dangerous when potentially harmful claims are perceived as reliable when they are actually not.

Object 2: Google Translate


Figure 2: GoogleTranslate

The English phrase “I like eating food” was translated to Hindi using GoogleTranslate. GoogleTranslate produced this linguistic knowledge based upon its algorithm . However, a native Hindi and English speaker found that GoogleTranslate’s Hindi phrase was translated to “I like eat food,” grammatically incorrect linguistic knowledge. This translation shows how reliability can be judged differently via artificial intelligence versus human intelligence which further highlights the reliability of knowledge generated through technologically which is interesting from a TOK perspective.

This GoogleTranslate example displays how the machine generated translation captures the idea of the original phrase. However, its coherence is limited by the difference between “I like eat” and “I like eating.” The GoogleTranslate algorithm reliably directly translated, word-for-word the phrase however overlooked the grammatical intricacies as it uses machine learning not human cognition. Spoken languages and their grammatical structures are highly sensitive to geographical and temporal context as well as the interpersonal dynamics between any two people talking. GoogleTranslate’s unawareness of geographical or social context when translating may be judged as unreliable as it’s technology cannot currently use emotion as a way of knowing to determine most suitable translation for a given context.

On the other hand, GoogleTranslate can be perceived as reliable despite its inability to structure and phrasing. Language may be interpreted in numerous ways and is not always a rational “1 + 1= 2 situation.” In one language, there may be many ways of phrasing the same idea, depending on context, personality or dialect. Some knower’s may argue that GoogleTranslate’s “I like eat” is a mistranslation, however some native Indian groups may speak in this non grammatically correct manner showing that perceived reliability can differ even within a group of native Hindi speakers.Thus, a feature of knowledge affecting perceived and actual reliability would be perspective and ways of knowing used when producing knowledge.

Object 3: Facetune

Object 3: Facetune

This object is the combination of an original photograph and a “facetuned” version of it. The FaceTune app was developed by LightTricks (2013) to allow users to edit their physical appearances in photographs. The 2 images above exemplify how a woman could edit her appearances, and thus the drastic yet realistic visually perceptible differences between them.2 Exploring these differences is interesting from a TOK perspective as they highlight how firstly technology can be used to capture a true visual representation of someone, but secondly how it can be used to create a false representation in a manner that seems reliable. Technology can be used to communicate false, visually acquired knowledge convincingly and thus influence how we learn about our world through generated images.

A knowledge feature is the mechanism and/or ways of knowing through which it is produced and/or communicated. To what extent an image is reliable is a subjective judgement. The images generated by FaceTune may seem reliable as they are viewed through individuals’ sense perception meaning that individuals produce knowledge from what they see. Humans often trust what they see through experience as reliable, for example if one person was to only see the edited image and not the original then it would lead them to believe that is how the person looks

A feature impacting judgments of reliability is a learner’s pre-existing knowledge of a subject or the technology used to produce claims. The FaceTune image within this chosen object may be deemed unreliable by those who saw the original photograph, personally know the subject or can identify FaceTune images from tell-tale signs. Pre-existing rational knowledge of a technology and its capabilities can impact how someone judges whether or not a knowledge source and claim is reliable. This object’s combination of an original photograph with its FaceTuned version highlights how real world knowledge issues arise when when images are judged reliable by viewers with no pre-existing knowledge of photographed individuals, especially when used for official identification purposes.


  1. TRUMP TWEET – 2020. Trump falsely claims he won the election; Twitter flags the tweet. [online] Available at: < tweet-claims-he-won-election-twitter-flags/> [Accessed 9 April 2021].
  2. Google Translate n.d. Before you continue. [online] Available at: <> [Accessed 9 April 2021].
  3. FACETUNED IMAGE – Insider. 2020. I asked influencers to edit my selfies and turn me into an entirely different person, and it just reminded me how damaging it is to chase an unattainable idea of perfection. [online] Available at: <https:// different-2020-6> [Accessed 6 April 2021].
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