Teaching Algorithmic Literacy within a Media Literacy Program

Introduction

Algorithms are not new, but the exponential growth of their influence on daily life in the 21^st century means that it is imperative to better educate the public about them. Algorithms are embedded in the media landscape as well as other domains, and, it is from our own media use that they gather much of the data they use to make decisions about and for us. They mediate, augment, produce, regulate and make consequential decisions in our lives. Thus, a new type of literacy needs to be added to media and digital literacies – algorithmic literacy.

Use of the term literacy is deliberate. Although literacies depend on a base of knowledge and skills, they also include ways of thinking and knowing that go beyond simplistic competencies. This is exactly the kind of approach needed to uncover and analyze – and to govern – the use of algorithms. Recent research has shown that knowledge in this domain is lacking (e.g. Cotter & Reisdorf, 2020; Gran et al., 2021; Head et al., 2020). Students do “not fully understand how big data and artificial intelligence (AI) are being used in educational technology and society. Neither do their professors” (Head et al., 2020, p. 1). Yet, from health care to lending, from the news stories chosen for you to the songs recommended for your listening, algorithms are everywhere. As media literacy proponents, we recognize the need for education, now. But where do we start? Literature on this topic has only appeared in the past few years, and, there are as yet few activities and lesson plans available on this topic. The topic itself also poses some unique challenges. This article suggests ways to start and grow algorithmic literacy in any level of education and any subject area. And once we begin, we can share such resources.

Why Algorithmic Literacy?

Algorithms are processes or sets of rules for problem-solving operations, especially by a computer. One type of algorithm is a machine learning algorithm, which, instead of repeatedly processing a stable set of instructions, rewrites itself as it works. To do this, it uses large data sets and evaluates the success of its predictions. Machine learning algorithms are sometimes (improperly) equated with artificial intelligence (AI), although this is somewhat of a misnomer as current technology is a long way from what we know as human intelligence; this is one of a handful of misconceptions about algorithms (Long & Magerko, 2020; Zarouli, Helberger, & de Vreese, 2021). Types of algorithms that most students encounter on a daily basis include: Search Engines that help users find ‘relevant’ information online, Content Moderation Engines that use “ranking” to determine what information to show, Recommendation Engines, systems that predict and narrow choices and offer ‘relevant’ suggestions (e.g., Netflix, TikTok, and online shopping), and Location Engines that make decisions based on GPS information (employed in ride sharing, maps, and availability/delivery of goods and services). What ‘relevant’ and ‘ranking’ mean is up to the algorithm and its creators – and this is a good place to begin discussion with students.

The prevalence of algorithms demands that we develop awareness, understanding, and opinions about them. Many college students recognize the role algorithms play in choosing content and targeted ads, however, they are less aware of the use of algorithms in other areas of their lives and the way that data collection affects them; most importantly, they feel helpless to make change (Head et al., 2020). We also need to teach that algorithms are not neutral. Like other technologies, they are made and used by humans. They “reflect and promulgate certain ideologies and have impacts and influences in the full range of human society. Cautions about algorithmic decision-making have identified the far-reaching implications for bias, fairness, privacy, and democratic processes” (Ridley & Pawlick-Potts, 2021, p. 2). In some domains, algorithms have the potential to unfairly disrupt lives, sway public opinion, and build divisions between members of society. Although we often “scarcely notice or question these data-based operations, yet they are not neutral, they shape particular social realities for us and should be debated” (Lomberg & Kapsch, 2019, p. 2). We can teach students of any age that:

a great deal of expertise, judgement, choice and constraints are exercised in producing algorithms. Moreover, algorithms are created for purposes that are often far from neutral: to create value and capital; to nudge behavior and structure preferences in a certain way; and to identify, sort and classify people…What this means is that algorithms need to be understood as relational, contingent, contextual in nature, framed within the wider context of their socio-technical assemblage. (Kitchin, 2017, p 18)

