3.1: Readings
- Page ID
- 261458
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Let’s explore bias in information. Bias is a prejudice, inclination, or preference that affects our abilities to judge information impartially. We find bias not only in the ways we interact with information, but also how information is organized in libraries and how it is presented to us online. For example, the Library of Congress system (discussed in module 2) was developed in 1897, and the subject headings and their order reveal the biases in scholarship at the time, privileging an American perspective, Western European colonialism, and including systemic racism and sexism, among many other biases. Librarians are working to repair this, in the way subject headings are currently being used and added, to amplify the voices of marginalized communities.
In addition to the bias we see in how information is organized, bias is present in information sources, and in the ways we engage with information on a daily basis:
- Bias in the media is something we encounter all the time. The chart below (which is continuously updated; click here to see the most recent version) compares dozens of media outlets based on their relative political bias, with news value and reliability on the vertical axis and political leanings of the outlets on the horizontal axis (Interactive Media Bias Chart).
Figure \(\PageIndex{1}\): Interactive Media Bias Chart (screenshot 24.6.21).
- Implicit bias or unconscious bias: Unconscious attitudes or stereotypes affect how we engage with the world, from our perceptions, to decisions and actions. Implicit bias can contribute to people’s unequal treatment, based on their race, ability, gender identity, sexual orientation, religion, socioeconomic status, and more.
- Bias in Search Algorithms: In addition to implicit bias, search engine algorithms can also perpetuate bias. These algorithms are complex sets of instructions that determine how search results are ranked and displayed. While algorithms are designed to be efficient, they can inherit and amplify biases present in the data they are trained on. For example, searching for a "successful entrepreneur" might return primarily results featuring men, reflecting a historical bias in the business world. Similarly, searching for a particular skill or profession might return gendered results, such as "nurse" returning mostly female results or "engineer" mostly male.
This report from the Kirwan Institute at The Ohio State University discusses implicit bias a bit further, and shows some of its serious real world consequences (https://www.nea.org/sites/default/files/2021-02/2.6%20Implicit%20Bias.pdf).
- Confirmation bias: People tend to favor information that matches their pre-existing expectations, beliefs, or hypotheses, and therefore confirms these ideas for them.
Figure \(\PageIndex{1}\): Confirmation bias Venn diagram. (Copyright Sara Klein)
On the web this is reflected back to us in filter bubbles, where our search results are pre-filtered by search engines according to what they think we want to see, according to our search histories.