This week, Sirin, Boris, and Demet have some recommended reading for you in the fields of descriptive data analysis, machine learning, and ethics in artificial intelligence. Have you recently read anything thought-provoking in the field of data science? Written anything thought-provoking? Be sure to comment and share your recommendations with us.
Seth Stephens-Davidowitz studies publicly available, anonymous Google Search data. His work reveals prejudices and sheds light on aspects of demography that are hard to tackle with surveys. It’s a long, yet captivating read and a great example data story telling that shows how insightful descriptive data analysis can be. It’s also deeply infuriating because, among other things, his work implies that open racism and biases against girls are widespread.
The post “Supervised learning is great — it’s data collection that’s broken” talks about the pain common to many machine learning practitioners, namely, obtaining good quality labeled data. Citing the post:
Instead of waiting for the unsupervised messiah to arrive, we need to fix the way we’re collecting and reusing human knowledge.
This is a Turing Institute opinion piece on Ethical AI and accountability which is always an effort to be celebrated. It discusses the shortcomings of the General Data Protection Regulation (GDPR) and the potential to make it more enforceable in the future. I think as data scientists we should all be aware of the hidden biases in our work and aim to be as accountable as possible, but at the same time, it makes me question whether mandating fine-grained accountability from the algorithms we write might make our playing ground more restrictive. Though, even if that is the case, the trade-off may be well worth it.
In 2016 the EU General Data Protection Regulation (GDPR), Europe’s new data protection framework, was approved. The new regulation will come into force across Europe – and the UK – in 2018. It has been widely and repeatedly claimed that a ‘right to explanation’ of all decisions made by automated or artificially intelligent algorithmic systemswill be legally mandated by the new regulation. This “right to explanation” is viewed as an ideal mechanism to enhance the accountability and transparency of automated algorithmic decision-making.
Such a right would enable people to ask how a specific decision (e.g. being declined insurance or being denied a promotion) was reached.