SlideShare ist ein Scribd-Unternehmen logo
1 von 31
AI Governance and Ethics:
Industry Standards as vehicle to address socio-technical challenges
from AI
Ansgar Koene,
University of Nottingham
IEEE P7003 Standard for Algorithmic Bias Considerations
Undesired impact
The need for ethics, legal, social, economic intervention
Algorithms in the news
2
3
Algorithmic Discrimination
Machine Bias: There’s
software used across the
country to predict future
criminals. Propublica
Algorithmic systems are socio-technical
▸Algorithmic systems do not exist in a vacuum
▸They are built, deployed and used:
- by people,
- within organizations,
- within a social, political, legal and cultural context.
▸The outcomes of algorithmic decisions can have significant impacts
on real, and possibly vulnerable, people.
4
Governance frameworks for algorithmic systems
Public and Private sector responses to Undesired impacts of AI
EU response (in addition to GDPR)
EU Parliament Science and Technology Options Assessment
(STOA) panel request for study on “Algorithmic Opportunities
and Accountability”
6
Governance options
7
Florian Saurwein, Natascha Just, Michael Latzer, (2015) "Governance of algorithms: options and limitations", info, Vol. 17 Issue: 6, pp.35-49, doi: 10.1108/info-05-2015-
0025
Demand side Market solutions
8
Supply side Market solutions
9
Companies self-organization (aka CSR)
10
Branches self-regulations
11
Branches self-regulations: Industry standards
▸British Standards Institute (BSI) – BS 8611 Ethics design and application of robots
▸IEEE P70xx Ethics of Autonomous and Intelligent Systems standards
▸ISO/IEC JTC1 SC42 (launched at start of 2018)
- Artificial Intelligence Concepts and Terminology
- Framework for Artificial Intelligence Systems Using Machine Learning
• SG 2 on Trustworthiness
 transparency, verifiability, explainability, controllability, etc.
 robustness, resiliency, reliability, accuracy, safety, security, privacy, etc.
▸The EU standards bodies CEN and CENELEC officially created a Focus Group on AI, in support of
ISO/IEC SC42 (December 2018).
▸Jan 2018 China published “Artificial Intelligence Standardization White Paper.”
12
Co-regulation
13
State Intervention
14
Oversight by regulatory organisations
▸An FDA for algorithms – Andrew Tutt (2016)
▸An FAA for algorithms – paraphrasing Alan Winfield
(Chair of IEEE P7001 Standard for Algorithm Transparency)
15
Setting legal requirements
16
Industry Standards
IEEE P70xx Standards, developed as part of the
Global Initiative for Ethics of Autonomous and Intelligent Systems
17
18
IEEE P70xx Standards Projects
IEEE P7000: Model Process for Addressing Ethical Concerns During System Design
IEEE P7001: Transparency of Autonomous Systems
IEEE P7002: Data Privacy Process
IEEE P7003: Algorithmic Bias Considerations
IEEE P7004: Child and Student Data Governance
IEEE P7005: Employer Data Governance
IEEE P7006: Personal Data AI Agent Working Group
IEEE P7007: Ontological Standard for Ethically Driven Robotics and Automation Systems
IEEE P7008: Ethically Driven Nudging for Robotic, Intelligent and Autonomous Systems
IEEE P7009: Fail-Safe Design of Autonomous and Semi-Autonomous Systems
IEEE P7010: Wellbeing Metrics Standard for Ethical AI and Autonomous Systems
IEEE P7011: Process of Identifying and Rating the Trustworthiness of News Sources
IEEE P7012: Standard for Machines Readable Personal Privacy Terms
IEEE P7013: Inclusion and Application Standards for Automated Facial Analysis Technology
19
P7003 - Algorithmic Bias Considerations
▸All non-trivial* decisions are biased
▸We seek to minimize bias that is:
- Unintended
- Unjustified
- Unacceptable
▸as defined by the context where the system is used.
*Non-trivial means the decision space has more than one possible outcome and the
choice is not uniformly random.
Causes of algorithmic bias
▸Insufficient understanding of the context of use.
▸Failure to rigorously map decision criteria.
▸Failure to have explicit justifications for the chosen criteria.
Key question when developing or deploying an
algorithmic system
23
 Who will be affected?
 What are the decision/optimization criteria?
 How are these criteria justified?
 Are these justifications acceptable in the context where the
system is used?
IEEE P7003 general structure
24
5Rights Universal Standards for childhood and
adolescence
A collaboration between 5Rights and IEEE-SA
25
Why these Standards are needed
▸That presence of children in the digital environment must be anticipated
- by design
- by default
▸Technology must be provided in children in a way that upholds their rights
and meets their needs.
- Not simply about content
- The ways it uses their data
- The behaviours it encourages
- The responsibility it takes for the impact of its services.
26
The approach
Look systemically for the drivers and inhibitors in technological systems, to
create a good environment for child
▸How does the system impact on, or promote the autonomy of a child?
▸What effect might a child’s engagement have on their health or well being?
▸Have you considered both their physical and emotional wellbeing?
▸What processes are in place to inform the child of the likely impact of using your
service?
From the big ‘is it fair’ and ‘does it uphold the rights of the child’ to the entirely
granular about where on the screen a button might be better placed.
27
A suite of Standards/guidance - industry connections group
▸Age Appropriate Contract – that is to determine what terms and conditions, or community
rules should offer when the end user is a child.
▸Standards that cover:
- security of IoT,
- Child Online Protection Issues,
- Privacy differentials,
- context capacity authentication,
- guidance for duty of care,
- appropriate governance structures,
- reporting standards,
- flagging systems,
- data minimization standards,
- best practice geolocation
- etc.
28
Interplay between Standards and Legislation
▸5rights Founder and Chair, Baroness Beeban Kidron is the architect of
ground-breaking new Age Appropriate Design Code, an enhanced GDPR for
children under 18.
▸5Rights as an organization, believes that standards should both anticipate
and be an alternative to legislation.
▸Creating standards from a trusted source allows all businesses – small and
big – access to the thoughtful and ethical digital services for children.
29
Thank you!
ansgar.koene@Nottingham.ac.uk
IEEE P7003 Standard for Algorithmic Bias Considerations project site:
http://sites.ieee.org/sagroups-7003/
https://5rightsfoundation.com/

