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It’s a myth that young people don’t care about privacy — it’s all about context! Give them a concrete example they can relate to, and they care big-time! Also check out the research by Dana Boyd at NYU: they DO care.

Great infographic on cognitive #biases. Being aware of them is a good first step towards building #explainable and #trustworthy AI systems. lnkd.in/gaPM8F8F

So happy to see such initiatives! #girlswhocode

Happy International Day of Women in Science! To all the #WomenInScience thank you for paving the way. And to all the girls out there follow your passion, if you can dream it you can be it! #GirlsInScience #dreamitbeit #smashgenderbiases

Great discussion with George Peabody on the future of AI in financial services and the role of ethics and trustworthiness. Thank you for having me on the Payments on Fire #podcast! #ethicsinai #financialservices #privacy #trustworthyai onespan.com/resources/ai-m…

Thank you for having me on the #moneypot podcast!

Great insight from Ismini Psychoula about how the financial services industry can overcome inherent biases bringing us closer to trustworthy #AI

This week I am taking over Greek Girls Code to share my experience in research and STEM. I look forward to connecting and discussing with other women in the field!📚 💻

Hi everyone, this is Ismini Psychoula taking over this week from rainy UK! I will be sharing a little bit about my career path and my research on #trustworthyAI #explainability and #privacy. If there is any topic you would like me to focus more on please let me know in the comments!

If I can offer a bit of advice to current students, it's to be involved with a lot of extra activities like #hackathons, #seminars, #summerschools. It is a great way to gain experience and figure out which area you enjoy working with the most #algorithms #robotics #hardware

Trustworthy AI is a term used to describe the components needed to have AI systems that we can trust. Those are: -Privacy -Robustness -Explainability -Fairness -Transparency

Today let’s discuss #explainable #AI, which is a set of methods that allow users to understand how a #deepearning model came to each result/output. This helps us understand its impact and potential biases. (Image from darpa.mil/program/explai…)

Regulatory measures started being proposed to ensure #trustworthyAI. The EU introduced the AI Act, the UK released its AI Strategy, NIST is working on developing standards and tools and UNESCO members recently signed an agreement that defines the common values for ethical AI.

As my weekly takeover is coming to end, I am going to chat a bit about my PhD journey. Even though I knew I really enjoyed working on research projects looking for PhD programs was not easy.

Besides the financial part, the biggest benefits of the Marie Curie ITN for me were the opportunities for collaborations with other researchers/universities and training events that they offer (research methodologies, publishing, entrepreneurship, public speaking and specialised topics)

Last day of the takeover! Let’s have a Q&A and share tips and resources for anyone interested in #research and #ArtificialIntelligence (ask me anything and add your own tips)! 👩‍💻📑

Thanks for having me Greek Girls Code, it was a great pleasure! And thank you for all you do to inspire and empower young women in #STEM 💻🧪🛰️

How do you prove you are not an AI?! 🤖

Following up from yesterday’s threads on #uncertainty and machine learning models, I wanted to reiterate the impact the data has on model learning and behaviour. Learnt biases might be so strong that a model will ignore contextual information in the input.

Occurs to me that one of the most productive (but rare?) ways to increase the standing of women and other marginalized groups in computing is to invite them to collaborate with you, especially if you're in the elite of your field 1/4