The need for algorithmic literacy arises from two key and equally important perspectives: control and empowerment. Building algorithmic literacy “is needed to acknowledge both the technology’s power (control) over people and power (empowerment) for people” (Ridley & Pawlick-Potts, 2021, p. 5). On one hand, this literacy can help us embrace the possibilities and promises, and on the other, to exercise control over where and when they act upon us. Importantly, current digital and information literacy does not provide algorithmic literacy (Ridley & Pawlick-Potts, 2021, p. 1). Yet this is key, write Gran et al. (2021), because “knowing more about the structural forces that shape the Web is not just an online navigational skill, but a necessary condition managing information as an informed citizen” (p. 1790). Informed citizens will recognize and be able to articulate that “what is at stake then with the rise of ‘algorithm machines’ is new forms of algorithmic power that are reshaping how social and economic systems work” (Kitchin, 2017, p. 16).

Several authors have suggested that algorithmic literacy is part of a new digital divide (e.g. Cotter & Reisdorf, 2020, Gran et al., 2021, Zarouali, Helberger, & de Vreese, 2021). Cotter and Reisedorf defined this divide as disparities in algorithmic knowledge that “create classes of users with the skills to question and critique algorithmic representations of reality and classes more likely to unwittingly internalize the normative discourses inscribed in algorithmic outputs” (2020, p. 749-750). In their study, they found that lower socioeconomic status was related to lower algorithmic knowledge, a finding confirmed by others. Cotter and Reisdorf state that those with greater knowledge of algorithms have significant advantages and privileges in driving decision-making in society. Gran et al. (2021) found that in Norway, 41% of participants indicated no awareness of algorithms and 21% a low awareness, calling it “a rather bleak picture” (p. 1790). This study further confirmed that traditional digital divide factors such as age and education level were related to algorithmic awareness. They concluded that “these divides speak to WHO gets to make informed decisions about digital infrastructure – those who have the means/resources to question the processes and those who do not,” and that “one might argue that algorithmic awareness and literacy, as a meta-skill, are necessary conditions for an enlightened and rewarding online life” (p. 1791).

Experts responding to a Pew study “predicted that an algorithm-assisted future will widen the gap between the digitally savvy (predominantly the most well-off, who are the most desired demographic in the new information ecosystem) and those who are not nearly as connected or able to participate” (Rainie & Anderson, 2017, n.p.). For example, David Lankes (professor and director at the University of South Carolina School of Library and Information Science) wrote “unless there is an increased effort to make true information literacy a part of basic education, there will be a class of people who can use algorithms and a class used by algorithms” (quoted in Rainie & Anderson, 2017, n.p.).

Defining Algorithmic Literacy

Literacy is a term that has often been overused in non-academic circles. But to those who research and teach literacies (information, news, digital, media, etc.) it encompasses more than just technical skills. Literacy also has a social dimension in that it includes evaluating information’s impact beyond the individual. Ridley & Pawlick-Potts (2021) define it this way: “literacy enables a reflective, critical, and integrative approach to information that utilizes a broad knowledge base for both understanding and communicating ideas” (p. 3).

Applying literacy to the domain of algorithms, Finn (2017) writes: “we need a kind of algorithmic literacy, one that builds from a basic understanding of computational systems, their potential and their limitations, to offer us intellectual tools for interpreting the algorithms shaping and producing knowledge” (p. 3). Long and Magerko (2020) define algorithmic literacy as “a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace” (p. 2). The authors present a set of 17 core competencies and 15 associated learning design considerations, which center around enabling students to answer the following key questions: “What is AI?; What can AI do?; How does AI work?; How should AI be used?; and How do people perceive AI?” (p. 3). Ridley & Pawlick-Potts, speaking to librarians, define algorithmic literacy as:

