Digital Society

Description of each author and their core arguments/ statements

Housley

Week 1 for Emerging contours of digital society: Talks about how digital society should not be a seperate digital sphere of life. Instead it is deeply intertwined with social organization, relationships and transformation. He also talks about the 4 Analytic frames for studying a digital society:

1) Interaction order: How everyday interaction is changing (ex mediation of interaction through tiktok)

2) Scientific & Social controversies : how it gives researches the ability to study controversy but also how the tech themselves can be the source of controversy
3) Power & social transformations: How it creates new forms of power, exploitation and inequality

4) Ideology / discourse / world making: How tech actually changes our vision of the future and our goals

Davis & Chouinard

Week 1 “Theorizing affordances: From request to refuse”
They talk about the role of affordances(= a possibility for an action provided by environment or object) in technology. They talk how affordances are relational for example a chair affords sitting but only to humans, not animals. Tech is less binary however so there are 6 affordance mechanisms.
1) Request: Tech asks or invites you to perform an action
2) Demand: Tech requires an action (ex you must give email when signing up)
3) Allow: Tech makes an action possible (ex messanger allows you to send messages)

4) Encourage: Tech makes an action more attractive (ex posting when likes are availible)

5) Discourage: Tech makes an action less attractive (ex hard to find content or warnings)

6) Refuse: Tech prevents action

Verbeek

Week 1 “Materializing Morality”
Verbeek talks about how technology is not neutral and how it actively mediates our interaction with the world (how we see the world and how we behave) and influences what we are likely to do.
Morality can be built into the technology itself. Not only humans or institutions can influence behaviour but the design of objects can also encourage or discourage behaviour.
Ex: A speed bump doesnt tell you to drive safe, its design physically encourages slow driving

or

Ex: Notifications doesn’t force you to the app but it encourages and influences their behivour to open the app again.

Swierstra & Waelbers

Week 2: “Technology shapes morality”
Talks about how tech does not merely help us achieve our goals but also change our goals, our values, habits and understanding of what we think is desirable

Tech mediation 3 questions:

1.What is the case? (What do we believe about the technology)
2.What can we do? (What actions do we believe the technology allows us to do)

3.What ough we to do? (What actions do we think is right)

Ex AI

1. users believe AI is a digital service with no physical resources required

2. Users can generate thousands of images

3. User concludes that generating content is acceptable as no harm done

If user learns about true consequences, his morality might change but tech is the same

Unintended consequences: Outcomes can contradict intended purposes.

Ex. Energy efficient lighting means less energy is used but people just use more lighting so it offsets the savings.

Also talks about the “matrix of consideration” of the moral implications of technologies with each entity: Stakeholders, Consequences and “The good life” (what kind of life we think is desirable”) and their perspectives on the 3 tech mediation questions.

Bresnihan & Brodie

Week 2
Argues how “green” technology can still be extractive i.e how expansion of digital infrastructure and green industrial projects can reproduce extractive relationship with nature under the language of sustainability.

The article talks about Ireland as its example and how back in the day peat was extracted which damaged the landscape to produce energy
Now: We build digital infrastructure which consumes energy and resources to generate economic growth.
Even though some aspects may be greener the question still is “how can we use this landscape to generate economic value” under the language of sustainability by using words like “green” or “renewable” etc but dont talk about if the WHOLE system is sustainable

Cubitt

Week 2 “finite media”
Cubitt talks about how media is more than just information but the physical resources behind it. Uses the example of the ned kelly film and how it degrades over time and the resources it consumed.