Weitere ähnliche Inhalte

Was ist angesagt?

Algorithmic Bias: Challenges and Opportunities for AI in Healthcare
Algorithmic Bias:  Challenges and Opportunities for AI in HealthcareAlgorithmic Bias:  Challenges and Opportunities for AI in Healthcare
Algorithmic Bias: Challenges and Opportunities for AI in HealthcareGregory Nelson
 
Ethics of Analytics and Machine Learning
Ethics of Analytics and Machine LearningEthics of Analytics and Machine Learning
Ethics of Analytics and Machine LearningMark Underwood
 
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdf
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfUNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdf
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
 
Why it’s unethical to focus on ‘AI Ethics’
Why it’s unethical to focus on ‘AI Ethics’Why it’s unethical to focus on ‘AI Ethics’
Why it’s unethical to focus on ‘AI Ethics’Kye Andersson
 
Generative AI and law.pptx
Generative AI and law.pptxGenerative AI and law.pptx
Generative AI and law.pptxChris Marsden
 
Responsible AI in Industry (ICML 2021 Tutorial)
Responsible AI in Industry (ICML 2021 Tutorial)Responsible AI in Industry (ICML 2021 Tutorial)
Responsible AI in Industry (ICML 2021 Tutorial)Krishnaram Kenthapadi
 
Using the power of Generative AI at scale
Using the power of Generative AI at scaleUsing the power of Generative AI at scale
Using the power of Generative AI at scaleMaxim Salnikov
 
Leveraging Generative AI & Best practices
Leveraging Generative AI & Best practicesLeveraging Generative AI & Best practices
Leveraging Generative AI & Best practicesDianaGray10
 
How do we train AI to be Ethical and Unbiased?
How do we train AI to be Ethical and Unbiased?How do we train AI to be Ethical and Unbiased?
How do we train AI to be Ethical and Unbiased?Mark Borg
 
