Poor AI decision making and weak human processes involved within the legal setting can have devastating consequences to individuals, families and wider society. It's critical we come to the correct conclusion but what risks does biased AI posed in this process?
Human Resource Management (HRM) is incredibly complex and challenging for humans, so as organisations implement Artificial Intelligence to better manage people what are the risks and how might biases result in undesirable outcomes?
Removing bias from AI in the banking sector isn’t just the right thing to do from an ethical standpoint, it is critical to unlocking the full potential of economies across the world.
Insurance is an essential service that’s mandatory in many instances, so whilst the industry has to discriminate in pricing risk it should be free from bias - which is difficult when the data used is biased.
AI algorithms are flawed by biases caused by the real data they are trained on. The difference with healthcare is the actual consequences that these biases bear.
In April 2021, the European Commission released the proposed Regulation on Artificial Intelligence, most known as the ‘AI Act’.
The Montreal AI Ethics Institute interviewed our CTO Nathan on the synthetic data market and data biases.
Bloomberg interviewed our CEO on Fairgen's use of synthetic data.
I24 TV interviewed our CEO Samuel Cohen on the dangers of algorithmic biases.
Vivatech, Europe's biggest tech conference, invited Nathan (CTO) to speak about how Fairgen enhances diversity & inclusion through technology.
Calcalist, an Israeli media, interviewed the Fairgen founders on their vision, product and market.
Creator Fund explain what is the potential they see in Fairgen's team, product and ethical AI.
We just completed our Seed Round. It will accelerate the development of our data debiasing platform.
Vary your model's decision threshold for different subgroups to achieve fair outcomes while reducing accuracy minimally.
Removing a sensitive attribute is not enough to make it disappear as it can hide in proxy variables.
Read about the different notions of fairness available to stakeholders.
Read about the origin of biases in data and why you should care about them.