He says that people associate digital technology with “limitless growth, information and storage” but the physical world it operates on is finite.
He says to stop treating digital technology as immaterial and start treating it as an ecological/material system

The entire lifecycle of media is not just the resources it consumes but also the extraction → production → energy use → storage/use → disposable waste and e-waste

This is where he also distinguishes digital waste vs e-waste how e-waste is the waste of actual physical technology like phones, laptops, chargers etc.

and digital waste is the waste of data, media and digital content that are produced, stored and duplicated which eventually become useless or obsolete

Langdon Winner

Week 3: Do artificats have politics?
His main argument is that technology is not strictly neutral and that their design and use can embody certain forms of power and authority

We should ask more than if the tech is efficient or does what its designed to do but also questions like: Who is the tech excluding? Who can make decisions? or who benefits from this?

2 concepts in the article:
1) Artifacts can have politcal properties: with 2 ways it can have. One being the design can benefit certain groups (example the new york low bridge where buses are restricted, or complex technological appeals to content moderation)
Another is that the technology requires or strongly favours certain power or authority measures. Think of how nuclear weapons demand authority.

2) Politcal consequences in the design: How technology is not a neutral tool but it can shape society through design. Example is the recommendation algorithm on TikTok. The question here is how does Tiktok’s design distribute power and opportunities among its users

Birch

Week 3: “Do artifacts have politcal economy?”

Very similar to winners arguments but focusses on political economy (Meaning how technology can embody or support certain economic systems, labour, profit and distribution of resources) instead of political power as a whole.

Instagram as an example: Winner: How does Instagram's design distribute political power and influence who gets visibility or authority?

Birch: How do Instagram's business model influence how the platform is designed and operated? Who benefits financially, and who bears the costs?

Birch wants us to look at who is affected, who owns the technology who makes profit etc.

He also says that technology and economic systems influence and shape each other meaning that technology can favour certain economic systems and the economic systems also influence what and how technologies are designed and used. He defines this as “Constructivist Political Economy” meaning that they are not seperate things that independently influence each other but instead social relationships and choices build them together

Example: AI
A company develops an AI but the stakeholders and developers who make a profit influence the design and usability of the technology. But when the AI gets adopted it can change the hiring practices, how companies organize and how value is created which in turn influences the economic system

example constructivist political economy: Think of a HR department implementing an algorithm to evaluate workers. The company influenced the creation of the algorithm but once implemented it might affect the companies hiring strategy

Bonini & Treré

Week 3: “Algoritms of resistance: the fight against platform power”
Bonini and Trere main argument is the relationship between platform power and people’s ability to resist the power.

Platforms can be designed to distribute work, visibility etc and some people might think their all powerfull, howeber bonini and trere don’t agree. They say there are a few ways that people fight against platform power.
1) Algorithmic agency: People able to influence the algorithm outcome through their actions.
Think of a delivery driver as an example. If they work at certain times or accept certain orders it might bring them closer to their desired outcome. They can behave “strategically”

2) Algorithmic resistance: A more aggresive fight against the algorithm. This concept is about people actively challenging or working against the algorithm. People can do this by example manipulating or circumventing the algorithm. For example a group of creators finds ways around the algorithm to post content that the algorithm is trying to supress.

3) Platform “Moral Economy”: The values, expectations and ideas that shape the economic relationship between platform and users. For example a delivery platform creates their platform that wants workers to accept jobs as fast as possible and deliver food as fast. So the platform might reward high acceptance. However the drivers would think its fair if they dont get penalized for breaks or be able to reject a non-profitable delivery for them. This “Moral economy” perspective shows WHY economic relationships strain not just how.

4) Contested platforms: Lastly they argue that platforms are constantly contesting over the two extremes: Platforms have complete control vs users have complete control. So its constantly a struggle between platform power and user power.

“Platforms have power, but that power is never absolute because users can adapt to, negotiate with, and resist the systems that platforms create.”

McMillan Cottom

Week 3: Platform capitalism with racial capitalism

She says that platforms are often seen to connect people and create opportunity. But he adds that these 'benefits’ are not distributed equally.

Two concepts: Platform and racial capitalism

Platform capitalism: How platforms organize interaction and create profit

Racial capitalism: talks about how racism and capitalism are interconnected and that racism shouldnt be seen as seperate to capitalism. This means questions like who has access and who benefits from these digital technologies to if platforms reproduce existing racism.

