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Building Organizational Trust in AI — Ksenia Palke // Airspace

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How do you start using AI in your organization? A bad reason to use AI is that you have this intellectual curiosity, which is great to have, but it’s not a good enough reason to start using AI. How do you, as CTO, present the advantages of mining your data? How do you know if you have enough data or whether you should be programming or training? Listen to Ksenia Palke, Director of AI at Airspace, as she discusses building organizational trust in AI.

About The Speaker:

Ksenia Palke
Ksenia PalkeDirector of AI at Airspace
Ksenia Palke leads the AI team at Airspace. Ksenia has been involved with multiple San Diego startups. She has designed and deployed AI for a range of uses, from fraud detection and vehicle collision to natural language processing of customer feedback and location intelligence. She joined Airspace in 2019 to head the development of state-of-the-art patented deep learning models that power time-critical logistics, and to work on optimizing operations. Ksenia is passionate about the implementation of cutting-edge AI as well as AI usability, trust, and utilization. She received her Ph.D. from Stanford.

Episode Resources:

Check out

Ksenia Palke: Website // LinkedIn

Etienne de Bruin: Website // LinkedIn // Twitter

Show Notes


Doing good is not enough. In the pursuit of AI expect ethical pushback

Many people want to work at a company that does good or at least doesn’t do something evil but in the line of duty, you may have to collect personally identifiable data. In the pursuit of AI and new ideas, you will come to ethical crossroads.


The gray zone

There are companies in the gray zone that unavoidably have to use personal data to deliver utility. When using AI, you have to avoid crossing over into the unethical.


AI bias

As you adopt AI services integrated into your company, consider that the models have biases in them.


Beyond APIs, are there ethical considerations or biases that require more due diligence?

Whether you’re buying data to use or maybe using the model or fully relying on them, you have to have an honest conversation about biases that might be affecting the models.


Do you really need a data scientist?

Many companies don’t understand that they don’t need to start with a data scientist. They need data engineers or someone who will build the infrastructure and later bring on a data scientist


Getting started with models and AI

Who comes first, a data scientist or a data engineer?