Explainable AI in Industry (FAT* 2020 Tutorial)
Explainable AI in Industry (FAT* 2020 Tutorial)Explainable AI in Industry (FAT* 2020 Tutorial)
Explainable AI in Industry (FAT* 2020 Tutorial)Krishnaram Kenthapadi
 
Explainability and bias in AI
Explainability and bias in AIExplainability and bias in AI
Explainability and bias in AIBill Liu
 
AI in Manufacturing - John.pdf
AI in Manufacturing - John.pdfAI in Manufacturing - John.pdf
AI in Manufacturing - John.pdfJohn Chang
 
GENERATIVE AI, THE FUTURE OF PRODUCTIVITY
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYGENERATIVE AI, THE FUTURE OF PRODUCTIVITY
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYAndre Muscat
 
An Introduction to Generative AI
An Introduction  to Generative AIAn Introduction  to Generative AI
An Introduction to Generative AICori Faklaris
 
Artificial Intelligence and Machine Learning
Artificial Intelligence and Machine LearningArtificial Intelligence and Machine Learning
Artificial Intelligence and Machine LearningMykola Dobrochynskyy
 
Generative-AI-in-enterprise-20230615.pdf
Generative-AI-in-enterprise-20230615.pdfGenerative-AI-in-enterprise-20230615.pdf
Generative-AI-in-enterprise-20230615.pdfLiming Zhu
 

Was ist angesagt? (20)

Algorithmic Bias: Challenges and Opportunities for AI in Healthcare
Algorithmic Bias:  Challenges and Opportunities for AI in HealthcareAlgorithmic Bias:  Challenges and Opportunities for AI in Healthcare
Algorithmic Bias: Challenges and Opportunities for AI in Healthcare
 
Ethics of Analytics and Machine Learning
Ethics of Analytics and Machine LearningEthics of Analytics and Machine Learning
Ethics of Analytics and Machine Learning
 
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdf
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfUNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdf
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdf
 
Why it’s unethical to focus on ‘AI Ethics’
Why it’s unethical to focus on ‘AI Ethics’Why it’s unethical to focus on ‘AI Ethics’
Why it’s unethical to focus on ‘AI Ethics’
 
Bias in AI
Bias in AIBias in AI
Bias in AI
 
Generative AI and law.pptx
Generative AI and law.pptxGenerative AI and law.pptx
Generative AI and law.pptx
 
Generative AI
Generative AIGenerative AI
Generative AI
 
Responsible AI in Industry (ICML 2021 Tutorial)
Responsible AI in Industry (ICML 2021 Tutorial)Responsible AI in Industry (ICML 2021 Tutorial)
Responsible AI in Industry (ICML 2021 Tutorial)
 
Using the power of Generative AI at scale
Using the power of Generative AI at scaleUsing the power of Generative AI at scale
Using the power of Generative AI at scale
 
Leveraging Generative AI & Best practices
Leveraging Generative AI & Best practicesLeveraging Generative AI & Best practices
Leveraging Generative AI & Best practices
 
How do we train AI to be Ethical and Unbiased?
How do we train AI to be Ethical and Unbiased?How do we train AI to be Ethical and Unbiased?
How do we train AI to be Ethical and Unbiased?
 
Explainable AI in Industry (FAT* 2020 Tutorial)
Explainable AI in Industry (FAT* 2020 Tutorial)Explainable AI in Industry (FAT* 2020 Tutorial)
Explainable AI in Industry (FAT* 2020 Tutorial)
 
Explainability and bias in AI
Explainability and bias in AIExplainability and bias in AI
Explainability and bias in AI
 
Responsible AI
Responsible AIResponsible AI
Responsible AI
 
AI in Manufacturing - John.pdf
AI in Manufacturing - John.pdfAI in Manufacturing - John.pdf
AI in Manufacturing - John.pdf
 
Journey of Generative AI
Journey of Generative AIJourney of Generative AI
Journey of Generative AI
 
GENERATIVE AI, THE FUTURE OF PRODUCTIVITY
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYGENERATIVE AI, THE FUTURE OF PRODUCTIVITY
GENERATIVE AI, THE FUTURE OF PRODUCTIVITY
 
