A Human Rights approach to machine learning algorithms
Why and where can algorithms produce inequality outcomes?
Why and where can algorithms be gender biased?
How can a human rights-based approach be applied to computer algorithms that engage, reason about, and make decisions on people?
Our methodology incorporates Human rights concepts with a hands-on data science approach.
Designed in collaboration with OHCHR and EPFL, the workshop includes a Human Rights module and a Jupyter notebook field with code that connects how human rights interplay with decisions made at various points of the data and model lifecycle. This workshop is aimed at computer and data science students.
- Explain a human rights-based approach to AI.
- Identify relevance of different biases and importance of intersectionality, gender equality and bias to computer science and engineering / institutional objectives.
- Apply how and when to use use tools and techniques to mitigate bias in AI.
- Evaluate methods to integrate non-discrimination into design, planning and implementation of AI projects.
The workshop consists of 2 parts:
I. Human Rights Module , and an applied research conversation,
II. applied coding toolbox.
- Human Rights Module
- Introducing basic human rights concepts and a human rights based approach to machine learning.
- Applied Research
- Research Representatives (PhD students, postdoc, faculty) present their research and current work on how human rights fit with AI
- Practical Toolbox
- Step-by-step case study, to see how to apply a human rights based approach in practice (debiasing data and algorithms)
Stand-alone Jupyter notebook
Experiment with data to see how different mathematical and data concepts of fairness interrelate. Begin with a critical analysis checklist of the data process and apply some of the concepts and debiasing literature to hands-on exercise.
- Introduction to fairness
What is fairness? Fair to whom? Mathematical definitions of fairness and their limitations.
- Build a Baseline model
Why was the dataset created? Who created it? Who is in the data and who isn't?
Build a simple model to see how it performs with different fairness metrics.
- Pre-processing (Data)
Where can we find bias in the data? What types of data biases exist? What can we mitigate them?
- In-processing (Model)
How bias can be introduced in the design decisions made when creating the algorithm?
- Post-processing (Predictions)
When we use the predictions, what assumptions are we making?
Human Rights module
Human rights and their principles. Equality and non-discrimination. A human rights-based approach. Legal resources.
A Jupyter notebook with code and exercises to apply in practice the concepts learned
The social impact of development choices: Tinder usecase
The impact of inequalities produced by development choices through a practical case study; the dating app Tinder
Dictionary for the terms.
An interdisciplinary community with in conversation with different sections, disciplines, and universities
- Open to all disciplines
From computer and data scientists, to humanities, social scientists, law students
- Open to all regions
Engage and participate in discussion with students from different regions and universities
- Open to all levels
Contact us as we build our interdisciplinary community!
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