For example, a gig-work platform might offer flexible employment opportunities to everyone. However, workers who have fewer alternatives may become more dependent on insecure work and have less bargaining power.

Another important concept: Predatory inclusion

which means when marginalized groups (or minorities) are given access to opportunities but only on terms that allow others to exploit them.
ex. think of online universities: They say they give more reach and access to people who historically have faced barriers, however they mostly would need significant loans without actually giving them the benefit they were expecting.

This article challenges the assumption that greater access means better equality. Take for example a gig work company which allows people to actually work outside the traditional employer route, however they often get taken advantage of with for example harsher work conditons, no insurance and unpredicatable earnings

Griffin, Green & Welie

Week 4: Excuses, excuses
They talk about the moral responsibilities of developers in AI and how the systems they design is their responsibility and can’t just use the phrase “I was just doing my job” if something goes wrong.

Main concepts are:
1) Moral agency: Meaning the ability to make moral decisions and take responsibility for those decisions. They argue that developers must see that their technical decisions can have ethical consequences.

2) Problem of many hands: Meaning that if many people work on a single system that has a harmful outcome it can be hard to say whos responsible for the system. Everyone can just blame someone else leading to noone to blame. They say there are 4 ways to avoiding responsiblity:
a) Intentionality: “It wasn’t my intention”
b) Ignorance: “I had no idea this could happen”
c) Compulsion: “I was just following orders.”
d) Inevitability: "There was no way of avoiding the harm.”

They also draw on MacIntyre’s philsophy and 3 conditions for moral agency:

1) A moral identity: as in you see yourself beyond your job with ethical responsibilities and morals

2) Community: as in a community where you can discuss and critique ethical questions

3) A supportive environment: where you can act ethically

McDermott, Folds & Hallo

Week 4: “Addressing cognitive bias in systems engineering teams”
They argue that developers are not objective even if they use scientific and data driven methods. They argue that under this there is a cognitive bias that can lead to poor decision making.

Main concepts:

Cognitive bias: A tendency in human thinking that distorts our judgement or how information is interpreted.

Confirmation bias: A tendency to favour information that supports our existing belief while supressing contradictory evidence. In engineering example: An engineer runs tests on their AI system that shows it works, while overlooking evidence that it fails in certain situations.

Anchoring bias: Relying too heavily on the first piece of information encountered.

Groupthink: team members avoid disagreement to maintain consensus

Systems thinking: Looking at the entire system, including relationship between components and people. instead of examining each part in isolation.

Sunk cost bias: The bias to keep investing in a failing project because of the time and money already invested in it

Safiya Umoja Noble

Week 5: Algorithms of oppression

Noble argues that for example a search engine like google can reinforce sexist or racist stereotypes by presenting discriminated representation of groups as legitimate.

Example searching “black women” on google might give stereotypical or sexualized results.

Important concepts:
Algorithmic bias: Algorithms can emit bias and discriminatory results because of data, assumptions and/or commercial interests behind it.

Racism in search engines: Search engines can reinforce racist stereotypes instead of showing the objective reality

Commercial interests: Search engines prioritize profit from ads, not always accuracy or fairness

Abeba Birhane

Week 5: Algorithmic injustice

Birhane argues that algorithmic injustice cant just be solved with unbiased datasets, instead we must look at who is harmed by this algorithm and the purpose of the system.

Example: An AI recruitment system discriminates against women. Instead of merely adjusting the algorithm to improve its fairness score, we should also examine discriminatory hiring practices and involve the people affected in deciding how recruitment should work.

Main concepts:
Algorithmic injustice: Algorithms can automate and reinforce existing social inequalities.

Relational ethics: Ethics must consider relationships, history, social context, and power—not just technical rules.

Marginalised communities: The people most affected by a system should be central to decisions about its design and use.