Practical Fairness Quotes
Practical Fairness: Achieving Fair and Secure Data Models
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Aileen Nielsen9 ratings, 4.00 average rating, 0 reviews
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Practical Fairness Quotes
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“These rights are the more concrete manifestations of a variety of general legal principles and privacy- and autonomy-enhancing principles that guide the GDPR, in general promoting the individual’s right to agency in navigating digital data-driven products and right to understand how their information and the resulting outcomes are derived from the data. The GDPR empowers individuals through rights to information but also rights to affect how and whether entities can use their information.”
― Practical Fairness: Achieving Fair and Secure Data Models
― Practical Fairness: Achieving Fair and Secure Data Models
“Our discussion of equity was quite wide-ranging and went far beyond the matter of treating similarly meritorious individuals similarly and into the domain of also treating all individuals with a baseline level of respect and autonomy.”
― Practical Fairness: Achieving Fair and Secure Data Models
― Practical Fairness: Achieving Fair and Secure Data Models
“In this group of issues related to fair play, we consider whether we are putting data subjects in a position that undermines their autonomy or authority about how their personal information is used.”
― Practical Fairness: Achieving Fair and Secure Data Models
― Practical Fairness: Achieving Fair and Secure Data Models
“Openness in political decision-making matters. It is key to maintaining confidence in public institutions and to achieving fairness and due process.”
― Practical Fairness: Achieving Fair and Secure Data Models
― Practical Fairness: Achieving Fair and Secure Data Models
“Activists successfully challenged inequitable access to public assistance by appealing decisions and demanding access to administrative law procedures known as fair hearings.”
― Practical Fairness: Achieving Fair and Secure Data Models
― Practical Fairness: Achieving Fair and Secure Data Models
“Research on bias, fairness, transparency, and the myriad dimensions of safety now forms a substantial portion of all of the work presented at major AI and machine-learning conferences.”
― Practical Fairness: Achieving Fair and Secure Data Models
― Practical Fairness: Achieving Fair and Secure Data Models
“Concerns about technology and fairness go back a long way, even from a legal perspective. For example, as early as the 1970s it was illegal under French law to make any decisions affecting human beings in a purely algorithmic manner—that is, without any human supervision.”
― Practical Fairness: Achieving Fair and Secure Data Models
― Practical Fairness: Achieving Fair and Secure Data Models
“Is it fairer for everyone to have the same opportunities or to have the same outcomes? Equality of opportunity or equality of outcome? Is it fairer for decisions to be uniform or to embody an element of human empathy? Impartial justice or individual allowances? Is it fairer to let people know how decisions are made or to have an opaque system to prevent cheating? Transparency or security?”
― Practical Fairness: Achieving Fair and Secure Data Models
― Practical Fairness: Achieving Fair and Secure Data Models
“Practical considerations most often come up in the form of three fundamental questions a society needs to answer in order to function: Who gets what? (Rules of allocation) How do we decide who gets what? (Rules of decision) Who decides who decides? (Rules of political authority)”
― Practical Fairness: Achieving Fair and Secure Data Models
― Practical Fairness: Achieving Fair and Secure Data Models
“We would all like a world where everyone has equal opportunities and receives fair treatment.”
― Practical Fairness
― Practical Fairness
