Stephen E. Arnold: When Big Data Becomes Bad Data Through Algorithmic Bias, Discrimination, and Distortion

IO Impotency
Stephen E. Arnold
Stephen E. Arnold

Algorithmic Bias and the Unintentional Discrimination in the Results

The article titled When Big Data Becomes Bad Data on Tech In America discusses the legal ramifications of relying on algorithms for companies. The “disparate impact” theory has been used in the courtroom for some time to ensure that discriminatory policies be struck down whether they were created with the intention to discriminate or not. Algorithmic bias occurs all the time, and according to the spirit of the law, it discriminates although unintentionally. The article states,

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Phi Beta Iota: Big Data is a monstrous scam. Like the porn industry (or the secret world) these people are mired in the past. They simply do not get the reality that the next big thing is all information in all languages all the time in a distributed cloud replete with a shared tool-kit and a global to local geospatial and time/historical laydown for every datum.

See Especially:

Robert Steele: Applied Collective Intelligence 2.0 – Draft Article Seeking Peer Comments

2015 Robert Steele – Foreword to Stephen E. Arnold’s CyberOSINT: Next Generation Information Access

See Also:

Big Data @ Phi Beta Iota


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