References
Please refer to the following resources to gain additional information:
- The $300m flip flop: how real-estate site Zillow's side hustle went badly wrong, theguardian.com: https://www.theguardian.com/business/2021/nov/04/zillow-homes-buying-selling-flip-flop
- Zillow says it's closing homebuying business, cutting 25% of workforce; earnings miss estimates, cnbc.com: https://www.cnbc.com/2021/11/02/zillow-shares-plunge-after-announcing-it-will-close-home-buying-business.html
- Google Photos Tags Two African-Americans As Gorillas Through Facial Recognition Software, forbes.com: https://www.forbes.com/sites/mzhang/2015/07/01/google-photos-tags-two-african-americans-as-gorillas-through-facial-recognition-software/
- Google apologises for Photos app's racist blunder, bbc.com: https://www.bbc.com/news/technology-33347866
- Tutorial on Interpretable Machine Learning, MICCAI'18, by Samek and Binder: http://www.heatmapping.org/slides/2018_MICCAI.pdf
- Chapter 1 – Interpretation, Interpretability, and Explainability and Why Does It All Matter? by Serg Masis, Packt Publishing Ltd.: https://www.amazon.com/Interpretable-Machine-Learning-Python-hands/dp/180020390X
- Interpretable Machine Learning, Christoph Molnar: https://christophm.github.io/interpretable-ml-book/
- Towards A Rigorous Science of Interpretable Machine Learning by Doshi-Velez and Kim: https://arxiv.org/abs/1702.08608