Fairly AI — What is AI Bias And Why AI Fairness Matters
Artificial intelligence is implemented nearly everywhere, and machine learning algorithms pretty much run the world we live in today. From banks and universities to criminal justice systems, algorithms follow a very narrow set of rules to make serious decisions that impact almost every aspect of our lives.
As we are approaching the Fourth Industry Revolution, a new chapter in human development driven by technology, the responsible use of AI is becoming increasingly important, and AI fairness should not be a question going forward.
AI is not infallible
From medicine to disaster-prevention, AI can be incredibly valuable in many fields. To create a smarter and more automated future, companies and organizations deploy Deep Learning models. These activities consist of algorithms that analyze and identify patterns in massive amounts of data.
Today, Machine-Learning algorithms are responsible for the vast majority of the AI application. Unfortunately, since algorithms learn from previous data inputs, AI is inclined to develop a bias and sets itself off to become unfair to certain groups.
If a trained data set lacks diversified examples or a substantial number of statistics among protected groups, then the data output will not be accurate. In some cases, historical data sets from previous inputs may hold racial inequality; in other cases, some data inputs created by engineers may include unconscious bias. The algorithm can also influence the data that it gets and amplifies what happened in the past with a positive feedback loop. Consequently, transparency in algorithms is vital to help companies identify factors that cause any prejudice.
Advocating for humanity in the age of intelligent machines
Fairly AI a Toronto based startup, is on the mission to help businesses and organizations analyze and build more ethical and responsible AI with unbiased algorithms. Failry AI has developed an easy-to-use assessment tool that’s compatible with any existing AI solutions regardless whether they were developed in-house or with third party systems.
“I understand the capabilities of deep learning deployed on massive datasets and clearly see two futures in which it is either (i) used to reinforce existing structural discrimination or (ii) massively reduce it.” — David Van Bruwaene, Co-founder at Fairly.AI
A call for checks and balances
AI fairness is not a new issue, and researchers have been studying the impact of AI bias for years. Since the number of AI applications increases, a need to audit high-stakes AI decisions becomes inevitably essential. Additionally, as the current standard for evaluating AI is insufficient, AI applications should be examined for bias by a third party. The graph below shows that companies cannot govern themselves, and without question, a set of regulations needs to be in place.
“Our view is that AI fairness is going to be a leading issue in tech, just like how data privacy and data security were in the last decade.” — Fion Lee-Madan, Co-founder AT Fairly.ai
Interested in joining Fairly AI on their mission?
Fairly AI is currently looking for companies with strong corporate social responsibility values to help them reduce biases in their AI applications and find new opportunities in the current climate where AI fairness is becoming a leading issue around the world. If you are using an AI solution and are interested in continuing leveraging AI responsibly, contact Fion at fion@fairly.ai to find out how to get started.
Originally published at http://byvi.co on July 27, 2020.