The skill, expertise, and awareness to

• Understand and reason about algorithms and their processes
• Recognize and interpret their use in systems (whether embedded or overt)
• Create and apply algorithmic techniques and tools to problems in a variety of domains
• Assess the influence and effect of algorithms in social, cultural, economic, and political contexts
• Position the individual as a co-constituent in algorithmic decision-making. (p. 4)

It has been proposed that algorithmic literacy has several dimensions. The two most important are awareness (knowing that an algorithm is in use, what algorithms are used for, and in what contexts they are used) and knowledge (understanding how they work, their capabilities and goals, and their implications for users) (Dogruel et al., 2021; Hargittai et al., 2020). Other aspects that should also be investigated include attitudes about algorithms and evaluation of their effects including ethics and social and political implications, as well as actions taken in response to the other dimensions.  Several authors have proposed quantitative scales to measure algorithmic literacy. These include the Algorithmic Media Content Awareness Scale (Zarouali, Boerman, & de Vreese, 2021), which is specific to algorithms that manage content on social media platforms (e.g., Facebook, YouTube, Netflix). This scale would be a good place to start for students because it is on these platforms that they encounter the effects of algorithms most frequently.  However, algorithms appear in many other domains, so Dogruel at al. (2021) proposed and tested a platform-neutral scale that included an 11-item measure for awareness and an 11-item scale for knowledge.

Understanding algorithms has many challenges. Algorithms “are complex, opaque, invisible, shielded by intellectual property protection” (Ridley & Pawlick-Potts, 2021, p. 4) and as a result, are often called ‘black boxes’ (e.g., Hargittai et al., 2020).  In addition, companies optimize algorithms to feel invisible and become normalized, making awareness even more difficult, and leading to the lack of a “ground truth” about how algorithms operate for researchers and teachers to rely upon (Zarouali, Helberger, & de Vreese, 2021; Hargittai et al., 2020). Indeed,

the inner workings of even the most common algorithms can be confusing to users. Facebook is among the most popular social media platforms, but roughly half of Facebook users – including six-in-ten users ages 50 and older – say they do not understand how the site’s algorithmically generated news feed selects which posts to show them. And around three-quarters of Facebook users are not aware that the site automatically estimates their interests and preferences based on their online behaviors in order to deliver them targeted advertisements and other content. (Smith, 2019, n.p.)

However, being able to read and understand code is only one possible component to algorithmic literacy. Algorithms “can be thought about in a number of ways: technically, computationally, mathematically, politically, culturally, economically, contextually, materially, philosophically, ethically and so on” (Kitchin, 2017, p. 16). Although “we cannot study algorithmic operations directly…we should study the processes by which they are made, tweaked and put to work…If we cannot open the black box itself, we can study the relationships that people experience with algorithms” (Lomborg & Kapsch, 2019, p. 2). This outlook fits well with the tenets of media literacy education, and means that it can be studied by students in any subject area; it particularly calls for those in the Liberal Arts, Humanities, and Sociology to participate.

Teaching Algorithmic Literacy

Step 1: Assess to Find Knowledge Gaps

What do you know about algorithms? What do your students know? This is an important step to determine where and how to begin. As mentioned above, there are some proposed scales for measuring algorithmic literacy (Dogruel et al., 2021; Zarouali et al., 2021). You might begin by taking them yourself; in addition, a formal or informal classroom survey will help establish how much your students know. Another way to assess current knowledge is through exploring misconceptions (e.g., Zarouli, Helberger, & de Vreese, 2021). Although students may claim to understand algorithms, can they identify where algorithms are acting in a day or a week of their life? Do they understand the goals of these algorithms? And most importantly, do they recognize areas for concern, such as potential for bias and data privacy, that come with algorithms? Finding gaps in knowledge, understanding, and application will guide where to begin and what to cover.

