Raw LLM Responses

Admittedly not familiar with the specific example, but yes this is possible with input bias. The programmers feed a bunch of data into the program for analysis, but its unrealistic to feed absolutely everything in. So there's a few potential sources of bias: 1. Data fed in includes unrepresentative sample of crimes. 2. African Americans are historically more likely to receive lengthier sentences including parole. With longer parole periods, theres more time for individuals to reoffend and violate parole in that time frame. 3. African Americans are more likely to be charged with higher crimes or be offered less beneficial plea deals. The program may therefore see a higher rate of felonies vs. plea deal misdemeanors. 4. Input data may fail to include other distinguishing characteristics, such as socioeconomic status, unemployment rates in area, family, government, or NGO support available for reintegration, etc. If African Americans fare poorer in these other areas upon release, but they are not included in data, then software will note correlation to race when actual correlation is to these other factors. In general, when you plug bad data into a program, you get bad data out.