unstructuredio/SCORE-Bench · Datasets at Hugging Face

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published: 22 October 2020
doi: 10.3389/frai.2020.543305
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Artiﬁcial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children
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Xanthe Hunt 1* , Mark Tomlinson 1,2 , Siham Sikander³, Sarah Skeen¹, Marguerite Marlow¹, Stefani du Toit¹ and Manuel Eisner⁴
¹Department of Global Health, Institute for Life Course Health Research, Stellenbosch University, Stellenbosch, South Africa, ²School of Nursing and Midwifery, Queens University Belfast, Belfast, United Kingdom, ³Global Health Department, Health Services Academy, Islamabad, Pakistan, ⁴Institute of Criminology, University of Cambridge, Cambridge, United Kingdom
Violence against children is a global public health threat of considerable concern. At least half of all children worldwide experience violence every year; globally, the total number of children between the ages of 2 and 17 years who have experienced violence in any given year is one billion. Based on a review of the literature, we argue that there is substantial potential for AI (and associated machine learning and big data), and mHealth approaches to be utilized to prevent and address violence at a large scale. This potential is particularly marked in low- and middle-income countries (LMIC), although whether it could translate into effective solutions at scale remains unclear. We discuss possible entry points for Artiﬁcial Intelligence (AI), big data, and mHealth approaches to violence prevention, linking these to the World Health Organization’s seven INSPIRE strategies. However, such work should be approached with caution. We highlight clear directions for future work in technology-based and technology-enabled violence prevention. We argue that there is a need for good agent-based models at the level of entire cities where and when violence can occur, where local response systems are. Yet, there is a need to develop common, reliable, and valid population- and individual/family-level data on predictors of violence. These indicators could be integrated into routine health or other information systems and become the basis of Al algorithms for violence prevention and response systems. Further, data on individual help-seeking behavior, risk factors for child maltreatment, and other information which could help us to identify the parameters required to understand what happens to cause, and in response to violence, are needed. To respond to ethical issues engendered by these kinds of interventions, there must be concerted, meaningful efforts to develop participatory and user-led work in the AI space, to ensure that the privacy and proﬁling concerns outlined above are addressed explicitly going forward. Finally, we make the case that developing AI and other technological infrastructure will require substantial investment, particularly in LMIC.
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Edited by: Alain B. Labrique, Johns Hopkins University, United States
Reviewed by: Guohua Huang, Shaoyang University, China Melissa O’Donnell, University of South Australia, Australia
*Correspondence: Xanthe Hunt xanthehuntwrites@gmail.com
Specialty section: This article was submitted to Medicine and Public Health, a section of the journal Frontiers in Artiﬁcial Intelligence
Received: 16 March 2020
Accepted: 08 September 2020
Published: 22 October 2020
Citation: Hunt X, Tomlinson M, Sikander S, Skeen S, Marlow M, du Toit S and Eisner M (2020) Artiﬁcial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children. Front. Artif. Intell. 3:543305. doi: 10.3389/frai.2020.543305
Keywords: violence, child abuse, artiﬁcial intelligence, mHealth, big data, machine learning, LMIC
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Frontiers in Artiﬁcial Intelligence \| www.frontiersin.org
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1
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October 2020 \| Volume 3 \| Article 543305
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Permit Number: EPR/BU5453IY Operator: AGC Chemicals Europe Limited Facility: AGC Chemicals Europe, Hillhouse AGC Chemicals Permit No: EPR/BU5453IY
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Reporting of Continuously Monitored Emissions to Air for NOₓ, Emission Point A14 - TTP for Jun, 2024
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30 min ELV Daily ELV
mg/Nm3
600 480 360 240 120 0
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Key Daily average ELV 30 min. average ELV Mean 30 min. average Max. 30 min average Min. 30 min average Daily mean
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95th Percentile = 141.6 No. of valid averages = 1109
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Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
30 min ELV 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400
Max. Off Off Off Off Off Off 140.6 151.8 148.6 146.7 151.4 155.7 153.7 140.3 150.5 156.0 156.6 131.5 133.2 136.2 153.2 133.5 136.1 127.4 124.7 134.9 132.5 138.6 139.9 134.3
Mean Off Off Off Off Off Off 124.9 127.9 123.3 113.8 109.1 137.0 128.1 125.6 126.8 133.8 128.8 121.8 120.6 122.8 127.1 122.7 121.6 117.0 113.7 117.1 121.3 126.5 120.8 123.9
Min. Off Off Off Off Off Off 111.4 110.6 115.7 94.8 84.2 112.9 97.1 114.9 116.9 120.5 106.2 104.4 104.9 114.0 109.2 112.1 95.1 92.8 104.1 109.5 89.5 107.2 112.9 113.1
No invalid 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
No > ELV 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Daily ELV 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200
Daily Mean Off Off Off Off Off Off N/A 127.9 123.3 113.8 109.1 137.0 128.1 125.6 126.8 133.8 128.8 121.8 120.6 122.8 127.1 122.7 121.6 N/A 113.7 117.1 121.3 126.5 120.8 123.9
Invalid day N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N
> ELV N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N
Report day N N N N N N N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y
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Month summary
30 min ELV
Monthly max. 156.6
Monthly mean 123.2
Monthly min. 84.2
Total invalid 0
Sum > ELV 0
Daily ELV
Monthly max 137.0
No. invalid days 0
No. > ELV 0
No. report days 22
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mCERTS THE ENVIRONMENT AGENCY’S
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Data is as processed without repairs.
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Signature
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Authorised Representative
Date 26/07/2024
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Source: https://www.industrydocuments.ucsf.edu/docs/ftgd0346
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Permit Number: EPR/BU543IY
Operator: AGC Chemicals Europe Limited
Facility: AGC Chemicals Europe, Hillhouse
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published: 22 October 2020

