DeepAI AI Chat
Log In Sign Up

Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems

by   Avinash Agarwal, et al.

Decisions made by various Artificial Intelligence (AI) systems greatly influence our day-to-day lives. With the increasing use of AI systems, it becomes crucial to know that they are fair, identify the underlying biases in their decision-making, and create a standardized framework to ascertain their fairness. In this paper, we propose a novel Fairness Score to measure the fairness of a data-driven AI system and a Standard Operating Procedure (SOP) for issuing Fairness Certification for such systems. Fairness Score and audit process standardization will ensure quality, reduce ambiguity, enable comparison and improve the trustworthiness of the AI systems. It will also provide a framework to operationalise the concept of fairness and facilitate the commercial deployment of such systems. Furthermore, a Fairness Certificate issued by a designated third-party auditing agency following the standardized process would boost the conviction of the organizations in the AI systems that they intend to deploy. The Bias Index proposed in this paper also reveals comparative bias amongst the various protected attributes within the dataset. To substantiate the proposed framework, we iteratively train a model on biased and unbiased data using multiple datasets and check that the Fairness Score and the proposed process correctly identify the biases and judge the fairness.


page 1

page 2

page 3

page 4


Towards Fairness Certification in Artificial Intelligence

Thanks to the great progress of machine learning in the last years, seve...

Algorithmic Fairness

An increasing number of decisions regarding the daily lives of human bei...

Speciesist bias in AI – How AI applications perpetuate discrimination and unfair outcomes against animals

Massive efforts are made to reduce biases in both data and algorithms in...

Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models

Prioritizing fairness is of central importance in artificial intelligenc...

Generalizing Fairness: Discovery and Mitigation of Unknown Sensitive Attributes

Ensuring trusted artificial intelligence (AI) in the real world is an cr...

Joint Optimization of AI Fairness and Utility: A Human-Centered Approach

Today, AI is increasingly being used in many high-stakes decision-making...

Conceptualization and Framework of Hybrid Intelligence Systems

As artificial intelligence (AI) systems are getting ubiquitous within ou...