Step 2: Educate Yourself

Whether you prefer books, videos, news investigations or podcasts, there are a variety of helpful resources for understanding algorithms (See Recommended Reading Appendix). Short videos such as TED talks, YouTube tutorials, and educational explainers are easy to understand and take little time; in the process of viewing yourself, you will likely find some that would appeal to your students. Longer videos including full-length documentaries (such as Coded Bias) also appeal to the visual learner and can become part of the classroom lessons as well. If you prefer to listen, there are a number of podcasts available (e.g. NPR’s On The Media), including some featuring the authors of books on big data and algorithms, so search for their materials (e.g. Cathy O’Neil, Safia Noble, Joy Buolamwini, Ruha Benjamin). For a deeper dive, grab one or more books, or explore journal articles such as those cited by articles in this issue of JML. Check your favorite educational website (Common Sense Media, for example) for lesson plans and teacher education about algorithms.

Step 3: Find a Relevant Hook

Where do students encounter algorithms in their lives? Where in your curriculum do algorithms fit? Making algorithms relevant to your audience is key to getting and keeping attention for what can be a steep learning curve. There are interesting and engaging lessons that help students see how search and recommendations differ because of algorithms. For example, most students will have used a search engine like Google. Therefore, a lesson about differing results for different users (and geographies, see Search Atlas in Recommended Readings) may engage students’ interest. Examples can be tailored to searches within your subject area – a search on climate change, for example, may result in different results that will demonstrate the search engine algorithm at work. Another common algorithm is the recommendation engine, and it is most obvious on TikTok. No two students will see the same recommendation, and they can easily choose a few videos to watch and see how that recommendation changes as a result. Another way to hook students is by using a domain-specific example. For example, algorithms play a part in health care, criminal justice, human resources (hiring), immigration, and more. Investigative reporting on algorithms from the New York Times, Wired Magazine, and ProPublica are easily digestible by instructors and students. Finally, creating a class quiz about algorithms or misconceptions may engage students.

Step 4: Start Small

In many cases, students need to understand the basic vocabulary of algorithms in order to read and discuss them. Existing units on media literacy, privacy and big data can be enhanced to include algorithms. A single lecture or unit may be developed, centering around definitions, misconceptions, the discussion of a video or article, or the outcomes of completing an algorithmic literacy scale. Remember that for short lessons, keep it balanced – talk about both the advantages and challenges of algorithms, even if discussion won’t reach to meeting and solving those challenges. Not only is algorithmic literacy needed to avoid negative personal and social effects of algorithms, but also to recognize “the full potential of algorithms for societal good” (Zarouali, Helberger, & de Vreese, 2021, p. 135).

In domain-specific courses, specific examples of algorithmic decision-making can be explored in a lesson or as a part of a series of lessons. For example, algorithms have been used and discussed in human resources, genome sequencing, immigration, banking, and criminal justice. Job seekers may be enticed to explore the use of algorithms in hiring, future health care workers in the mis-use of predictive health care algorithms, and law enforcement in the use of algorithms in courtroom sentencing. This is a good place to begin if you plan to go deeper; “users may more easily identify and discern critical implications of algorithmic operations in contexts where real-world stakes are perceived as high” (Lomborg & Kitsch, 2019, p 14).

In the spirit of media literacy, we can form and key questions which mirror those that are often used to analyze media messages (e.g., Common Sense Media). For example, Key Questions to Ask When Analyzing an Algorithm may include:

1. Who created and owns this algorithm?
2. What are the goals of this algorithm? Why was it created?
3. How does this algorithm impact me?
4. How does this algorithm impact others and society in general?
5. How can this algorithm be held to ethical standards?

Step 5: Go Deeper

When students understand and are aware of algorithms, it is time to move from instruction to discussion. Once enabled with facts and examples, (older) students should explore, reflect, and become active. Algorithms continue to be developed and deployed, and solutions to algorithmic challenges are still being sought, so you can construct ideas along with your students. For reflection, journaling or position papers are useful assignments. These may examine the pros and cons of a specific application or algorithm. Deeper case studies with multiple sources can be assigned. For action and activity, students can discover and recommend which habits and social media use could be changed and how they affect the algorithm’s influence in their lives. Lessons and tools have also been developed that enable students to build algorithms and see how they work, even for young K-12 students (the terms used by these tools and their learning objectives include computational literacy or algorithmic thinking).

