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Communications of the ACM.

Material type: Text

TextSeries: Publication details: New York : Association for Computing Machinery (ACM), c2023.Description: 112 pages : color illustrations ; 28 cmISSN:

0001-0782

Subject(s):

Contents:

Computing Divided: How Wide the Chasm? -- How Not to Win a Tech War -- A Computer Scientist with a Biologist’s Ambition: Advance Humanity -- Making AI Fair, and How to Use It -- Error Control Begins to Shape Quantum Architectures -- The Outlook for Crypto -- Making Traffic a Thing of the Past -- Frederick P. Brooks, Jr. 1931–2022 -- Remembering Valérie Issarny -- From Quantum Computing to Quantum Communications -- Getting a Handle on Handles -- Are Software Updates Useless against Advanced Persistent Threats? -- The End of Programming -- Are We Cobblers without Shoes? Making Computer Science Data FAIR -- The AI Ethicist’s Dirty Hands Problem Distributed Latency Profiling through Critical Path Tracing -- Research for Practice: Crash Consistency -- The Many Faces of Resilience -- ACE: Toward Application-Centric, Edge-Cloud, Collaborative Intelligence -- Democratizing Domain-Specific Computing -- A Linearizability-based Hierarchy for Concurrent Specifications -- The Impact of Auditing for Algorithmic Bias -- Actionable Auditing Revisited— Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products -- Maximal Cocktails

Summary: [Article Title: Computing Divided: How Wide the Chasm?/ Andrew A. Chien, p. 5] https://doi.org/10.1145/3572994Summary: [Article Title: How Not to Win a Tech War/ Moshe Y. Vardi, p. 7] https://doi.org/10.1145/357107Summary: [Article Title: A Computer Scientist with a Biologist's Ambition: Advance Humanity/ Michelle Zhou, p. 9] https://doi.org/10.1145/3571064Summary: [Article Title: Making AI Fair, and How to Use It/ Marc Rotenberg, and Jeremy Roschelle, p. 10-11] https://doi.org/10.1145/3570517Summary: [Article Title: Error Control Begins to Shape Quantum Architectures/ Chris Edwards, p. 13-15] https://doi.org/10.1145/3570518Summary: [Article Title: The Outlook for Crypto/ Neil Savage, p. 16-18] https://doi.org/10.1145/3570520Summary: [Article Title: Making Traffic a Thing of the Past/ Logan Kugler, p. 19-20] https://doi.org/10.1145/3570519Summary: [Article Title: In Memoriam: Frederick P. Brooks, Jr. 1931--2022/ Simson Garfinkel, and Eugene H. Spafford, p. 21-22] https://doi.org/10.1145/3572995Summary: [Article Title: Remembering Valérie Issarny/ John Delaney, p. 23] https://doi.org/10.1145/3573217Summary: [Article Title: From Quantum Computing to Quantum Communications/ Michael A. Cusumano, p. 24-27] https://doi.org/10.1145/3571450Summary: [Article Title: Getting a Handle on Handles/ Alexandra J. Roberts, p. 28-30] https://doi.org/10.1145/3571451Summary: [Article Title: Are Software Updates Useless against Advanced Persistent Threats?/ Fabio Massacci, and Giorgio di Tizio, p. 31-33] https://doi.org/10.1145/3571452Summary: [Article Title: The End of Programming/ Matt Welsh, p. 34-35] https://doi.org/10.1145/3570220Summary: [Article Title: Are We Cobblers without Shoes?: Making Computer Science Data FAIR/ Natasha Noy, and Carole Goble, p. 36-38] https://doi.org/10.1145/3528574 Summary: [Article Title: The AI Ethicist's Dirty Hands Problem/ Henrik Skaug Sætra, Mark Coeckelbergh, and John Danaher, p. 39-41] https://doi.org/10.1145/35297Summary: [Article Title: Distributed Latency Profiling through Critical Path Tracing/ Brian Eaton, Jeff Stewart, Jon Tedesco, and N. Cihan Tas, p. 44-51] https://doi.org/10.1145/3570522Summary: [Article Title: Research for Practice: Crash Consistency/Ramnatthan Alagappan, and Peter Alvaro, p. 52-54] https://doi.org/10.1145/3570521Summary: [Article Title: The Many Faces of Resilience/Ted G. Lewis, p. 56-61] https://doi.org/10.1145/3519262Summary: [Article Title: ACE: Toward Application-Centric, Edge-Cloud, Collaborative Intelligence/Luhui Wang, Cong Zhao, Shusen Yang, and Xinyu Yang, p. 62-73] https://doi.org/10.1145/3529087Summary: [Article Title: Democratizing Domain-Specific Computing/ Yuze Chi, Weikang Qiao, Atefeh Sohrabizadeh, Jie Wang, and Jason Cong, p. 74-85] https://doi.org/10.1145/3524108Summary: [Article Title: A Linearizability-based Hierarchy for Concurrent Specifications/ Armando Castañeda, Sergio Rajsbaum, and Michel Raynal, p. 86-97] https://doi.org/10.1145/3546826Summary: [Article Title: Technical Perspective: The Impact of Auditing for Algorithmic Bias/ Vincent Conitzer, Gillian K. Hadfield, and Shannon Vallor, p. 100] https://doi.org/10.1145/3571152Summary: [Article Title: Actionable Auditing Revisited: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products/ Inioluwa Deborah Raji, and Joy Buolamwini, p. 101-108] Abstract: Although algorithmic auditing has emerged as a key strategy to expose systematic biases embedded in software platforms, we struggle to understand the real-world impact of these audits and continue to find it difficult to translate such independent assessments into meaningful corporate accountability. To analyze the impact of publicly naming and disclosing performance results of biased AI systems, we investigate the commercial impact of Gender Shades, the first algorithmic audit of gender- and skin-type performance disparities in commercial facial analysis models. This paper (1) outlines the audit design and structured disclosure procedure used in the Gender Shades study, (2) presents new performance metrics from targeted companies such as IBM, Microsoft, and Megvii (Face++) on the Pilot Parliaments Benchmark (PPB) as of August 2018, (3) provides performance results on PPB by non-target companies such as Amazon and Kairos, and (4) explores differences in company responses as shared through corporate communications that contextualize differences in performance on PPB. Within 7 months of the original audit, we find that all three targets released new application program interface (API) versions. All targets reduced accuracy disparities between males and females and darker- and lighter-skinned subgroups, with the most significant update occurring for the darker-skinned female subgroup that underwent a 17.7--30.4% reduction in error between audit periods. Minimizing these disparities led to a 5.72--8.3% reduction in overall error on the Pilot Parliaments Benchmark (PPB) for target corporation APIs. The overall performance of non-targets Amazon and Kairos lags significantly behind that of the targets, with error rates of 8.66% and 6.60% overall, and error rates of 31.37% and 22.50% for the darker female subgroup, respectively. This is an expanded version of an earlier publication of these results, revised for a more general audience, and updated to include commentary on further developments. https://doi.org/10.1145/3571151Summary: [Article Title: Maximal Cocktails/ Dennis Shasha, p. 112] https://doi.org/10.1145/3571275

