This document contains all the lecture notes from the course Topic Algorithm in the Digital Society and all the reading notes.
It includes all the names of the articles and their authors the articles are the following:
1. Zarouali, B., Boerman, S.C., Voorveld, H.A.M., & Van Noort, G. (2022). The ...
W1: Videos
Video 1: What exactly is an algorithm?
- Algorithm: process/ set of rules to be followed in calculations or other problem-solving
operations, especially by a computer
- Set of instructions that enable a computer to put together different sources of
information and generate results (e.g., recipe to get a specific result)
- To deal with data (data can be anything!)
- Algorithms calculate based on features the sort of things that put
some things at the top of a list, and others at the bottom
- Process: Data goes in > goes through instructions > results come out
Coding vs. Algorithm
- Coding is the language of algorithms
Algorithm vs. humans
- No human error bc the computer goes through the instructions (and that's all they know
how to do)
- The human writing the code can make an error
Benefits of algorithms
- Speeding up decision-making
- Making whole processes efficient
- Maybe spotting things that humans have not spotted
W1: Readings
Defining concepts of the digital society
- Terminology shapes reality: phenomena addressed in research & terminology used for
research
- Terms & concepts: lenses on the complexity of reality that foreground some aspects
while neglecting others. Bear normative assumptions, install specific ways of
understanding new phenomena, create regulatory implications
- More frequently used = phenomena and its specific framing becomes increasingly
more self-evident and ordinary
- Hierarchisation of terms and ideas: choosing certain terms over others, giving
voice to a selection of authors, their respective disciplines and viewpoints
(problems: western perspectives, uneven mix of disciplinary positions, dominant
rep of certain auctorial subjectivities in terms of gender, race, ethnicity)
, - Algorithmic governance: builds on notation that technology allows for a specific
mode of governing society (alternative form of governing/ social order where
algorithms are applied to regulations)
- E.g. increasing amount of algorithmic systems (e.g. automated content
moderation) that increasingly rely on algorithmic governance
- Autonomous systems
- Transparency
- Smart technologies
- Platformization
- Implications: large-scale extraction of data = appropiation of social
resources with the general objective (mostly by Western companies) to
"dispossess"
- Filter bubbles
- Datafication: describes a cultural logic of quantification and monetisation of
human life through the digital information
- Implications for labour & establishment of new markets
- Basic rights of the self, autonomy and privacy are increasingly called into
question
Privacy in question
- Shift the possibilities & boundaries of human perception & action by creating
visbilities and forms of interaction that are no longer defined by physical presence
- E.g. personal pics potentially become accessible for a worldwide audience
- E.g. data is easy and cheap to store, become spermanent in digital records
- Data is argued to be used to identify behavioural patterns (not
same as personal data)
Zarouali, B., Boerman, S.C., Voorveld, H.A.M., & Van Noort, G. (2022). The Algorithmic Persuasion
Framework in online communication: Conceptualization and a future research agenda.
Internet Research
Goals of paper:
1. Define and conceptualize algorithmic persuasion
2. Proposal a fw that integrates different dynamic (e.g., data inout, algorithms, persuasion
attempts)
3. Develop future research agenda based on insights derivd from this fw
Algorithm: set of step-by-step instructions computers are programmed tofollow to accomplish
certain tasks
- Tranforming online platforms into codified environments that expose users to the
content that is likely to be most persuasive to them
,Aims of algorithms
- Persuade
- Increase value and capital
- Nudge behavor
- Change ppl's preferences
Concerns about algorithms
1. Fragmented public sphere
2. (Covrt) Highler likelihood of manipulation (voter/ consumers)
3. Increase in attitudinal polarization
4. More privacy infringements
5. Increase in user surveillance
6. Loss of user autonomy
7. Information asymmetry: imbalance in knwoeldgand decision-making
power favoring data processors over the user
8. Bias (bc coders unconsciously program their biases, e.g., prejudices,
stereotypes)
Algorithmic persuasion framework (APF) (in online communication)
- Algorithms have transformed online environments into persuasive architectures that
influence online choices of media users through unobtrusive and subtle processes
Algorithmic persuasion: "any deliberate attempt by a persuader to influence the beliefs, attitudes
and behaviors of people through online communication that is mediated by algorithms"
- Deliberate attempt: FOCUS ON INTENT OF PERSUADER! attempt is
purposefully initiated by the persuades (attempt to persuade does not have to be
recognized by receiver)
- Persuader: brand/ organization/ person
- Influence: change in people's beliefs, attitudes and behaviors (what ppl think, feel
and do) after expsorue to algorithm-mediated comm (varies from: v. weak - v.
strong; can be concious/ unconscious level; ST/LT; intended/unintended)
- Online communication: transmission of algorithm-mediated comm from senters to
receivers in online environments (always online comm activity driven by
algorithms)
- Algorithm-mediated: online comm must be mediated by algorithms that
automatically decide which content to select and present to which users based on
large corpus of input data
- Dynamic process
- 5 components that are central to
algorithmic persuasion
, (circular;feedbsack loops, all are cause & effect of each other): input, algorithm,
persuasion, attempt, persuasion process, persuasion effects
Component 1: Input: Data
- Involves all data that is ussed in algorithmic persuasion, "customer data"
- Prior to algorithm-mediated comm can be provided to online users, data has to be
collected and readied for the algorithm
- First-party data: data owned/ collected by the sender (soruce of persuasion
attempt), you collected it, you own it
- Collected through: cookies, dta about online purchases, data
entered when receiver becomes a member of/ donator to a pol
party, data disclosed during registation of a device
- Second-party data: data that can be used for algorithmic persuasion bc
they are owned (BOUGHT) by a collaborative party
- E..g, buy media space in an automated what based on real-time
bidding (buy data from Google)
- Third-party data: data collected by companies that are not directly
involved in the primary process, persuaders purchase data from data
brokers that specilize in collecting and combining data
- Explicit data: (WITTINGLY) data wittingly (with knowledge/ deliberately)
disclosed by users in online environments
- Implicit data: (UNWITTINGLY) compilation of and/or inferences from data
about users colleced w/out their awareness (e.g., surfing history, preferences, IP
address)
Component 2: Algorithm: Techniques, persuader, objectives, biases
- Algorithms: encoded procedures for transforming input dasta into desired outputs, based
on specified calculations. Data is convreed into "algorithm friendly" format
- (Simple) rule-based algorithms: based on a set of rules/steps
- Typically "IF-THEN" statements → if person likes x content then give y
as result
- Pos: quick to formulate & easy to follow bc it reads as plain text)
- Neg: only applicable to condition in which they are formulated, only for
specific platforms bc different platforms have different "if"s
- (Complex) machine learning algorithms: algorithms that "learn" by themselves
(based on statistical models rather than deterministic rules, trianed with large
corpus of data)
- "Trained" and therfreore "learn"to make decisions w/out human oversight
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