000			01456nam a2200217Ia 4500
008			210916s9999 xx 000 0 und d
020			_a9789385889783
082			_a005.743 _bNIE
100			_aNielsen, Aileen _96484
245		0	_aPractical Fairness : _bachieving fair and secure data models
260			_aUSA _bSPD _c2021
300			_a330p.
520			_aFairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we're trying to fight in the real world. But what does fairness mean when it comes to code? This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that's fair and free of bias. Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to closed model audits. Author Aileen Nielsen guides you through technical, legal, and ethical aspects of making code fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms.
650			_aBusiness Ethics _93627
650			_aData Science _95167
650			_aNeural Networks _91124
650			_aHuman-Computer Interaction (HCI) _96485
650			_aArtificial intelligence _9560
650			_aInformation modeling _96486
942			_cBK
999			_c6948 _d6948