Human Comprehension of Fairness in Machine Learning
AI & Swarm intelligence is the latest trending technology in many fields especially in industries like Manufacturing, Automation, Control Systems, Healthcare, Energy, Transport, Defence, Space, Data Mining, etc. Artificial Intelligence will be a common platform to gain knowledge and share new ideas amongst the Technologist, Professionals, Industrialists, Researchers, Innovators and students from research area of Machine Learning. Experts will share their research experiences and engage in many interactive discussions at the event.
Bias in machine learning has manifested injustice in several areas, such as medicine, hiring, and criminal justice. In response, computer scientists have developed myriad definitions of fairness to correct this bias in fielded algorithms.
While some definitions are based on established legal and ethical norms, others are largely mathematical. It is unclear whether the general public agrees with these fairness definitions, and perhaps more importantly, whether they understand these definitions. We take initial steps toward bridging this gap between ML researchers and the public, by addressing the question: does a non-technical audience understand a basic definition of ML fairness?
We develop a metric to measure comprehension of one such definition--demographic parity. We validate these metric using online surveys, and study the relationship between comprehension and sentiment, demographics, and the application at hand.
The quest for Artificial Intelligence (AI) begins with dreams – as all quests do. Human-like machines are described in many stories and are pictured in sculptures, paintings, and drawings. Starting from the time of ancient Greek philosopher Aristotle people dreamed of automation. This session of Artificial Intelligence is specially meant for celebrating the vision of the dreamers which includes philosophers, Science Historian, writers, scientists, researchers etc.
A Knowledge-Based System is a computer program which uses Artificial Intelligence to solve problems within a specialize sphere that involves human expertise. Knowledge-based systems were first developed by Artificial Intelligence researchers. These initial knowledge-based systems were primarily expert systems. The most recent progress of knowledge-based systems has been to adopt the technologies for the development of systems that use the internet. This session will help researchers to understand how to use Knowledge-Based System as a diagnostic tool.
The journal invites different types of articles including original research article, review articles, short note communications, case reports, Editorials, letters to the Editors and expert opinions & commentaries from different regions for publication.
A standard editorial manager system is utilized for manuscript submission, review, editorial processing and tracking which can be securely accessed by the authors, reviewers and editors for monitoring and tracking the article processing. Manuscripts can be uploaded online at Editorial Tracking System (https://www.longdom.org/editorial-tracking/publisher.php) or forwarded to the Editorial Office at https://www.longdom.org/eye-diseases-and-disorders.html
The Journals includes around 150Abstracts and 100 Keynote speakers have given their valuable words. The meet has provided a great scope for interaction of professionals including in addition to clinical experts and top-level pathologists and scientists from around the globe, on a single platform.
International journal of swarm intelligence and evolutionary computation