An Introduction to Generative AI
An Introduction  to Generative AIAn Introduction  to Generative AI
An Introduction to Generative AI
 
Artificial Intelligence and Machine Learning
Artificial Intelligence and Machine LearningArtificial Intelligence and Machine Learning
Artificial Intelligence and Machine Learning
 
Generative-AI-in-enterprise-20230615.pdf
Generative-AI-in-enterprise-20230615.pdfGenerative-AI-in-enterprise-20230615.pdf
Generative-AI-in-enterprise-20230615.pdf
 

Ähnlich wie AI Governance and Ethics - Industry Standards

Bias in algorithmic decision-making: Standards, Algorithmic Literacy and Gove...
Bias in algorithmic decision-making: Standards, Algorithmic Literacy and Gove...Bias in algorithmic decision-making: Standards, Algorithmic Literacy and Gove...
Bias in algorithmic decision-making: Standards, Algorithmic Literacy and Gove...Ansgar Koene
 
Industry Standards as vehicle to address socio-technical AI challenges
Industry Standards as vehicle to address socio-technical AI challengesIndustry Standards as vehicle to address socio-technical AI challenges
Industry Standards as vehicle to address socio-technical AI challengesAnsgar Koene
 
A REVIEW OF THE ETHICS OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN THE...
A REVIEW OF THE ETHICS OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN THE...A REVIEW OF THE ETHICS OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN THE...
A REVIEW OF THE ETHICS OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN THE...IJCI JOURNAL
 
Taming AI Engineering Ethics and Policy
Taming AI Engineering Ethics and PolicyTaming AI Engineering Ethics and Policy
Taming AI Engineering Ethics and PolicyAnsgar Koene
 
Emerging Technologies in Data Sharing and Analytics at Data61
Emerging Technologies in Data Sharing and Analytics at Data61Emerging Technologies in Data Sharing and Analytics at Data61
Emerging Technologies in Data Sharing and Analytics at Data61Liming Zhu
 
e-SIDES workshop at BDV Meet-Up, Sofia 14/05/2018
e-SIDES workshop at BDV Meet-Up, Sofia 14/05/2018e-SIDES workshop at BDV Meet-Up, Sofia 14/05/2018
e-SIDES workshop at BDV Meet-Up, Sofia 14/05/2018e-SIDES.eu
 
What regulation for Artificial Intelligence?
What regulation for Artificial Intelligence?What regulation for Artificial Intelligence?
What regulation for Artificial Intelligence?Nozha Boujemaa
 
Ethical Dimensions of Artificial Intelligence (AI) by Rinshad Choorappara
Ethical Dimensions of Artificial Intelligence (AI) by Rinshad ChoorapparaEthical Dimensions of Artificial Intelligence (AI) by Rinshad Choorappara
Ethical Dimensions of Artificial Intelligence (AI) by Rinshad ChoorapparaRinshad Choorappara
 
A koene un_bias_ieee_ebdvf_nov2017
A koene un_bias_ieee_ebdvf_nov2017A koene un_bias_ieee_ebdvf_nov2017
A koene un_bias_ieee_ebdvf_nov2017Ansgar Koene
 
[DSC Europe 23] Bunmi Akinremi - Ethical Considerations in Predictive Analytics
[DSC Europe 23] Bunmi Akinremi - Ethical Considerations in Predictive Analytics[DSC Europe 23] Bunmi Akinremi - Ethical Considerations in Predictive Analytics
[DSC Europe 23] Bunmi Akinremi - Ethical Considerations in Predictive AnalyticsDataScienceConferenc1
 
Digital Forensics for Artificial Intelligence (AI ) Systems.pdf
Digital Forensics for Artificial Intelligence (AI ) Systems.pdfDigital Forensics for Artificial Intelligence (AI ) Systems.pdf
Digital Forensics for Artificial Intelligence (AI ) Systems.pdfMahdi_Fahmideh
 
AI NOW REPORT 2018
AI NOW REPORT 2018AI NOW REPORT 2018
AI NOW REPORT 2018Peerasak C.
 