Step 6: Move to Advocacy

Awareness and knowledge are crucial first steps toward algorithmic literacy. However, like in media literacy, action is a part of the lesson. Such action may need to be urged by teachers, because “knowledge itself does not seem to prompt more critical engagement with and valuation of algorithms” (Lomborg & Kapsch, 2019, p. 13). Social justice lessons are a good place to insert algorithmic activism. As with other social and technical issues, assign students to create artifacts to educate others, whether peers, their campus. or their community. They can practice skills in speaking and presenting, designing and visuals, and even video. Ask them to write to stakeholders such as educators, legislators, and social media owners presenting problems and solutions. Explore national movements such as the Center for Human Technology and the Algorithmic Justice League (see Recommended Reading). The goal is to “help raise people’s critical consciousness, suggest ways to equip people to know and be able to make more informed decisions, and thereby enable a public conversation about what algorithms should and should not be made to do” (Lomborg & Kapsch, 2019, p. 2). And, individually, the fact that algorithms learn from the data we create allows a unique personal agency through choosing and controlling our data privacy – to “push back on the inscribed, and thus preferred, use of a technology…users may challenge existing ideologies and power differentials between designers and users” (Lomborg & Kapsch, 2019, p 5).

Step 7: Share It Out

In order to be successful at increasing algorithmic literacy, teachers and instructors need to pilot and share classroom materials, lessons, activities, and outcomes in the same ways that we have been sharing other media literacy resources. Outlets may include making materials available online through your own or an organization’s repository, publishing in a teaching journal, sharing with fellow instructors, and hosting workshops and panels at conferences and professional development events.

Conclusion

A growing number of researchers, teachers, and librarians have called for education about algorithms. For example,Head, et al. (2020) in their educational study write that “we are facing a lack of public knowledge about who holds power over information systems and how that power is wielded, a gap in understanding that educators need to begin to address” (p. 7) and that “our findings suggest the age of algorithms demands that teaching strategies be reconsidered as we redefine information literacy” (p. 28).  Gran, et al. (2021) go further, concluding that: “algorithm awareness becomes an issue of agency, public life, and democracy” because algorithms are “fundamentally embedded in crucial decision-making processes in most sectors of society” and “often work to perpetuate structural inequalities and historical bias in sometimes unforeseen ways” (1779-1780). Thus, teaching algorithmic literacy, like media literacy, becomes an urgent matter of preventing digital divides and halting the enculturation of bias into applications that potentially impact many lives cognitively, emotionally, and yes, even physically. Zarouali et al. (2021) speak directly to the concerns of media literacy researchers:

On the one hand, being aware of algorithmic recommendations on online platforms might encourage online users to make more critical reflections and decisions regarding the content they are being presented on these platforms. On the other, a lack of algorithmic awareness might contribute to major societal problems, such as the spread of mis- and disinformation, the proliferation of filter bubbles, an increased susceptibility to data-driven manipulation, and the reinforcement of stereotypes, inequalities and discrimination. (p. 2)

These issues, particularly misinformation, are not new to media literacy, but, rather than focus only on source credibility, we need to explore with students the reasons why such information is created, disseminated, and popularized – by algorithms. And rather than leaving students to feel helpless, we need to teach a literacy that enables and empowers them to make individual and cooperative changes to harness the disadvantages of algorithms. Ridley and Pawlick-Potts (2021), speaking to librarians, summarize why this particular literacy is so important in an educational context: “algorithms are not a technology like AI or, more generally, computers. Algorithms provide a structure that frames—and constrains—how we express ourselves. They are a way of seeing and acting in the world”  (p. 18).

