Normative Principles for Evaluating Fairness in Machine Learning

Leben, Derek

doi:10.1145/3375627.3375808

Cited by 29 publications

(26 citation statements)

References 6 publications

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“…The large gap in the FPR rates, despite addressing historical bias against a minority group, thus poses a problem from this viewpoint. From a political and moral philosophy perspective, judgements on fairness are often driven by broader normative principles [33]. In this regard, an unequal error rate could be used to correct a past bias.…”

Section: Geometric Deep Learning: a Fairer Approach?mentioning

confidence: 99%

Tackling racial bias in automated online hate detection: Towards fair and accurate detection of hateful users with geometric deep learning

2022

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Online hate is a growing concern on many social media platforms, making them unwelcoming and unsafe. To combat this, technology companies are increasingly developing techniques to automatically identify and sanction hateful users. However, accurate detection of such users remains a challenge due to the contextual nature of speech, whose meaning depends on the social setting in which it is used. This contextual nature of speech has also led to minoritized users, especially African–Americans, to be unfairly detected as ‘hateful’ by the very algorithms designed to protect them. To resolve this problem of inaccurate and unfair hate detection, research has focused on developing machine learning (ML) systems that better understand textual context. Incorporating social networks of hateful users has not received as much attention, despite social science research suggesting it provides rich contextual information. We present a system for more accurately and fairly detecting hateful users by incorporating social network information through geometric deep learning. Geometric deep learning is a ML technique that dynamically learns information-rich network representations. We make two main contributions: first, we demonstrate that adding network information with geometric deep learning produces a more accurate classifier compared with other techniques that either exclude network information entirely or incorporate it through manual feature engineering. Our best performing model achieves an AUC score of 90.8% on a previously released hateful user dataset. Second, we show that such information also leads to fairer outcomes: using the ‘predictive equality’ fairness criteria, we compare the false positive rates of our geometric learning algorithm to other ML techniques and find that our best-performing classifier has no false positives among a subset of African–American users. A neural network without network information has the largest number of false positives at 26, while a neural network incorporating manual network features has 13 false positives among African–American users. The system we present highlights the importance of effectively incorporating social network features in automated hateful user detection, raising new opportunities to improve how online hate is tackled.

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Section: Geometric Deep Learning: a Fairer Approach?mentioning

confidence: 99%

Tackling racial bias in automated online hate detection: Towards fair and accurate detection of hateful users with geometric deep learning

2022

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“…Philosophical research directly on fair ML can be roughly divided based on its stance towards the standard approach. Some research seeks to clarify and improve the often‐implicit normative underpinnings and commitments of various fairness measures (Glymour & Herington, 2019; Hellman, 2020; Leben, 2020). Many fairness measures are purportedly based in moral and legal doctrines (e.g., ‘disparate impact’, ‘equality of opportunity’, …), and so we might naturally expect that those statistical measures would track the conceptual intuitions in a wide range of cases.…”

Section: Solutions To Biasmentioning

confidence: 99%

Algorithmic bias: Senses, sources, solutions

Fazelpour

Danks

2021

Philosophy Compass

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Data‐driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so‐called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm‐based decision‐making. This paper aims to facilitate the application of philosophical analysis to these contested issues by providing an overview of three key topics: What is algorithmic bias? Why and how can it occur? What can and should be done about it? Throughout, we highlight connections—both actual and potential—with philosophical ideas and concerns.

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“…Bias-related issues can be identified by a proper analysis of the decisions made by the workflow, which in turn requires models to be accountable and transparent enough to thoroughly characterize their sensitivity to bias, and how inputs and outputs (decisions) correlate in regards to protected features. It is also remarkable to note that several proposals have been made to quantify fairness in machine learning pipelines, yielding useful metrics that account for the parity of models when processing groups of inputs [ 205 , 206 ]. Without these aspects being considered jointly with performance measures, data-based ITS developments in years to come are at the risk of being restricted to the academia playground [ 207 ].…”

Section: Emerging Ai Areas Towards Actionable Itsmentioning

confidence: 99%

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Laña

Medina

Vlahogianni

et al. 2021

Sensors

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Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.

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Normative Principles for Evaluating Fairness in Machine Learning

Cited by 29 publications

References 6 publications

Tackling racial bias in automated online hate detection: Towards fair and accurate detection of hateful users with geometric deep learning

Tackling racial bias in automated online hate detection: Towards fair and accurate detection of hateful users with geometric deep learning

Algorithmic bias: Senses, sources, solutions

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

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