Ansgar rcep algorithmic_bias_july2018
Ansgar rcep algorithmic_bias_july2018Ansgar rcep algorithmic_bias_july2018
Ansgar rcep algorithmic_bias_july2018Ansgar Koene
 
Algorithmically Mediated Online Inforamtion Access workshop at WebSci17
Algorithmically Mediated Online Inforamtion Access workshop at WebSci17Algorithmically Mediated Online Inforamtion Access workshop at WebSci17
Algorithmically Mediated Online Inforamtion Access workshop at WebSci17Ansgar Koene
 
Data at the centre of a complex world
Data at the centre of a complex world Data at the centre of a complex world
Data at the centre of a complex world Kate Carruthers
 
Ethics In DW & DM
Ethics In DW & DMEthics In DW & DM
Ethics In DW & DMabethan
 

Ähnlich wie AI Governance and Ethics - Industry Standards (20)

Bias in algorithmic decision-making: Standards, Algorithmic Literacy and Gove...
Bias in algorithmic decision-making: Standards, Algorithmic Literacy and Gove...Bias in algorithmic decision-making: Standards, Algorithmic Literacy and Gove...
Bias in algorithmic decision-making: Standards, Algorithmic Literacy and Gove...
 
Industry Standards as vehicle to address socio-technical AI challenges
Industry Standards as vehicle to address socio-technical AI challengesIndustry Standards as vehicle to address socio-technical AI challenges
Industry Standards as vehicle to address socio-technical AI challenges
 
A REVIEW OF THE ETHICS OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN THE...
A REVIEW OF THE ETHICS OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN THE...A REVIEW OF THE ETHICS OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN THE...
A REVIEW OF THE ETHICS OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN THE...
 
Taming AI Engineering Ethics and Policy
Taming AI Engineering Ethics and PolicyTaming AI Engineering Ethics and Policy
Taming AI Engineering Ethics and Policy
 
Emerging Technologies in Data Sharing and Analytics at Data61
Emerging Technologies in Data Sharing and Analytics at Data61Emerging Technologies in Data Sharing and Analytics at Data61
Emerging Technologies in Data Sharing and Analytics at Data61
 
e-SIDES workshop at BDV Meet-Up, Sofia 14/05/2018
e-SIDES workshop at BDV Meet-Up, Sofia 14/05/2018e-SIDES workshop at BDV Meet-Up, Sofia 14/05/2018
e-SIDES workshop at BDV Meet-Up, Sofia 14/05/2018
 
What regulation for Artificial Intelligence?
What regulation for Artificial Intelligence?What regulation for Artificial Intelligence?
What regulation for Artificial Intelligence?
 
Ai Now institute 2017 report
 Ai Now institute 2017 report Ai Now institute 2017 report
Ai Now institute 2017 report
 
Ethical Dimensions of Artificial Intelligence (AI) by Rinshad Choorappara
Ethical Dimensions of Artificial Intelligence (AI) by Rinshad ChoorapparaEthical Dimensions of Artificial Intelligence (AI) by Rinshad Choorappara
Ethical Dimensions of Artificial Intelligence (AI) by Rinshad Choorappara
 
A koene un_bias_ieee_ebdvf_nov2017
A koene un_bias_ieee_ebdvf_nov2017A koene un_bias_ieee_ebdvf_nov2017
A koene un_bias_ieee_ebdvf_nov2017
 
[DSC Europe 23] Bunmi Akinremi - Ethical Considerations in Predictive Analytics
[DSC Europe 23] Bunmi Akinremi - Ethical Considerations in Predictive Analytics[DSC Europe 23] Bunmi Akinremi - Ethical Considerations in Predictive Analytics
[DSC Europe 23] Bunmi Akinremi - Ethical Considerations in Predictive Analytics
 
Ai in compliance
Ai in compliance Ai in compliance
Ai in compliance
 
Digital Forensics for Artificial Intelligence (AI ) Systems.pdf
Digital Forensics for Artificial Intelligence (AI ) Systems.pdfDigital Forensics for Artificial Intelligence (AI ) Systems.pdf
Digital Forensics for Artificial Intelligence (AI ) Systems.pdf
 
