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Evaluating websites, news and media: Recognising bias

Identifying bias

The CRAP Test or SIFT Moves are great for initially evaluating online sources' credibility. Once you establish that a source is overall credible and useful for your work, you'll often want to read and evaluate the source more closely looking closely for bias and perspective.

Identifying bias can be tricky, as it is not clearly stated, but all sources should be evaluated for potential bias - from news, social media to scholarly articles. There are different types of bias to look out for and strategies you can use to identify biased sources.

If your information source is:

  • Heavily opinionated or one-sided
  • Relies on unsupported or unsubstantiated claims
  • Presents highly selected facts that lean to a certain outcome
  • Pretends to present facts, but offers only opinion
  • Uses extreme or inappropriate language
  • Tries to persuade you to think a certain way with no regard for factual evidence
  • The author is unidentifiable, lacks expertise, or writes on unrelated topics
  • The author is affiliated with an organisation and there is conflict of interest
  • Is entertainment-based or a form of parody or satire
  • Tries to sell you something in disguise

There may be bias!

You can read more about the 20 cognitive biases that influence your decisions. One of which is confirmation bias, which is the tendency to search for, interpret, favour or recall information in a way that confirms or supports your existing beliefs or values.

Algorithmic bias

As you evaluate your resources it can be useful to think about how you found the resources and was an algorithm involved? Could this algorithm have influenced your results? If you understand how algorithms work and where they are being used it can help you in the evaluation process and to avoid algorithmic bias.

Where are algorithms?

They are invisible! But they are working in the background when you use Google search, ChatGPT/Bard, social media, news feeds, even our very own Primo Search and the library’s databases has algorithms ranking your results.

Algorithms are designed to keep you engaged with the service and products (think Netflix, Facebook and Spotify). These companies collect data based on your online activity and use that data to tailor what you see and what you don’t see.

So, you don’t have to understand how the algorithms are working but it is important to recognise what the algorithm uses as inputs (this is all the data the algorithm has to work with), what operations it performs on these inputs (does it combine data from multiple sources) and how it generates the outputs that you see.

Since the information we see is personalised based on our previous searches, we are less likely to see diverse perspectives or conflicting points of view and the internet becomes an echo chamber, reinforcing our beliefs. Which leads us to algorithmic bias.

What is algorithmic bias?

Algorithmic bias describes systematic and repeatable errors in a computer system that create unfair outcomes. It also occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process. A filter bubble is an algorithmic bias that skews or limits the information a user sees on the internet. Watch the TED talk Beware of the filter bubble.

What can I do?

  • Understand where algorithms are working
  • Check your privacy and ad settings on any online services you are subscribed to
  • Read privacy statements to find out how your data is shared
  • Be mindful – follow news stories across multiple sites and use a variety of search engines. Try a search engine that doesn’t collect your data like DuckDuckGo
  • Consider what searches you have undertaken and how these inputs might be analysed
  • Examine your bias – how do your personal views affect the way you look for and interpret information?

Media bias

News will be biased. Look out for hidden media bias that misleads, manipulates and divides us. 

The image below is one opinion, but highlights that media in Australia comprises of different levels of bias and reliability.

Source: u/PolitiQuoll, 2018, Reddit

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