I believe that the main questions we need to ask as educators are those posed by Zarouali, Helberger, & de Vreese, (2020): “(1) Will citizens be able to ask the right critical questions about the role and functioning of digital technology?, (2) does the population possess the necessary level of literacy to benefit from these systems?, and (3) are users sufficiently prepared to recognized and protect themselves from possible negative consequences of these technologies?” (p. 141). How can you make a difference in these outcomes? As media literacy educators it is imperative that we extend literacy to include new types of digital literacies as soon as they enter and impact us. The prevalence and impact of algorithms makes algorithmic literacy critical.

References

Cotter, K., & Reisdorf, B. C. (2020). Algorithmic knowledge gaps: A new dimension of (digital) inequality. International Journal of Communication, 14,745-765.

Dogruel, L., Masur, P., & Joeckel, S. (2021). Development and validation of an algorithm literacy scale for Internet users. Communication Methods and Measures, 1-19. https://doi.org/10.1080/19312458.2021.1968361

Finn, E. (2017). Algorithms of the enlightenment. Issues in Science and Technology, XXXII (3).https://issues.org/perspective-algorithm-of-the-enlightenment/

Gran, A. B., Booth, P., & Bucher, T. (2021). To be or not to be algorithm aware: A question of a new digital divide? Information, Communication & Society, 24(12), 1779-1796. https://doi.org/10.1080/1369118X.2020.1736124

Hargittai, E., Gruber, J., Djukaric, T., Fuchs, J., & Brombach, L. (2020). Black box measures? How to study people’s algorithm skills. Information, Communication & Society, 23(5), 764-775. https://doi.org/10.1080/1369118X.2020.1713846

Head, A. J., Fister, B., & MacMillan, M. (2020). Information literacy in the age of algorithms: Student experiences with news and information, and the need for change. Project Information Literacy. https://projectinfolit.org/pubs/algorithm-study/pil_algorithm-study_2020-01-15.pdf

Kitchin, R. (2017) Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14-29. https://doi.org/10.1080/1369118X.2016.1154087

Lomborg, S., & Kapsch, P. H. (2020). Decoding algorithms. Media, Culture & Society, 42(5), 745-761. https://doi.org/10.1177%2F0163443719855301

Long, D., & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-16).

Mendoza, K. (2018) 5 Questions Students Should Ask About Media. Common Sense Media. https://www.commonsense.org/education/articles/5-questions-students-should-ask-about-media

Rainie, L. & Anderson, J. (2017, February 8). Code-dependent: Pros and cons of the algorithm age. Pew Research. https://www.pewresearch.org/internet/2017/02/08/code-dependent-pros-and-cons-of-the-algorithm-age/

Ridley, M., & Pawlick-Potts, D. (2021). Algorithmic literacy and the role for libraries. Information Technology and Libraries, 40(2). https://doi.org/10.6017/ital.v40i2.12963

 Smith, A. (2019, February 3). 7 things we’ve learned about computer algorithms. Pew Research. https://www.pewresearch.org/fact-tank/2019/02/13/7-things-weve-learned-about-computer-algorithms/

Zarouali, B., Boerman, S. C., & de Vreese, C. H. (2021). Is this recommended by an algorithm? The development and validation of the algorithmic media content awareness scale. (AMCA-scale). Telematics and Informatics, 62,  Article 101607. https://doi.org/10.1016/j.tele.2021.101607

Zarouali, B., Helberger, N., & de Vreese, C. H. (2021). Investigating algorithmic misconceptions in a media context: Source of a new digital divide? Media and Communication, 9(4), 134-144. https://doi.org/10.17645/mac.v9i4.4090

Appendix: Recommended Resources for Learning About Algorithms

Video/Audio

Film: Coded Bias https://www.codedbias.com/

Video: The Truth About Algorithms Cathy O’Neil (2:38 mins) https://www.youtube.com/watch?v=heQzqX35c9A

