Award-winning AI firm, Synthesized, has welcomed the adoption of the first ever global agreement on the ethics of artificial intelligence as a “step in the right direction”.
Synthesized, the leading all-in-one DataOps platform, is an AI and machine learning-based business which is keen to promote ethical and responsible use of AI across the industry.
Nicolai Baldin, CEO of Synthesized said: “We strongly welcome UNESCO’s global standard, and we hope that it serves as a turning point in the future of ethical and responsible use, and development, of artificial intelligence.
“AI is prevalent in our everyday lives - everything from social media, to mobile phones, from driverless cars to healthcare robots. We need to ensure that society trusts and understands the benefits of responsible AI. This global agreement is a step in the right direction.
“Industry needs to heed and comply with the values and principles of UNESCO’s ethical framework, and to start working to do so immediately. We believe there should be a code of conduct established which would give a “green tick” to those companies who comply in developing ethical and responsible AI in a transparent manner. This would create much-needed trust and a better future for society.”
UNESCO’s standard, adopted on Thursday (November 25), recognises the advantages AI brings to society but calls for action to ensure the protection of data, a ban on social scoring and mass surveillance, protection of the environment and monitoring and evaluation methodologies.
Synthesized has developed a robust, safe to use tool for organisations to generate realistic synthetic data in a matter of minutes. It removes all personal attributes and the risk of data breaches, reputational risks, or risk of fines for non-compliance. It can be used to detect and mitigate inequalities and bias in data, AI and machine learning training and software development.
Earlier this year, Synthesized made public and freely available its FairLens tool - the world’s first data-centric open-source software for identifying and measuring data bias.