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Item type	Current library	Home library	Collection	Call number	Copy number	Status	Date due	Barcode
Serials	National University - Manila	LRC - Main Periodicals	Gen. Ed. - CCIT	Communications of the ACM, Volume 66, Issue 1, January 2023 (Browse shelf(Opens below))	c.1	Available		PER000000607

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Includes bibliographical references.

[Article Title: Computing Divided: How Wide the Chasm?/ Andrew A. Chien, p. 5]

https://doi.org/10.1145/3572994

[Article Title: How Not to Win a Tech War/ Moshe Y. Vardi, p. 7]

https://doi.org/10.1145/357107

[Article Title: A Computer Scientist with a Biologist's Ambition: Advance Humanity/ Michelle Zhou, p. 9]

https://doi.org/10.1145/3571064

[Article Title: Making AI Fair, and How to Use It/ Marc Rotenberg, and Jeremy Roschelle, p. 10-11]

https://doi.org/10.1145/3570517

[Article Title: Error Control Begins to Shape Quantum Architectures/ Chris Edwards, p. 13-15]

https://doi.org/10.1145/3570518

[Article Title: The Outlook for Crypto/ Neil Savage, p. 16-18]

https://doi.org/10.1145/3570520

[Article Title: Making Traffic a Thing of the Past/ Logan Kugler, p. 19-20]

https://doi.org/10.1145/3570519

[Article Title: In Memoriam: Frederick P. Brooks, Jr. 1931--2022/ Simson Garfinkel, and Eugene H. Spafford, p. 21-22]