Video: TED Talk Vishal Sikka: The Beauty and Power of Algorithms (2013, 10:50 mins)  https://www.ted.com/talks/vishal_sikka_the_beauty_and_power_of_algorithms

Video: TED Talk (12 Minutes):  Robin Hauser on AI Protections  https://www.ted.com/talks/robin_hauser_can_we_protect_ai_from_our_biases  

Video:  TED Talk (13 mins): Cathy O’Neil on AI   https://www.youtube.com/watch?v=_2u_eHHzRto

Video: Joy Buolamwini

TED Talk  8:00 (2016) https://www.ted.com/talks/joy_buolamwini_how_i_m_fighting_bias_in_algorithms?language=en

Gender shades project (2018) (5:00) https://www.youtube.com/watch?v=TWWsW1w-BVo

Video: Ruha Benjamin: VOX episode “Are we automating racism” (20:21) https://www.youtube.com/watch?v=Ok5sKLXqynQ&feature=emb_title

Podcast: Carnegie Mellon University’s Center for Technology and Society: Consequential podcast  https://www.cmu.edu/block-center/news-events/consequential-podcast.html

Podcast: On the Media: “Biased Algorithms, Biased World” with Cathy O’Neil. September 1, 2021 https://www.npr.org/podcasts/452538775/on-the-media

Books

Benjamin, Ruha. (2019). Race After Technology: Abolitionist Tools for the New Jim Code.

Besteman, Catherine & Gusterson, Hugh. (2019) Life by Algorithms: How Roboprocesses Are Remaking Our World.

Eubanks, Virginia (2019). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor.

Fry, Hannah. (2019). Hello World: Being Human in the Age of Algorithms.

Giansiracusa, Noah. (2021). How Algorithms Create and Prevent Fake News: Exploring the Impacts of Social Media, Deepfakes, GPT-3, and More.

Noble, Safiya. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism.

O’Neil, Cathy. (2017). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.

Schuilenburg, Marc & Peeters, Rik (2021) The Algorithmic Society: Technology, Power, and Knowledge.

Chapters

Dogruel, L. (2021). What is algorithm literacy? In M. Taddicken & C. Schumann (Eds.), Algorithms and Communication (pp. 67-94). SSOAR Open Access Repository. https://www.ssoar.info/ssoar/handle/document/75898

Articles

Angwin, Julia. (2016, Aug 1). Making Algorithms Accountable.https://www.propublica.org/article/making-algorithms-accountable

Newberry, Christina. (2022, February 28). How the Facebook Algorithm Works in 2022 and How to Make it Work for You. https://blog.hootsuite.com/facebook-algorithm/

Noble, S. (2018, March 26). Google Has a Striking History of Bias Against Black Girls. Time. https://time.com/5209144/google-search-engine-algorithm-bias-racism/

Kearns, Michael & Roth, Aaron (2020, Jan. 13). Ethical Algorithm Design Should Guide Technology Regulation. https://www.brookings.edu/research/ethical-algorithm-design-should-guide-technology-regulation/

Other Sites for Resources and Exploration

The Algorithms & Data Literacy Project: https://algorithmliteracy.org/

A. I. For Anyone. https://www.aiforanyone.org/

Algorithmic Justice League  https://www.ajl.org/

Center for Humane Technology https://www.humanetech.com/

Electronic Privacy Information Center https://epic.org

Future of Privacy Forum  https://fpf.org

Slate resources:
https://slate.com/technology/2016/02/whats-the-deal-with-algorithms.html
https://slate.com/technology/2016/02/algorithms-101-a-cheat-sheet-to-the-terminology-the-ethical-debates-and-more.html

Image Atlas Visuals showing how search engine differ geographically: 
Site: http://www.imageatlas.org/
Article about Image Atlas: https://www.wired.com/story/tool-shows-google-results-vary-world/?utm_source=twitter&utm_medium=social&utm_campaign=onsite-share&utm_brand=wired&utm_social-type=earned