Algorithmic fairness in online information mediating systems (Extended Abstract)

Ansgar Koene; Sofia Ceppi; Helena Webb; Menisha Patel; Elvira Perez; Giles Lane; Marina Jirotka; Michael Rovatsos

doi:10.1145/3091478.3098864

Algorithmic fairness in online information mediating systems (Extended Abstract)

Ansgar Koene, Sofia Ceppi, Helena Webb, Menisha Patel, Elvira Perez, Giles Lane, Marina Jirotka, Michael Rovatsos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper explores the fundamental challenges around fair management of information access when the limits of human attention require algorithmic assistance for ‘€finding the diamond in the coal mountain’. While o‰en named as a key demand from users, the seemingly intuitive concept of fairness has proven to be very difficult to satisfyingly operationalise for implementation in algorithms.
Here we present two pilot studies aimed at getting a better understanding
of the conceptualisation of algorithmic fairness by users. The first was a multi-stakeholder focus-group discussion, the second a user experiment/questionnaire. Based on our data we arrive at a picture of fairness that is highly dependent and context and informedness of users, and possibly inherently misleading due to the implied projecting of human intentions onto an algorithmic process.

Original language	English
Title of host publication	WebSci '17 Proceedings of the 2017 ACM on Web Science Conference
Place of Publication	Troy, NY, USA
Publisher	ACM
Pages	391-392
Number of pages	2
ISBN (Print)	978-1-4503-4896-6
DOIs	https://doi.org/10.1145/3091478.3098864
Publication status	Published - 25 Jun 2017
Event	2017 ACM on Web Science Conference - Troy, United States Duration: 25 Jun 2017 → 28 Jun 2017 http://websci17.org/

Conference

Conference	2017 ACM on Web Science Conference
Abbreviated title	WEBSCI’17
Country/Territory	United States
City	Troy
Period	25/06/17 → 28/06/17
Internet address	http://websci17.org/

Access to Document

10.1145/3091478.3098864

koeneetal-websci2017 (1)Accepted author manuscript, 362 KB

http://dl.acm.org/citation.cfm?doid=3091478.3098864

UnBias: Emancipating Users Against Algorithmic Biases for a Trusted Digital Economy
Rovatsos, M.
EPSRC
1/09/16 → 30/11/18
Project: Research

Cite this

@inproceedings{3d750fc0e60148a0a854b8bb78b01622,

title = "Algorithmic fairness in online information mediating systems (Extended Abstract)",

abstract = "This paper explores the fundamental challenges around fair management of information access when the limits of human attention require algorithmic assistance for {\textquoteleft}€finding the diamond in the coal mountain{\textquoteright}. While o‰en named as a key demand from users, the seemingly intuitive concept of fairness has proven to be very difficult to satisfyingly operationalise for implementation in algorithms.Here we present two pilot studies aimed at getting a better understandingof the conceptualisation of algorithmic fairness by users. The first was a multi-stakeholder focus-group discussion, the second a user experiment/questionnaire. Based on our data we arrive at a picture of fairness that is highly dependent and context and informedness of users, and possibly inherently misleading due to the implied projecting of human intentions onto an algorithmic process.",

author = "Ansgar Koene and Sofia Ceppi and Helena Webb and Menisha Patel and Elvira Perez and Giles Lane and Marina Jirotka and Michael Rovatsos",

year = "2017",

month = jun,

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doi = "10.1145/3091478.3098864",

language = "English",

isbn = "978-1-4503-4896-6",

pages = "391--392",

booktitle = "WebSci '17 Proceedings of the 2017 ACM on Web Science Conference",

publisher = "ACM",

note = "2017 ACM on Web Science Conference, WEBSCI{\textquoteright}17 ; Conference date: 25-06-2017 Through 28-06-2017",

url = "http://websci17.org/",

}

Koene, A, Ceppi, S, Webb, H, Patel, M, Perez, E, Lane, G, Jirotka, M & Rovatsos, M 2017, Algorithmic fairness in online information mediating systems (Extended Abstract). in WebSci '17 Proceedings of the 2017 ACM on Web Science Conference. ACM, Troy, NY, USA, pp. 391-392, 2017 ACM on Web Science Conference, Troy, New York, United States, 25/06/17. https://doi.org/10.1145/3091478.3098864

TY - GEN

T1 - Algorithmic fairness in online information mediating systems (Extended Abstract)

AU - Koene, Ansgar

AU - Ceppi, Sofia

AU - Webb, Helena

AU - Patel, Menisha

AU - Perez, Elvira

AU - Lane, Giles

AU - Jirotka, Marina

AU - Rovatsos, Michael

PY - 2017/6/25

Y1 - 2017/6/25

N2 - This paper explores the fundamental challenges around fair management of information access when the limits of human attention require algorithmic assistance for ‘€finding the diamond in the coal mountain’. While o‰en named as a key demand from users, the seemingly intuitive concept of fairness has proven to be very difficult to satisfyingly operationalise for implementation in algorithms.Here we present two pilot studies aimed at getting a better understandingof the conceptualisation of algorithmic fairness by users. The first was a multi-stakeholder focus-group discussion, the second a user experiment/questionnaire. Based on our data we arrive at a picture of fairness that is highly dependent and context and informedness of users, and possibly inherently misleading due to the implied projecting of human intentions onto an algorithmic process.

AB - This paper explores the fundamental challenges around fair management of information access when the limits of human attention require algorithmic assistance for ‘€finding the diamond in the coal mountain’. While o‰en named as a key demand from users, the seemingly intuitive concept of fairness has proven to be very difficult to satisfyingly operationalise for implementation in algorithms.Here we present two pilot studies aimed at getting a better understandingof the conceptualisation of algorithmic fairness by users. The first was a multi-stakeholder focus-group discussion, the second a user experiment/questionnaire. Based on our data we arrive at a picture of fairness that is highly dependent and context and informedness of users, and possibly inherently misleading due to the implied projecting of human intentions onto an algorithmic process.

U2 - 10.1145/3091478.3098864

DO - 10.1145/3091478.3098864

M3 - Conference contribution

SN - 978-1-4503-4896-6

SP - 391

EP - 392

BT - WebSci '17 Proceedings of the 2017 ACM on Web Science Conference

PB - ACM

CY - Troy, NY, USA

T2 - 2017 ACM on Web Science Conference

Y2 - 25 June 2017 through 28 June 2017

ER -

Algorithmic fairness in online information mediating systems (Extended Abstract)

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UnBias: Emancipating Users Against Algorithmic Biases for a Trusted Digital Economy

Cite this