11:00 – 16:00
Claire Glanois, Vytas Jankauskas
Open Hub @Chronus Art Center (BL No.18, No.50 Mo Gan Shan Rd, Shanghai)
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The data we produce online is often distributed between various parties interested in knowing more about us. New insights are generated as machine learning algorithms help speculate about users' personalities, physical or psychological states, aspirations, among others. This information helps entities such as advertisers or corporations target individuals or groups with tailored products and services.
CAC Atelier ‘Shoppers Exposed’ will look into artificial intelligence as means to profile us based on individual consumer patterns. By exploring online purchase histories, identifying consumer types, we will discuss the human biases underlying algorithmic classification. We will play around and collectively re-appropriate customer taxonomies in an attempt to transgress normative labels. Based on the wordplay and semantics present in the product’s title, a new machine learning algorithm will be trained. The scope of the activity will therefore be to creatively grasp on how machine learning and predictive analytics work, rather than technocratically reverse-engineering their frameworks.
Drawing upon the semantic poetry of the product titles generated by our algorithm, we will try to imagine the next ‘perfect product’ in a world of overabundant commodities. What might our previously labeled ‘sociopath gamer’, ‘impulsive entrepreneur’, or ‘confused artist’ buy next? We will use collage and rapid prototyping to embody new-even potentially absurd, useless or extravagant-hybrids of devices or services.
Multi-function Emotional Shake Machine, final product sample ©️ Courtesy the artist
We will discuss how easy, hard, objective, biased, or ethically-challenging it may be to deploy algorithmic assessment on our digital profiles. We will reflect on the data we produce, the impact as well as the limits of statistical stereotypes attributed to our consumption. As part of the process we will learn practical skills in machine learning, data labelling, and creative prototyping.
11:00 - 11:30 What Can We Learn from our Purchase History?
Examples of implementation, their critique, and related artistic projects.
11:30 - 12:30 Shoppers Exposed Pt.I
Creating a labelled dataset: Imagining new consumer types based on our digital purchase history.
Techniques used in consumer quantification: machine learning, clustering, similarity metrics.
12:30 - 13:30 Lunch
13:30 - 14:30 Shoppers Exposed Pt.II
14:30 - 16:00 ‘You Might Want to Buy This Next’
1. Ideally please bring your laptop
2. Please bring a spreadsheet file (.csv/.xls/.xlss) with a selection of 10 previous TaoBao purchases (Title, Price, split in separate columns)
3. No previous coding experience necessary
About the Facilitators
Claire Glanois is an abstract mathematician whose work is situated at the crossroad of number theory, algebraic geometry and quantum field theory. Aside, she regularly engages in civic and non-profit projects, notably to open-up spaces for algorithmic diversity and transparency.
Vytas Jankauskas is an artist, designer, and researcher interested in how emerging technologies inhabit and shape our domestic mundane. His practice encompasses the Internet of Things, data ownership, digital citizenship, social media and their influence on our everyday. He has recently joined CAC as the new Head of Research/Creation at CAC_LAB.
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