AI NOW REPORT 2018
AI NOW REPORT 2018AI NOW REPORT 2018
AI NOW REPORT 2018
 
Ansgar rcep algorithmic_bias_july2018
Ansgar rcep algorithmic_bias_july2018Ansgar rcep algorithmic_bias_july2018
Ansgar rcep algorithmic_bias_july2018
 
Regulating Artificial Intelligence (AI).pptx
Regulating Artificial Intelligence (AI).pptxRegulating Artificial Intelligence (AI).pptx
Regulating Artificial Intelligence (AI).pptx
 
Regulating Artificial Intelligence (AI).pptx
Regulating Artificial Intelligence (AI).pptxRegulating Artificial Intelligence (AI).pptx
Regulating Artificial Intelligence (AI).pptx
 
Algorithmically Mediated Online Inforamtion Access workshop at WebSci17
Algorithmically Mediated Online Inforamtion Access workshop at WebSci17Algorithmically Mediated Online Inforamtion Access workshop at WebSci17
Algorithmically Mediated Online Inforamtion Access workshop at WebSci17
 
Data at the centre of a complex world
Data at the centre of a complex world Data at the centre of a complex world
Data at the centre of a complex world
 
Ethics In DW & DM
Ethics In DW & DMEthics In DW & DM
Ethics In DW & DM
 

Mehr von Ansgar Koene

Algorithms of Online Platforms and Networks
Algorithms of Online Platforms and NetworksAlgorithms of Online Platforms and Networks
Algorithms of Online Platforms and NetworksAnsgar Koene
 
A koene governance_framework_algorithmicaccountabilitytransparency_october2018
A koene governance_framework_algorithmicaccountabilitytransparency_october2018A koene governance_framework_algorithmicaccountabilitytransparency_october2018
A koene governance_framework_algorithmicaccountabilitytransparency_october2018Ansgar Koene
 
AI and us communicating for algorithmic bias awareness
AI and us communicating for algorithmic bias awarenessAI and us communicating for algorithmic bias awareness
AI and us communicating for algorithmic bias awarenessAnsgar Koene
 
IEEE P7003 Algorithmic Bias Considerations
IEEE P7003 Algorithmic Bias ConsiderationsIEEE P7003 Algorithmic Bias Considerations
IEEE P7003 Algorithmic Bias ConsiderationsAnsgar Koene
 
IEEE P7003 at ICSE Fairware 2018
IEEE P7003 at ICSE Fairware 2018IEEE P7003 at ICSE Fairware 2018
IEEE P7003 at ICSE Fairware 2018Ansgar Koene
 
iConference 2018 BIAS workshop keynote
iConference 2018 BIAS workshop keynoteiConference 2018 BIAS workshop keynote
iConference 2018 BIAS workshop keynoteAnsgar Koene
 
A koene humaint_march2018
A koene humaint_march2018A koene humaint_march2018
A koene humaint_march2018Ansgar Koene
 
A koene intersectionality_algorithmic_discrimination_dec2017
A koene intersectionality_algorithmic_discrimination_dec2017A koene intersectionality_algorithmic_discrimination_dec2017
A koene intersectionality_algorithmic_discrimination_dec2017Ansgar Koene
 
A koene ai_in_command_control
A koene ai_in_command_controlA koene ai_in_command_control
A koene ai_in_command_controlAnsgar Koene
 
Algorithmically Mediated Online Inforamtion Access at MozFest17
Algorithmically Mediated Online Inforamtion Access at MozFest17Algorithmically Mediated Online Inforamtion Access at MozFest17
Algorithmically Mediated Online Inforamtion Access at MozFest17Ansgar Koene
 
The Age of Algorithms
The Age of AlgorithmsThe Age of Algorithms
The Age of AlgorithmsAnsgar Koene
 
Human Agency on Algorithmic Systems
Human Agency on Algorithmic SystemsHuman Agency on Algorithmic Systems
Human Agency on Algorithmic SystemsAnsgar Koene
 
Editorial responsibilities arising from personalisation algorithms
Editorial responsibilities arising from personalisation algorithmsEditorial responsibilities arising from personalisation algorithms
Editorial responsibilities arising from personalisation algorithmsAnsgar Koene
 
