fairness and algorithmic bias

What is fairness in AI?
What is fairness? In many ways, bias and fairness in AI are two sides of the same coin. While there is no universally agreed upon definition for fairness, we can broadly define fairness as the absence of prejudice or preference for an individual or group based on their characteristics.
What is algorithm fairness?
Algorithm fairness is actually a bit of a misleading term. Algorithms, by themselves, are not inherently biased. They are just mathematical functions. By training one of these algorithms on data, we obtain a machine learning model. It is the introduction of biased data that will lead to a biased model.
What is algorithmic bias?
Algorithmic bias is a discriminatory case of algorithmic outcomes having an adversarial impact on protected or unprotected groups. It occurs when algorithms distribute benefits and burdens unequally among different stakeholders due to differences in their characteristics, talents, or luck (Akter et al., 2021; Kordzadeh & Ghasemaghaei, 2021 ).
Does de-biasing data solve the problem of algorithmic fairness?
Researchers suggest that the narrow focus on de-biasing data for improving algorithms does not solve the purpose (Dolata et al., 2021; Kordzadeh & Ghasemaghaei, 2021; Marjanovic et al., 2021 ). Researchers argue that the concept of algorithmic fairness is dynamic. Its normative understanding is shifting with time and context.

PDF	Economics Fairness and Algorithmic Bias Economics Fairness and Algorithmic Bias. ?. Bo Cowgill. Columbia University. Catherine Tucker. MIT & NBER. May 11

PDF	A Sociotechnical View of Algorithmic Fairness solutions to systemic biases and discrimination. Keywords: algorithmic fairness artificial intelligence

PDF	A Survey on Bias and Fairness in Machine Learning 1 Data to Algorithm. In this section we talk about biases in data which

PDF	Institut Montaigne Algorithmic bias: an old and complex problem 20. A. Biases pre-exist algorithms and are mainly found in the data they use 20. B. A fair algorithm has

PDF	AI Fairness 360: An Extensible Toolkit for Detecting Understanding 3 oct. 2018 open source Python toolkit for algorithmic fairness AI Fairness 360 ... metrics

PDF	Algorithmic Fairness 21 janv. 2020 and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness ...

PDF	Exacerbating Algorithmic Bias through Fairness Attacks Indeed most adversarial machine learning has focused on the impact of malicious attacks on the accuracy of the system

PDF	Fair Models for Impartial Policies: Controlling Algorithmic Bias in 9 juil. 2022 Keywords: algorithmic bias; behavioural transport models; machine learning; fairness; emerging technologies; data sources. 1. Introduction.

PDF	Algorithmic Fairness and Economics 24 sept. 2020 The model suggests that machine learning algorithms can remove human biases exhibited in historical training data but only if the human ...

PDF	Review into bias in algorithmic decision-making - GOV.UK can enter a system and how this might impact on fairness. The issue is not simply whether an algorithm is biased but whether the overall decision-making

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Fairness and Bias in Algorithmic Decision-Making - NYU Computer

Bias scan: a general approach for auditing (and correcting) black-box algorithms for fairness 1) Group Fairness: The same proportion of each group should be classified as “high risk” – Makes sense for analyzing discrimination in employment: about the same proportion of each group should be hired

PDF	Algorithms: Please Mind the Bias - Institut Montaigne bias that we will create confidence in the fairness of algorithms Testing the fairness of an algorithm has a cost and requires test data that specifically

PDF	Democratizing Algorithmic Fairness - PhilPapers KEYWORDS: Algorithmic Bias, Machine Learning, Fairness, Democratization, Accountability for Reasonableness ACKNOWLEDGEMENT: - Page 2

PDF	Algorithmic and Economic Perspectives on Fairness - Computing The fourth relates to data biases (Suresh and Guttag 2019) All statistical algorithms rely on training data, which implicitly encode the choices of algorithm

PDF	Landscape Summary: Bias in Algorithmic Decision-Making - Govuk 2 1 Algorithmic bias, discrimination and fairness 11 2 2 The legal context in the UK 13 2 3 How do algorithms work? How might algorithmic bias occur? 15

PDF	Introduction and Overview Algorithmic Fairness - Data, Responsibly Fixing bias in algorithms? Changing algorithms is easier than changing people: software on computers can be updated; the “wetware” in our brains