DeepAI AI Chat
Log In Sign Up

Towards Fairness Certification in Artificial Intelligence

by   Tatiana Tommasi, et al.

Thanks to the great progress of machine learning in the last years, several Artificial Intelligence (AI) techniques have been increasingly moving from the controlled research laboratory settings to our everyday life. AI is clearly supportive in many decision-making scenarios, but when it comes to sensitive areas such as health care, hiring policies, education, banking or justice, with major impact on individuals and society, it becomes crucial to establish guidelines on how to design, develop, deploy and monitor this technology. Indeed the decision rules elaborated by machine learning models are data-driven and there are multiple ways in which discriminatory biases can seep into data. Algorithms trained on those data incur the risk of amplifying prejudices and societal stereotypes by over associating protected attributes such as gender, ethnicity or disabilities with the prediction task. Starting from the extensive experience of the National Metrology Institute on measurement standards and certification roadmaps, and of Politecnico di Torino on machine learning as well as methods for domain bias evaluation and mastering, we propose a first joint effort to define the operational steps needed for AI fairness certification. Specifically we will overview the criteria that should be met by an AI system before coming into official service and the conformity assessment procedures useful to monitor its functioning for fair decisions.


page 1

page 2

page 3


Investigating underdiagnosis of AI algorithms in the presence of multiple sources of dataset bias

Deep learning models have shown great potential for image-based diagnosi...

Next Wave Artificial Intelligence: Robust, Explainable, Adaptable, Ethical, and Accountable

The history of AI has included several "waves" of ideas. The first wave,...

Generalizing in the Real World with Representation Learning

Machine learning (ML) formalizes the problem of getting computers to lea...

FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment

With the aim of studying how current multimodal AI algorithms based on h...

Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview

Quantifying and managing uncertainties that occur when data-driven model...