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# The 100 Days of Colour: Unearthing Algorithmic Bias in Music Streaming

When Emily Wright realized 95% of her Spotify recommendations featured white artists, she launched a 100-day experiment. Discover how she exposed the hidden algorithmic bias controlling your playlists.

- **Speakers:** [Emily Wright](https://www.wearedevelopers.com/@emily-wright)
- **Event:** World Congress 2026 Europe - Virtual Stage
- **Published:** July 2, 2026
- **Duration:** 31:01
- **URL:** https://www.wearedevelopers.com/videos/1970-the-100-days-of-colour-unearthing-algorithmic-bias-in-music-streaming

## Summary

Emily Wright's '100 Days of Color' experiment explores the intersection of subconscious habits and algorithmic bias in music streaming platforms. After realizing over 95% of her Spotify recommendations featured white artists, Wright designed a 100-day conscious listening challenge to determine if her personal bias or the platform's recommendation loops were driving this homogenous trend. By intentionally flooding her feed with diverse artists and tracking the platform's core algorithmic playlists, she uncovered the powerful grip of historical data on machine learning models. Her primary recommendations remained unchanged until she actively cleared her 'liked' history, illustrating how past digital baggage double-weights algorithmic assumptions.

Throughout the experiment, Wright highlights the 'convenience hierarchy,' demonstrating how easily accessible content heavily dictates user consumption and reinforces default societal priorities. Furthermore, she exposes 'algotorial filtering'—a process where human curators establish a baseline dataset that algorithms then personalize. By analyzing raw curated playlists, Wright identified the 'curator trap': the demographic representation of recommended artists strikingly mirrored the demographic makeup of Spotify's internal teams. This limitation parallels issues found in convolutional neural networks used for facial recognition, proving that 'algorithmic bias is a digitized version of subconscious bias.' Ultimately, Wright challenges users to audit their historical data, break free from automated echo chambers, and intentionally evaluate whether convenient algorithmic recommendations actually reflect their personal values.

**Keywords:** algorithmic bias detection, music recommendation loops, algotorial filtering systems, subconscious bias digitization, convenience hierarchy ux, algorithmic curator trap, machine learning training data, historical data weighting, convolutional neural network bias, digital wellness manipulation, content recommendation engines, playlist demographic analytics, tech diversity algorithm impact, automated echo chambers

## Chapters

1. **Identifying recommendation loops and algorithmic bias in music** (00:00) — Analyzing personal listening history reveals a subconscious preference amplified by streaming recommendation engines.
1. **Establishing principles for testing algorithmic responsiveness to new data** (03:44) — Flooding the recommendation engine with new inputs without using search functions ensures unbiased algorithmic reactions.
1. **Tracking demographic shifts across core algorithmic recommendation playlists** (07:30) — Monitoring various auto-generated playlists over time measures how quickly recommendation algorithms adapt to changed input.
1. **The persistent influence of historical data on automated recommendations** (10:01) — Clearing historical preferences reveals how algorithms double-weight legacy interactions over recent behavior shifts.
1. **Visualizing playlist accessibility and the convenience hierarchy concept** (12:46) — Grouping recommendation access points demonstrates how algorithmic conveniences reflect and reinforce dominant collective priorities.
1. **Human curation frameworks and algortorial filtering in streaming platforms** (16:01) — Examining how base recommendations established by human curators constrain subsequent machine learning personalization options.
1. **Identifying the curator trap in algorithmic training models** (21:09) — Comparing internal company demographics to baseline human-curated recommendations highlights systemic limits in machine learning systems.
1. **Recognizing and mitigating digitized subconscious bias in everyday technology** (25:23) — Acknowledging the human elements within automated systems helps navigate convenience hierarchies and minimize algorithmic manipulation.

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