TRILcon'17 confernece workshop presentation on UnBias stakeholder engagement
TRILcon'17 confernece workshop presentation on UnBias stakeholder engagementTRILcon'17 confernece workshop presentation on UnBias stakeholder engagement
TRILcon'17 confernece workshop presentation on UnBias stakeholder engagementAnsgar Koene
 
Young people's policy recommendations on algorithm fairness web sci17
Young people's policy recommendations on algorithm fairness web sci17Young people's policy recommendations on algorithm fairness web sci17
Young people's policy recommendations on algorithm fairness web sci17Ansgar Koene
 
A koene Rebooting The Expert Petcha Kutcha 2017
A koene Rebooting The Expert Petcha Kutcha 2017A koene Rebooting The Expert Petcha Kutcha 2017
A koene Rebooting The Expert Petcha Kutcha 2017Ansgar Koene
 
Internet Society (ISOC Uk England) Webinar on User Trust
Internet Society (ISOC Uk England) Webinar on User TrustInternet Society (ISOC Uk England) Webinar on User Trust
Internet Society (ISOC Uk England) Webinar on User TrustAnsgar Koene
 
are algorithms really a black box
are algorithms really a black boxare algorithms really a black box
are algorithms really a black boxAnsgar Koene
 
Explorers fair talk who_isincontrol_you_thealgorithm
Explorers fair talk who_isincontrol_you_thealgorithmExplorers fair talk who_isincontrol_you_thealgorithm
Explorers fair talk who_isincontrol_you_thealgorithmAnsgar Koene
 

Mehr von Ansgar Koene (20)

What is AI?
What is AI?What is AI?
What is AI?
 
Algorithms of Online Platforms and Networks
Algorithms of Online Platforms and NetworksAlgorithms of Online Platforms and Networks
Algorithms of Online Platforms and Networks
 
A koene governance_framework_algorithmicaccountabilitytransparency_october2018
A koene governance_framework_algorithmicaccountabilitytransparency_october2018A koene governance_framework_algorithmicaccountabilitytransparency_october2018
A koene governance_framework_algorithmicaccountabilitytransparency_october2018
 
AI and us communicating for algorithmic bias awareness
AI and us communicating for algorithmic bias awarenessAI and us communicating for algorithmic bias awareness
AI and us communicating for algorithmic bias awareness
 
IEEE P7003 Algorithmic Bias Considerations
IEEE P7003 Algorithmic Bias ConsiderationsIEEE P7003 Algorithmic Bias Considerations
IEEE P7003 Algorithmic Bias Considerations
 
IEEE P7003 at ICSE Fairware 2018
IEEE P7003 at ICSE Fairware 2018IEEE P7003 at ICSE Fairware 2018
IEEE P7003 at ICSE Fairware 2018
 
iConference 2018 BIAS workshop keynote
iConference 2018 BIAS workshop keynoteiConference 2018 BIAS workshop keynote
iConference 2018 BIAS workshop keynote
 
A koene humaint_march2018
A koene humaint_march2018A koene humaint_march2018
A koene humaint_march2018
 
A koene intersectionality_algorithmic_discrimination_dec2017
A koene intersectionality_algorithmic_discrimination_dec2017A koene intersectionality_algorithmic_discrimination_dec2017
A koene intersectionality_algorithmic_discrimination_dec2017
 
A koene ai_in_command_control
A koene ai_in_command_controlA koene ai_in_command_control
A koene ai_in_command_control
 
Algorithmically Mediated Online Inforamtion Access at MozFest17
Algorithmically Mediated Online Inforamtion Access at MozFest17Algorithmically Mediated Online Inforamtion Access at MozFest17
Algorithmically Mediated Online Inforamtion Access at MozFest17
 
The Age of Algorithms
The Age of AlgorithmsThe Age of Algorithms
The Age of Algorithms
 
Human Agency on Algorithmic Systems
Human Agency on Algorithmic SystemsHuman Agency on Algorithmic Systems
Human Agency on Algorithmic Systems
 