doi: 10.3389/frai.2020.543305

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Check for updates

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Artiﬁcial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children

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Xanthe Hunt 1* , Mark Tomlinson 1,2 , Siham Sikander³, Sarah Skeen¹, Marguerite Marlow¹, Stefani du Toit¹ and Manuel Eisner⁴

¹Department of Global Health, Institute for Life Course Health Research, Stellenbosch University, Stellenbosch, South Africa, ²School of Nursing and Midwifery, Queens University Belfast, Belfast, United Kingdom, ³Global Health Department, Health Services Academy, Islamabad, Pakistan, ⁴Institute of Criminology, University of Cambridge, Cambridge, United Kingdom

Violence against children is a global public health threat of considerable concern. At least half of all children worldwide experience violence every year; globally, the total number of children between the ages of 2 and 17 years who have experienced violence in any given year is one billion. Based on a review of the literature, we argue that there is substantial potential for AI (and associated machine learning and big data), and mHealth approaches to be utilized to prevent and address violence at a large scale. This potential is particularly marked in low- and middle-income countries (LMIC), although whether it could translate into effective solutions at scale remains unclear. We discuss possible entry points for Artiﬁcial Intelligence (AI), big data, and mHealth approaches to violence prevention, linking these to the World Health Organization’s seven INSPIRE strategies. However, such work should be approached with caution. We highlight clear directions for future work in technology-based and technology-enabled violence prevention. We argue that there is a need for good agent-based models at the level of entire cities where and when violence can occur, where local response systems are. Yet, there is a need to develop common, reliable, and valid population- and individual/family-level data on predictors of violence. These indicators could be integrated into routine health or other information systems and become the basis of Al algorithms for violence prevention and response systems. Further, data on individual help-seeking behavior, risk factors for child maltreatment, and other information which could help us to identify the parameters required to understand what happens to cause, and in response to violence, are needed. To respond to ethical issues engendered by these kinds of interventions, there must be concerted, meaningful efforts to develop participatory and user-led work in the AI space, to ensure that the privacy and proﬁling concerns outlined above are addressed explicitly going forward. Finally, we make the case that developing AI and other technological infrastructure will require substantial investment, particularly in LMIC.

