Listen to Krystyn Gutu, M.S. introduce Sasha Stoikov, a senior research associate at Cornell Financial Engineering in Manhattan (CFEM). His research studies algorithms in high-frequency financial trading, online ratings systems, and recommendation systems. In these various domains, he has come across algorithmic biases such as survivorship bias, popularity bias, and inflation bias.

He is also the founder of Piki, a startup that gamifies music ratings. Ratings produced by users on Piki can mitigate algorithmic biases, which he discusses in a recent paper, aimed at answering a simple but provocative question: “Are the popularities of artists like Justin Bieber or Taylor Swift truly justified?”

Check out his paper, Better Than Bieber? Measuring Song Quality Using Human Feedback, to find out. He also authored Evaluating Music Recommendations with Binary Feedback for Multiple Stakeholders, and Picky Eaters Make For Better Raters.

In this episode, we discuss

– the roles of survivorship bias, popularity bias, and inflation bias

– Piki, the music ratings app designed exclusively for those who know what they like

– how algorithms like Instagram, TikTok, and Spotify compare to how Piki analyzes song quality

– data used to train these and other platforms

– how interfaces collecting data unintentionally encourage certain biases

– implicit vs explicit data collection

– how data is collected and how it addresses the main concerns of their users

– how Piki incentives its users to listen to a larger music selection and nudges them into being fair with their ratings

– the golden era of data and the tremendous opportunities and dangers that lie ahead

If you like what you hear, follow us on LinkedIn (@Gakovii) or on Instagram (@gakovii__).

Technically Biased is available on Spotify, Apple, and Amazon, among others.

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