How to deal with bias in Artificial Intelligence models?

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗰𝗼𝗺𝗺𝗼𝗻 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗱𝗶𝘀𝘁𝗶𝗻𝗴𝘂𝗶𝘀𝗵𝗲𝘀 𝗰𝗿𝗲𝗱𝗶𝘁 𝗿𝗮𝗻𝗸𝗶𝗻𝗴𝘀 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗰𝗶𝘁𝘆 𝗼𝗳 𝗔𝗺𝘀𝘁𝗲𝗿𝗱𝗮𝗺 𝗶𝗻 𝘁𝗲𝗿𝗺𝘀 𝗼𝗳 𝗱𝗲𝗮𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗜 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀?

To talk about challenges in the domain of bias in AI, I invited fantastic guests:

Elsa Sklavounou , an expert on Natural Language Processing (NLP), a researcher in the area of linguistics and computational linguistics (Ph.D.), VP of AI Alliances, Global Partnerships - Content Technology at RWS Group,

Marijn Markus , AI Lead, data scientist, public speaker and thought leader, Managing Consultant at Capgemini,

Matt Fornito , AI transformation advisor to Fortune 500 companies, a keynote speaker and thought leader, Chief Data Officer at revenue.io

In the fifteenth episode of Sherpa Search on Tech, we discuss:

✅ What makes Artificial Intelligence biased? Data, algorithms or people?

✅ How can we bring inclusivity into artificial intelligence? How can we make AI more beneficial for society?

✅ A need to look at data collected from a different point of view to get a more holistic view

✅ What is bias and how do they arise?

✅ Are big data sets better for creating AI models?

✅ The role of data scientists in the decision-making process, and what model they need to apply.

✅ Do we really need tons of data collection?

✅ How can we deal with a diversity of data, and what actions do we need to take?

✅ What do we want to achieve with the use of AI, do we want AI to reproduce reality or rather teach us something?

✅ What is the real „science” part of data science?

✅ Bias is everyone’s problem because every company will be one day a data-driven business.

✅ Global and local perception of data and discrepancies between the US and Europa.

✅ Insights on regulating Artificial Intelligence.

✅ ChatGPT

✅ Book recommendations

I highly recommend this talk!

Links:

Maciej Szczerba on Linkedin

Elsa Sklavounou on Linkedin

Marijn Markus on Linkedin

Matt Fornito on Linkedin

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