| { |
| "paper_id": "2020", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T13:54:54.323660Z" |
| }, |
| "title": "Bias in AI-systems: A multi-step approach", |
| "authors": [ |
| { |
| "first": "Eirini", |
| "middle": [], |
| "last": "Ntoutsi", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Leibniz Universit\u00e4t Hannover & L3S Research Center", |
| "location": {} |
| }, |
| "email": "ntoutsi@kbs.uni-hannover.de" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "Algorithmic-based decision making powered via AI and (big) data has already penetrated into almost all spheres of human life, from content recommendation and healthcare to predictive policing and autonomous driving, deeply affecting everyone, anywhere, anytime. While technology allows previously unthinkable optimizations in the automation of expensive human decision making, the risks that the", |
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| "paper_id": "2020", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "Algorithmic-based decision making powered via AI and (big) data has already penetrated into almost all spheres of human life, from content recommendation and healthcare to predictive policing and autonomous driving, deeply affecting everyone, anywhere, anytime. While technology allows previously unthinkable optimizations in the automation of expensive human decision making, the risks that the", |
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| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
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| } |
| } |
