This experience is a reaction to the swift technological advancements that encircle us and significantly influence our day-to-day existence.
The objective is to raise the awareness of Earth citizens regarding the assemblages of code and data that they are encountering and engaging with daily.
We believe that transparency has a crucial part to play in envisioning the relationship between humans and non-humans.
In fact, making technology more accessible to everyone necessitates acquainting ourselves with current customs and goals before we can reconsider and establish ownership.
We believe that memorable and inspiring learning experiences are derived from being actively engaged in co-creative spaces.
Our experience is designed with the wish of unboxing the black box of recommendation algorithms, and makes us reflect about the future of the traces we leave online.
More informations here about the extraction, profiling and recommendation processes, and here about our specific prototype or the custom experience.
*is the shaded path of your online traces
Data extraction is a pervasive practice niftly woven within our digital spaces.
Vast amounts of data are relentlessly collected from individuals without explicit consent or poor explaination, raising concerns about data ownership, surveillance and the becoming of these online traces.
A certain culture of obscurity common among the technosphere makes it difficult for individuals to access or understand their data, or grasp the possible impacts of extractivism.
A certain portion of Machine Learning algorithms are utilized for modeling human beings in order to predict various aspects of their lives such as infrastructure use, energy consumption patterns, political views, movie choices, and consumer behavior.
The latter is known as Consumer Predictive Analysis (or Algorithmic Consumer Culture), which has in the last years reshaped the marketing industry, boosted by Big Data and smart businesses.
The data we produce online --or trade for services-- is commonly distributed among various entities who are interested in gaining more 'insights' into our lives, and extends way beyond our Google Searches or our purchase history.
New 'insights' are then generated as machine learning algorithms help speculate about our personalities, physical or psychological states, desires, beliefs, values, or intentions, among others.
To which extent can we reconstruct a person --their digital double-- from these online traces?
Beyond wondering how accurare, these practices are disquietening. Quoting the art collective Cached:
"Psychographic algorithms often resemble a hybrid of digital psychologists and fortune-tellers. The interpretation of this sort might not be as accurate as direct data on clicks, GPS points or biometrics. Nevertheless, the very idea that we are perceived as social beings rather than quantitative datasets raises the data privacy discourse to a new level."
These data points help entities such as advertisers or corporations target individuals or groups with tailored products or services.
These recommendations services have infiltrated a consequent part of our lives, from what we listen to, what we watch, where we go, what information are we exposed to --or what information are we *not* exposed to--, what we buy, etc.
Although helping us navigate the vast amount of information available online, these recommendation systems are often opaque, biased, and normative, and built at the expense of users' privacy, control abd agency.
Based on problematic metrics (time spent on the platform, money spent, etc.), current profit-driven recommendation systems have been criticized for perpetuating filter bubbles, reinforcing existing biases, creating addiction, and pushing users towards consuming more content without regard for its quality or relevance.
Current recommender systems may control us more than we control them.
We should not underestimate to which extent these predictive algorithms are altering the behaviors they are speculating about, in an insidious feedback loop.
To address these issues, new ways of recommendation could be implemented that prioritize transparency, diversity, and user control.
First, recommendations could be accompanied by clear explanations of how they were generated, and on which criteria they were grounded.
Recommendations could be more diversified to expose users to a broader range of content and perspectives, and be aware of their echo chambers when needed.
Current recommender systems are performing more poorly when targetting certain more radical communities, cultural minorities, or marginalized groups, and are often not designed to account for the diversity of users' interests and preferences, which leads to stereotypical judgements.
Another approach to improving recommendation systems is to prioritize user needs and preferences over profits.
This could involve using alternative revenue models such as subscription-based services, where the focus is on delivering high-quality content that users are willing to pay for, rather than maximizing ad revenue.
Additionally, platforms could be designed to emphasize community building and collaboration, with recommendations based on social connections and shared interests, rather than individualized consumption patterns.
Users should be given more control over when and which recommendations they receive, allowing them to customize their experience and avoid being spoon-fed content they find objectionable, dull, or addictive.
Regaining control over our recommenders also brings up the question of where should they start, and where should they end.
What if various grassroot or situated recommenders could emerge, tailored to various needs, ends, communities, and inclinations?
What if recommenders could be grounded upon values of inclusivity, care, non-binarity, vulnerability, symbiosis, resilience, situatedness, ecological porosity, softness, and diversity?
The experience offers a novel approach to teaching algorithms by means of experiential and active learning.
Rather than solely presenting theoretical concepts, the experience allows students to engage interactively and see the outcomes of applying various algorithms in a transparent manner.
By coupling physical and active experiences with abstract concepts, teachers may provide opportunities for students to gain a deeper understanding of algorithmic processes and their real-world implications.
This approach to learning may enhance cognitive development and executive function through promoting problem-solving skills, critical thinking, and a deeper comprehension of algorithmic principles.
Discussion prompts:
* What are the benefits and drawbacks of data collection and storage for individuals and society as a whole?
* How can companies and governments ensure that they are collecting and storing personal data in an ethical and secure manner?
* What are algorithms, and how do they work? How are they used in daily life?
* How do algorithms and recommendation systems influence the content and information we see online? Do they limit our access to diverse perspectives and viewpoints?
* How can we ensure that algorithms and recommendation systems are not perpetuating biases and discrimination?
* What are the challenges and opportunities of the growing field of big data analytics?
* How can we ensure that artificial intelligence (AI) and machine learning technologies are being used for the greater good and not causing harm?
* What are some alternative ways to design recommendation systems that would ensure that users are exposed to a wider variety of content and perspectives?
* How can recommendation systems be designed to promote serendipity and discovery, rather than just reinforcing what a user already knows or likes?
* How can we build recommendation systems that are transparent and explainable, so that users can understand why they are being recommended certain content or products?
* What role should users play in shaping the recommendations they receive, and how can we empower them to do so?
* How can we balance the need for personalization with the need for diversity in recommendation systems?
* How can we design recommendation systems that take into account the broader context and purpose of the user's interaction with the system?
* How can we evaluate the effectiveness of alternative recommendation systems, and what metrics should we use to do so?
We take data privacy and security seriously and adhere to the General Data Protection Regulation (GDPR) guidelines to ensure that the personal data you provide us with, is processed securely and ethically.
We are committed to transparency and will always be clear about the nature, scope, context, and purposes of the data processing activities.
We delete your data after your experience ends and keep records of the processing activities for future reference.
We do not share personal data with other parties. By following these guidelines, we can ensure that we are protecting personal data and complying with GDPR regulations.
This project has been made by Simone and Claire Aoi. We would love to hear from you, thoughts, critic, prompt, comment, gestures, ideas, collaborations. A Feel free to reach us at "tropozone [[[[[[[at]]]] protonmail.com"