K2SP 3500 - Decoding biases in Artificial Intelligence

The course invites students to explore the issues of discrimination in AI. The deluge of public data and the advent of deep learning techniques in the last two decades have generated a lot of hope for designing more personalized and balanced public policies. However, using machine learning algorithms to make public decisions about every citizen has not come without generating fierce criticisms from activists and citizens concerned by the way they are being computed.  In this class, we invite students to get their hands on data and code and investigate on how and why public data and state-of-the-art algorithms, far from being neutral and objective, may inherently produce discrimination toward specific populations. Conversely, we will also use state-of-the-art machine learning models to audit how they may embed systematic racial or gender biases, while still considering how they fit a larger socio-technical context. These questions are particularly pertinent and increasingly complex in the current context of the advent of generative AI models.  The objectives of the class are threefold: (i) discuss the ongoing debates on algorithmic fairness and its application to data-driven policy, (ii) learn and practice large data collection, manipulation, and main families of machine learning algorithms, (iii) create your own research design to investigate some original dataset or existing algorithm and put it to the test in a collective empirical project.

Célia NOURI,Jean-Philippe COINTET

Cours magistral seul

English

- In Class Presence: 2 hours a week / 24 hours a semester - Online learning activities: 20 minutes a week / 4 hours a semester - Reading and Preparation for Class: 45 minutes a week / 18 hours a semester - Research and Preparation for Group Work: 2 hours a week / 24 hours a semester - Research and Writing for Individual Assessment: 20 minutes a week / 4 hours per semester

Empirical explorations of real-world cases are central in this class. For these hands-on sessions, basic Python coding skills are required: defining a function, importing an external library (we will use pandas extensively), list and dictionary manipulation.

Spring 2024-2025

There will be two main assessments during the semester. The most important one (2/3 of the grade) is a collective project for which students are required to identify/generate a dataset online, design an experimental plan to analyze its inherent biases, and finally visualize and reflect upon the systematic discriminations embedded in the dataset. The final delivery will take the form of a website. An individual take-home paper will also be graded. Active participation during the class can also be rewarded with an extra point.

The pedagogical format is strongly oriented toward a workshop-style class. Typically, the class will start with a discussion of the reading, followed by a short lecture on the concepts of the session before the class turns into applied mode, wherein students will practice data coding by themselves.