Combatting Biased AI using Blockchain technology – Unbiased by Sukesh Kumar Tedla (unbiased)

The Speaker is Sukesh Kumar Tedla (CEO and Founder of Unbiased, Sweden) Description of Presentation: One of the important things in the AI industry is the growing importance of data, whether it is the mobility or banking or finance or customer-service sector. Data is essential to make algorithms more efficient and intelligent. There are new methodologies surfacing in the market but the data still remains the essential part when building a consumer-ready algorithm. The quality of an algorithm depends on the data it is trained upon. If your data is biased towards a certain entity, groups, religions, races, and other such socio-economical factors, then the algorithm comes with inherent bias. Though there are many ways bias can impact an algorithm the bias in data is a fundamental issue. This is one of the biggest challenges in the AI industry today.

As the digital connectivity foot-print increases the value for data increases as well. We are moving in the direction of a data-sharing economy where data will be traded in exchange for financial value. Often times people do not know the implications of giving consent on various applications in relation to their data. This is one of the reasons we need better consent management systems and Blockchain technology seems to be the answer. Blockchains offers transparency and establishes trust between different parties, so while sharing data, there is a trust that your data is being used as per your consent. During this talk, I will present why and how blockchain technology can make a difference and also give a demo of the tool we have built at Unbiased.

Presenter BIO: Sukesh Kumar Tedla is the CEO and Founder of Unbiased. He is 25 Years old, a young entrepreneur challenging the implications of technology on the societies at large. In his current role as the CEO, he is driving the innovation of building ethical & transparent AI solutions that help fight complex technical & societal challenges like Bias in AI, ML &

Leave a Reply