Here are the potential issues identified based on the analysis of the `spotify-2023.csv` dataset:

```json
[
    {
        "issue": "Corrupted track_name entry",
        "evidence": "Seven (feat. Latto) (Explicit Ver.), Daylight (entry appearing twice)",
        "description": "The track_name column contains entries that appear to be duplicated or incorrectly encoded, such as 'Daylight' appearing twice, which could indicate data corruption or incorrect merging of data entries."
    },
    {
        "issue": "Suspiciously high in_spotify_playlists value",
        "evidence": "52898 (in_spotify_playlists value)",
        "description": "An entry in the in_spotify_playlists column has a value of 52898, which is significantly higher than the typical range observed. This might indicate an error in data entry or a corruption issue."
    },
    {
        "issue": "Invalid key values",
        "evidence": "858 non-null values in key column, but 11 unique values",
        "description": "The key column has many non-null entries but only 11 unique values, which is less than expected considering musical key diversity. This suggests possible data corruption or misclassification of key values."
    },
    {
        "issue": "Incorrect instrumentalness_% value",
        "evidence": "instrumentalness_% max value 91",
        "description": "The instrumentalness_% column contains a maximum value of 91, which is unusually high. Instrumentalness is typically measured on a scale from 0 to 1 (0% to 100%). Such high values might suggest data corruption or an error in the dataset."
    },
    {
        "issue": "Released year outlier",
        "evidence": "1930 in released_year column",
        "description": "The released_year column includes an outlier year value of 1930, which seems unrealistic for a dataset focusing on contemporary Spotify tracks. This could indicate an entry error or corrupted data."
    }
]
```

These issues suggest that the dataset has several potential corruption problems, such as duplicated or incorrectly encoded entries, outliers in numeric fields, and values that do not match the expected ranges for certain columns. Further data cleaning and validation are recommended to ensure the integrity of the dataset.