Data Transformers Podcast
Ethical considerations for companies in implementing AI
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Episode Title : Ethical considerations for companies in implementing AI
Episode Summary: Artificial Intelligence deployments are at an early stage almost akin to E-Commerce deployments were 15 years ago. The terminology is still being understood and normalized. Fion Lee Madan of Fairly AI goes over the need for fairness for AI based on her observations in personal life. Similar to DevOps for e-commerce, there is need for ML ops and model ops for AI as well. Unfortunately, business decision makers focus on ROI first with governance second. Regulation can help balance the equation.
02:00: One of the sources of inequality could be lack of data itself. Or incomplete data. Or lower income people not having access to technology or the internet itself.
05:00 Focus of Fairly AI is to be horizontal but initial focus is Financial services which is more ready than other industries.
07:30 (Headliner): AI is currently the same state as e-commerce was 15 years ago. For example: Buy Vs. Build decisions; DevOps used to be the thing for e-commerce; ML Ops and Model Ops is very reactive. So we need Analytics Ops.
10:40: Need for explainable AI. There is a lot of need for explainable AI. We should make AI more transparent.
13:00: Business decision makers focus on ROI. But data scientists may not be so. Need to fix that gap. Business users need to understand the tradeoffs of more accuracy versus more cost.
15:00: Companies first need to focus on their data. Ethics need to be considered even before design is concerned. Should look at something like AI readiness planner to deal with ethical considerations.
19:00: AI Governance – Reputational harm should be one of the main concerns of any organization. Other than that regulation is the way to ensure that governance is in the front burner.
Resources mentioned in this episode:
Podcast website: https://DataTransformersPodcast.Com
Data Transformers Podcast
Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.
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