東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Risky Reforms: A Sociotechnical Anal...

Green, Ben.

FindBook

Google Book

Amazon

博客來

Risky Reforms: A Sociotechnical Analysis of Algorithms as Tools for Social Change.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Risky Reforms: A Sociotechnical Analysis of Algorithms as Tools for Social Change./
作者:	Green, Ben.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	254 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
Contained By:	Dissertations Abstracts International82-06B.
標題:	Applied mathematics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28094129
ISBN:	9798698534082

Risky Reforms: A Sociotechnical Analysis of Algorithms as Tools for Social Change.
Green, Ben.

Risky Reforms: A Sociotechnical Analysis of Algorithms as Tools for Social Change. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 254 p.

Source: Dissertations Abstracts International, Volume: 82-06, Section: B.

Thesis (Ph.D.)--Harvard University, 2020.

This item must not be sold to any third party vendors.

This thesis considers the relationship between efforts to address social problems using algorithms and the social impacts of these interventions. Despite widespread optimism about algorithms as tools to promote reform and improve society, there is often little rigorous analysis regarding how algorithmic interventions will lead to particular desired outcomes. In turn, many well-intentioned applications of algorithms have led to social harm. In this thesis, I focus on the use of "risk assessments" in the U.S. criminal justice as a notable example of machine learning algorithms being used as tools for social change. Treating these algorithmic interventions as sociotechnical and political reform efforts rather than primarily technical projects, I center my analyses of risk assessments around their social and political consequences. In Part I (Interaction), I introduce a new "algorithm-in-the-loop" framework for evaluating the impacts of algorithms in practice, using experiments to uncover unexpected behaviors that occur when people collaborate with risk assessments. In Part II (Risk and Response), I interrogate typical conceptions of risk and how to respond to it, developing a novel machine learning method to analyze structural factors of violence and to support non-punitive and public health-inspired violence prevention efforts. In Part III (Reform), I place these technical studies in the broader context of social and political reform, describing the limits of risk assessments as a tool for criminal justice reform and articulating a new mode of practice-"algorithmic realism"-that synthesizes computer science, law, STS, and political theory in order to equip computer scientists to work more rigorously in the service of social change. By expanding the scope of questions asked of risk assessments, this dissertation sheds new light on how risk assessments represent a "risky" strategy for achieving criminal justice reform. Through these analyses, I chart the beginnings of a more interdisciplinary and rigorous approach to evaluating and developing algorithms as tools for social change.

ISBN: 9798698534082Subjects--Topical Terms:

2122814
Applied mathematics.
Subjects--Index Terms:

Algorithmic fairness

Risky Reforms: A Sociotechnical Analysis of Algorithms as Tools for Social Change.
LDR:03316nmm a2200385 4500 001 2281858
005 20210927083413.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798698534082
035 $a (MiAaPQ)AAI28094129
035 $a AAI28094129
040 $a MiAaPQ $c MiAaPQ
100 1 $a Green, Ben. $3 693239
245 1 0 $a Risky Reforms: A Sociotechnical Analysis of Algorithms as Tools for Social Change.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 254 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-06, Section: B.
500 $a Advisor: Chen, Yiling.
502 $a Thesis (Ph.D.)--Harvard University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a This thesis considers the relationship between efforts to address social problems using algorithms and the social impacts of these interventions. Despite widespread optimism about algorithms as tools to promote reform and improve society, there is often little rigorous analysis regarding how algorithmic interventions will lead to particular desired outcomes. In turn, many well-intentioned applications of algorithms have led to social harm. In this thesis, I focus on the use of "risk assessments" in the U.S. criminal justice as a notable example of machine learning algorithms being used as tools for social change. Treating these algorithmic interventions as sociotechnical and political reform efforts rather than primarily technical projects, I center my analyses of risk assessments around their social and political consequences. In Part I (Interaction), I introduce a new "algorithm-in-the-loop" framework for evaluating the impacts of algorithms in practice, using experiments to uncover unexpected behaviors that occur when people collaborate with risk assessments. In Part II (Risk and Response), I interrogate typical conceptions of risk and how to respond to it, developing a novel machine learning method to analyze structural factors of violence and to support non-punitive and public health-inspired violence prevention efforts. In Part III (Reform), I place these technical studies in the broader context of social and political reform, describing the limits of risk assessments as a tool for criminal justice reform and articulating a new mode of practice-"algorithmic realism"-that synthesizes computer science, law, STS, and political theory in order to equip computer scientists to work more rigorously in the service of social change. By expanding the scope of questions asked of risk assessments, this dissertation sheds new light on how risk assessments represent a "risky" strategy for achieving criminal justice reform. Through these analyses, I chart the beginnings of a more interdisciplinary and rigorous approach to evaluating and developing algorithms as tools for social change.
590 $a School code: 0084.
650 4 $a Applied mathematics. $3 2122814
650 4 $a Computer science. $3 523869
650 4 $a Criminology. $3 533274
653 $a Algorithmic fairness
653 $a Criminal justice system
653 $a Decision-making
653 $a Risk assessments
653 $a Social networks
653 $a Sociotechnical
690 $a 0364
690 $a 0984
690 $a 0627
710 2 $a Harvard University. $b Engineering and Applied Sciences - Applied Math. $3 3184332
773 0 $t Dissertations Abstracts International $g 82-06B.
790 $a 0084
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28094129