A lot of the hyperbolic coverage on the implications of AI, in tandem with the overarching umbrella under which products and services are labeled as “AI”, is rooted in misinformation. De-blackboxing the myths and paving the way for a clearer personal understanding of artificial intelligence and similar concepts has been my primary goal as our class navigated through the field. Fittingly, the European Union’s guidelines for developing ethical applications of AI provide a succinct summation of the range of concepts covered over the course of our class. Some of the key points were as follows:
- Human Agency & Oversight (the very first concept we tackled in this class, that human autonomy allows for design and intervention)
- Privacy and Governance
- Diversity, Non-discrimination and fairness (linking back to the work we did on ML fairness)
This journey of insights led to narrowing down my fields of interest – NLP, ML and deep learning systems – to determine my final project on Spotify and the manner in which its algorithms are changing the way we interact with music. Spotify uses “taste analysis data” a technology developed by Echo Nest (Titlow, 2016), which groups the music users frequently listen to into clusters (not genres, as human categorization of music is largely subjective). Examples of this are Spotify’s Discover Weekly and Daily Mix playlists, and also the end of the year “wrapped” playlists, where they provide each user with insights about their music habits. Essentially, Discover Weekly is Spotify’s unique version of the recommendation engine – similar to the way in which Amazon recommends new books (and just about everything else under the sun) both online and recently bringing the same phenomenon to their brick and mortar Amazon Bookstores – “if you like this, try this!
According to Marcus, “….in speech recognition, for example, a neural network learns a mapping between a set of speech sounds, and set of labels (such as words or phonemes)”. For the purpose of my project, I aim to determine how deep learning systems and neural networks learn how to map songs for the “Discover Weekly” playlist, for example, to determine which set of categories a certain song belongs to. Marcus also claims that “the logic of deep learning is such that it is likely to work best in highly stable worlds”, which is problematic, both for the scope of my project (especially in today’s world of fluid genres in terms of music) and the larger sociotechnical system we live in.