https://doi.org/10.1145/3572995

[Article Title: Remembering Valérie Issarny/ John Delaney, p. 23]

https://doi.org/10.1145/3573217

[Article Title: From Quantum Computing to Quantum Communications/ Michael A. Cusumano, p. 24-27]

https://doi.org/10.1145/3571450

[Article Title: Getting a Handle on Handles/ Alexandra J. Roberts, p. 28-30]

https://doi.org/10.1145/3571451

[Article Title: Are Software Updates Useless against Advanced Persistent Threats?/ Fabio Massacci, and Giorgio di Tizio, p. 31-33]

https://doi.org/10.1145/3571452

[Article Title: The End of Programming/ Matt Welsh, p. 34-35]

https://doi.org/10.1145/3570220

[Article Title: Are We Cobblers without Shoes?: Making Computer Science Data FAIR/ Natasha Noy, and Carole Goble, p. 36-38]

https://doi.org/10.1145/3528574

[Article Title: The AI Ethicist's Dirty Hands Problem/ Henrik Skaug Sætra, Mark Coeckelbergh, and John Danaher, p. 39-41]

https://doi.org/10.1145/35297

[Article Title: Distributed Latency Profiling through Critical Path Tracing/ Brian Eaton, Jeff Stewart, Jon Tedesco, and N. Cihan Tas, p. 44-51]

https://doi.org/10.1145/3570522

[Article Title: Research for Practice: Crash Consistency/Ramnatthan Alagappan, and Peter Alvaro, p. 52-54]

https://doi.org/10.1145/3570521

[Article Title: The Many Faces of Resilience/Ted G. Lewis, p. 56-61]

https://doi.org/10.1145/3519262

[Article Title: ACE: Toward Application-Centric, Edge-Cloud, Collaborative Intelligence/Luhui Wang, Cong Zhao, Shusen Yang, and Xinyu Yang, p. 62-73]

https://doi.org/10.1145/3529087

[Article Title: Democratizing Domain-Specific Computing/ Yuze Chi, Weikang Qiao, Atefeh Sohrabizadeh, Jie Wang, and Jason Cong, p. 74-85]

https://doi.org/10.1145/3524108

[Article Title: A Linearizability-based Hierarchy for Concurrent Specifications/ Armando Castañeda, Sergio Rajsbaum, and Michel Raynal, p. 86-97]

https://doi.org/10.1145/3546826

[Article Title: Technical Perspective: The Impact of Auditing for Algorithmic Bias/ Vincent Conitzer, Gillian K. Hadfield, and Shannon Vallor, p. 100]

https://doi.org/10.1145/3571152

[Article Title: Actionable Auditing Revisited: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products/ Inioluwa Deborah Raji, and Joy Buolamwini, p. 101-108]

Abstract: Although algorithmic auditing has emerged as a key strategy to expose systematic biases embedded in software platforms, we struggle to understand the real-world impact of these audits and continue to find it difficult to translate such independent assessments into meaningful corporate accountability. To analyze the impact of publicly naming and disclosing performance results of biased AI systems, we investigate the commercial impact of Gender Shades, the first algorithmic audit of gender- and skin-type performance disparities in commercial facial analysis models. This paper (1) outlines the audit design and structured disclosure procedure used in the Gender Shades study, (2) presents new performance metrics from targeted companies such as IBM, Microsoft, and Megvii (Face++) on the Pilot Parliaments Benchmark (PPB) as of August 2018, (3) provides performance results on PPB by non-target companies such as Amazon and Kairos, and (4) explores differences in company responses as shared through corporate communications that contextualize differences in performance on PPB. Within 7 months of the original audit, we find that all three targets released new application program interface (API) versions. All targets reduced accuracy disparities between males and females and darker- and lighter-skinned subgroups, with the most significant update occurring for the darker-skinned female subgroup that underwent a 17.7--30.4% reduction in error between audit periods. Minimizing these disparities led to a 5.72--8.3% reduction in overall error on the Pilot Parliaments Benchmark (PPB) for target corporation APIs. The overall performance of non-targets Amazon and Kairos lags significantly behind that of the targets, with error rates of 8.66% and 6.60% overall, and error rates of 31.37% and 22.50% for the darker female subgroup, respectively. This is an expanded version of an earlier publication of these results, revised for a more general audience, and updated to include commentary on further developments.