MI2DataLab

Professionals creating edu content, successful solutions & innovative research in responsible ML & explainable AI • Affiliated with @WUT_edu & @UniWarszawski 🤖

[MI2DataLab @mi2_ai]

Hey everyone! 👋

We’re a group of scientists creating:

➡️ edu content
➡️ successful solutions
➡️ innovative research

in responsible ML & explainable AI 🤖

We will post daily so you can learn with us - in general & about work we feel proud about.

Let us know who to follow!

[Tilo Winkler @[email protected] @tilo_winkler]

EXCELLENT compact #dataviz #reference guidelines for color use in figures every scientist should keep at hand. Thank you, @krzyzinskim @mi2_ai
Also, check the work of @SteveFranconeri @datavisFriendly

Free PDF #openscience: https://t.co/c1uavHC2Z6

[Bartek Pieliński @bartpiel]

If you want to test your resilience against manipulative AI, please play this game: https://t.co/1rzh4SS92H created by @mi2_ai The game is a simplified version of Who Wants to Be a Millionaire? with AI as your best/worst advisor ...

[Mustafa Çavuş @mustafa__cavus]

1⃣You should definitely check out the new level glocal explanations we proposed in our latest preprint 📰'Glocal Explanations of Expected Goal Models in Soccer' w/Adrian Stańdo and @PrzeBiec from @mi2_ai, if you only use local and global #XAI tools to explain black-box ML models!

[Mustafa Çavuş @mustafa__cavus]

Our proposed solution with details can be found in our recent pre-print, w/ Adrian Stańdo and @PrzeBiec from @mi2_ai, titled 'The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems'

You can access the paper on arXiv: https://t.co/d49rO5QZ0U (5/6)

[Hubert Baniecki @hbaniecki]

🏁 The {modelStudio} project started in 2019 but only now feels complete. We have:
1. Implemented software for interactive #explainableai

[Przemysław Biecek @PrzeBiec]

Model explanation is a process. Read more about it in 'The grammar of interactive explanatory model analysis' (https://t.co/pCBpwWO8J0) just published in the SI on Explainable and Interpretable Machine Learning and Data Mining (by @hbaniecki and Dariusz Parzych @mi2_ai #RedTeam)

[MI2DataLab @mi2_ai]

Many thanks for awesome presentation and organisation to @LindauerMarius @FrankRHutter @AutoML_org!

You can definitely expect @mi2_ai during next editions. 💪

[MI2DataLab @mi2_ai]

This week our lab members @woznicakat and @kozaka93 attended #AutoMLFallSchool 2022 in Freiburg! 🔥

Want to know what they think about it? 🧵👇

[MI2DataLab @mi2_ai]

This is what they shared 👇

"Throughout the week we participated in many lectures, hands-on sessions and a hackathon. Lots of knowledge and new insights into the future of #AutoML!

It was also a unique opportunity to meet many people working in this area."

[Data Science Summit @DSS_conference]

#DataScienceSummit #RadaProgramowa #DataScience

Przedstawiamy Przemysława Biecek (@PrzeBiec), który w tym roku zasiada w Radzie Programowej Data Science Summit.
--
Rejestracja już trwa! Nie zwlekaj i zarejestruj się już dziś!

[Katarzyna Woźnica @woznicakat]

Today on #ECMLPKDD2022, I have a pleasure of presenting a poster based on our newest paper “Interpretable meta-score for model performance” published in Nature Machine Intelligence.

➡️ It answers a question - how to improve evaluation metrics in ML? 👇🧵

[MI2DataLab @mi2_ai]

We're proud of our representation on #ECMLPKDD2022!

➡️ If you're there, make sure to catch up with @PrzeBiec, @hbaniecki & @woznicakat to talk about @mi2_ai newest and cutting-edge research in #XAI.

[Hubert Baniecki @hbaniecki]

Today, I am presenting our work on adversarial #xai at #ECMLPKDD2022 in Grenoble, France.

Can we trust model explanations?

We highlight that Partial Dependence can be manipulated with adversarial data perturbations!

👇 1/3

[MI2DataLab @mi2_ai]

One of the topics we focus on in MI2 is #datavisualization.

➡️Ania is our expert in visualization & will definitely help how to do it right! 🔥

For the start, you should visit Ania's profile and check out awesome posters that she & her students prepared about climate topics 👇

[Anna Kozak @kozaka93]

Last academic year, together with @hbaniecki, I had the pleasure to teach Data Visualization Techniques course at @WUT_edu 👇

➡️Good #datavisualization is extremely important, so during the course, we introduced students to the world of ggplot2 and other visualization tools.

[MI2DataLab @mi2_ai]

Another achievement in our lab! 🔥

➡️ @krzyzinskim just published the details of his & his colleagues work on interpretability in survival models with a time-dependent SHAP! Time dependence is a major breakthrough in this case.

Follow him for more on XAI & Survival Analysis 👇

[Mateusz Krzyziński @krzyzinskim]

📉 SurvSHAP(t): Time-dependent explanations of machine learning survival models

The lack of #interpretability in #ML survival models is a significant challenge, as in many areas, including medicine, it is crucial to know what affects the model output.

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[MI2DataLab @mi2_ai]

We're very proud of you Kasia and waiting for more cutting-edge research! 🔥

And if you're interested in:

➡️ Hyperparameter Optimization
➡️ AutoML
➡️ Meta Learning

make sure to follow @woznicakat for more content on it!

[Katarzyna Woźnica @woznicakat]

I had the great pleasure of presenting the results of our last year’s work at #COSEAL22 workshop!

Main topic: Consolidated Learning. It’s a novel look at meta-learning in a specific domain of predictive problems, closer to reflecting real-world applications of ML models.

[MI2DataLab @mi2_ai]

Ceteris Paribus is a useful method for local explainability which:

1️⃣ for a single example in data (eg. Ben's spendings)
2️⃣ shows how the output changes (eg. his credit score)
3️⃣ when we modify 1 feature (eg. spendings on fuel)
4️⃣ and keep the rest of the features intact.

[Mustafa Çavuş @mustafa__cavus]

📦{fairmodels} provides useful tools to check the fairness of the ML models.

I highly recommend the {fairmodels} package for responsible ML!

#rstats #responsibleML

[MI2DataLab @mi2_ai]

Plotting a graph in readable way is an actual art! 🖌

How to do it right?

➡️ Tip 4: Intuitiveness!

If your data is related to real life concepts, think about intuitive colours for them. Is blue the first colour you think of when describing a hot dessert?

More tips to follow!

[MI2DataLab @mi2_ai]

TensorFlow + dalex == ❤️, see for yourself in our tutorial 👇

https://t.co/BSVoroN0lz

[MI2DataLab @mi2_ai]

Starting with dalex package for Explainable AI is super easy! 🔥

➡️ The idea is to create an Explainer wrapper to your model and data.

In the upcoming flashcards, we will explore what we can do with it. 👀

Link to the full tutorial in the comment! 👇