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Your search keyword '"Lee, Michelle Seng Ah"' showing total 2 results
Start Over Author "Lee, Michelle Seng Ah" Topic artificial intelligence
2 results on '"Lee, Michelle Seng Ah"'

1. Algorithmic Fairness in Mortgage Lending: from Absolute Conditions to Relational Trade-offs

Author

Lee, Michelle Seng Ah, Floridi, Luciano, Lee, Michelle Seng Ah [0000-0001-7725-2503], Floridi, Luciano [0000-0002-5444-2280], and Apollo - University of Cambridge Repository

Subjects
Algorithmic fairness, Philosophy of mind, Philosophy of science, Forcing (recursion theory), Computer science, Fairness trade-offs, Trade offs, Representation (systemics), Binary number, 5001 Applied Ethics, 02 engineering and technology, Mortgage discrimination, Philosophy, 50 Philosophy and Religious Studies, Absolute (philosophy), Artificial Intelligence, Technology ethics, 020204 information systems, Machine learning, Theory of computation, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, General Article, Mathematical economics
Abstract

To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decisionmaking processes. Using US mortgage lending as an example use case, we discuss the ethical foundations of each definition of fairness and demonstrate that our proposed methodology more closely captures the ethical trade-offs of the decision-maker, as well as forcing a more explicit representation of which values and objectives are prioritised.

Published
2020
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2. Practical fundamental rights impact assessments.

Author

Janssen, Heleen, Lee, Michelle Seng Ah, and Singh, Jatinder

Subjects
DATA protection laws, CIVIL rights, ARTIFICIAL intelligence, RISK assessment
Abstract

The European Union's General Data Protection Regulation tasks organizations to perform a Data Protection Impact Assessment (DPIA) to consider fundamental rights risks of their artificial intelligence (AI) system. However, assessing risks can be challenging, as fundamental rights are often considered abstract in nature. So far, guidance regarding DPIAs has largely focussed on data protection, leaving broader fundamental rights aspects less elaborated. This is problematic because potential negative societal consequences of AI systems may remain unaddressed and damage public trust in organizations using AI. Towards this, we introduce a practical, four-Phased framework, assisting organizations with performing fundamental rights impact assessments. This involves organizations (i) defining the system's purposes and tasks, and the responsibilities of parties involved in the AI system; (ii) assessing the risks regarding the system's development; (iii) justifying why the risks of potential infringements on rights are proportionate; and (iv) adopt organizational and/or technical measures mitigating risks identified. We further indicate how regulators might support these processes with practical guidance. [ABSTRACT FROM AUTHOR]

Published
2022
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