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OPEN ACCESS

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Edited by: Alain B. Labrique, Johns Hopkins University, United States

Reviewed by: Guohua Huang, Shaoyang University, China Melissa O’Donnell, University of South Australia, Australia

*Correspondence: Xanthe Hunt xanthehuntwrites@gmail.com

Specialty section: This article was submitted to Medicine and Public Health, a section of the journal Frontiers in Artiﬁcial Intelligence

Received: 16 March 2020

Accepted: 08 September 2020

Published: 22 October 2020

Citation: Hunt X, Tomlinson M, Sikander S, Skeen S, Marlow M, du Toit S and Eisner M (2020) Artiﬁcial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children. Front. Artif. Intell. 3:543305. doi: 10.3389/frai.2020.543305

Keywords: violence, child abuse, artiﬁcial intelligence, mHealth, big data, machine learning, LMIC

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Frontiers in Artiﬁcial Intelligence | www.frontiersin.org

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1

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October 2020 | Volume 3 | Article 543305

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Permit Number: EPR/BU5453IY Operator: AGC Chemicals Europe Limited Facility: AGC Chemicals Europe, Hillhouse AGC Chemicals Permit No: EPR/BU5453IY

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Reporting of Continuously Monitored Emissions to Air for NOₓ, Emission Point A14 - TTP for Jun, 2024

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30 min ELV Daily ELV

mg/Nm3

600 480 360 240 120 0

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Key Daily average ELV 30 min. average ELV Mean 30 min. average Max. 30 min average Min. 30 min average Daily mean

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95th Percentile = 141.6 No. of valid averages = 1109

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Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

30 min ELV 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400 400

Max. Off Off Off Off Off Off 140.6 151.8 148.6 146.7 151.4 155.7 153.7 140.3 150.5 156.0 156.6 131.5 133.2 136.2 153.2 133.5 136.1 127.4 124.7 134.9 132.5 138.6 139.9 134.3

Mean Off Off Off Off Off Off 124.9 127.9 123.3 113.8 109.1 137.0 128.1 125.6 126.8 133.8 128.8 121.8 120.6 122.8 127.1 122.7 121.6 117.0 113.7 117.1 121.3 126.5 120.8 123.9

Min. Off Off Off Off Off Off 111.4 110.6 115.7 94.8 84.2 112.9 97.1 114.9 116.9 120.5 106.2 104.4 104.9 114.0 109.2 112.1 95.1 92.8 104.1 109.5 89.5 107.2 112.9 113.1

No invalid 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

No > ELV 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Daily ELV 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200 200

Daily Mean Off Off Off Off Off Off N/A 127.9 123.3 113.8 109.1 137.0 128.1 125.6 126.8 133.8 128.8 121.8 120.6 122.8 127.1 122.7 121.6 N/A 113.7 117.1 121.3 126.5 120.8 123.9

Invalid day N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N

> ELV N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N

Report day N N N N N N N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y N Y Y Y Y Y Y

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Month summary

30 min ELV

Monthly max. 156.6

Monthly mean 123.2

Monthly min. 84.2

Total invalid 0

Sum > ELV 0

Daily ELV

Monthly max 137.0

No. invalid days 0

No. > ELV 0

No. report days 22

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mCERTS THE ENVIRONMENT AGENCY’S

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Data is as processed without repairs.

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Signature

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Authorised Representative

Date 26/07/2024

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Source: https://www.industrydocuments.ucsf.edu/docs/ftgd0346

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Permit Number: EPR/BU543IY

Operator: AGC Chemicals Europe Limited

Facility: AGC Chemicals Europe, Hillhouse

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