Editorial responsibilities arising from personalisation algorithms
Editorial responsibilities arising from personalisation algorithmsEditorial responsibilities arising from personalisation algorithms
Editorial responsibilities arising from personalisation algorithms
 
TRILcon'17 confernece workshop presentation on UnBias stakeholder engagement
TRILcon'17 confernece workshop presentation on UnBias stakeholder engagementTRILcon'17 confernece workshop presentation on UnBias stakeholder engagement
TRILcon'17 confernece workshop presentation on UnBias stakeholder engagement
 
Young people's policy recommendations on algorithm fairness web sci17
Young people's policy recommendations on algorithm fairness web sci17Young people's policy recommendations on algorithm fairness web sci17
Young people's policy recommendations on algorithm fairness web sci17
 
A koene Rebooting The Expert Petcha Kutcha 2017
A koene Rebooting The Expert Petcha Kutcha 2017A koene Rebooting The Expert Petcha Kutcha 2017
A koene Rebooting The Expert Petcha Kutcha 2017
 
Internet Society (ISOC Uk England) Webinar on User Trust
Internet Society (ISOC Uk England) Webinar on User TrustInternet Society (ISOC Uk England) Webinar on User Trust
Internet Society (ISOC Uk England) Webinar on User Trust
 
are algorithms really a black box
are algorithms really a black boxare algorithms really a black box
are algorithms really a black box
 
Explorers fair talk who_isincontrol_you_thealgorithm
Explorers fair talk who_isincontrol_you_thealgorithmExplorers fair talk who_isincontrol_you_thealgorithm
Explorers fair talk who_isincontrol_you_thealgorithm
 

Kürzlich hochgeladen

Introduction to Prompt Engineering (Focusing on ChatGPT)
Introduction to Prompt Engineering (Focusing on ChatGPT)Introduction to Prompt Engineering (Focusing on ChatGPT)
Introduction to Prompt Engineering (Focusing on ChatGPT)Chameera Dedduwage
 
Presentation on Engagement in Book Clubs
Presentation on Engagement in Book ClubsPresentation on Engagement in Book Clubs
Presentation on Engagement in Book Clubssamaasim06
 
BDSM⚡Call Girls in Sector 97 Noida Escorts >༒8448380779 Escort Service
BDSM⚡Call Girls in Sector 97 Noida Escorts >༒8448380779 Escort ServiceBDSM⚡Call Girls in Sector 97 Noida Escorts >༒8448380779 Escort Service
BDSM⚡Call Girls in Sector 97 Noida Escorts >༒8448380779 Escort ServiceDelhi Call girls
 
Re-membering the Bard: Revisiting The Compleat Wrks of Wllm Shkspr (Abridged)...
Re-membering the Bard: Revisiting The Compleat Wrks of Wllm Shkspr (Abridged)...Re-membering the Bard: Revisiting The Compleat Wrks of Wllm Shkspr (Abridged)...
Re-membering the Bard: Revisiting The Compleat Wrks of Wllm Shkspr (Abridged)...Hasting Chen
 
Mohammad_Alnahdi_Oral_Presentation_Assignment.pptx
Mohammad_Alnahdi_Oral_Presentation_Assignment.pptxMohammad_Alnahdi_Oral_Presentation_Assignment.pptx
Mohammad_Alnahdi_Oral_Presentation_Assignment.pptxmohammadalnahdi22
 
Report Writing Webinar Training
Report Writing Webinar TrainingReport Writing Webinar Training
Report Writing Webinar TrainingKylaCullinane
 
lONG QUESTION ANSWER PAKISTAN STUDIES10.
lONG QUESTION ANSWER PAKISTAN STUDIES10.lONG QUESTION ANSWER PAKISTAN STUDIES10.
lONG QUESTION ANSWER PAKISTAN STUDIES10.lodhisaajjda
 
AWS Data Engineer Associate (DEA-C01) Exam Dumps 2024.pdf
AWS Data Engineer Associate (DEA-C01) Exam Dumps 2024.pdfAWS Data Engineer Associate (DEA-C01) Exam Dumps 2024.pdf
AWS Data Engineer Associate (DEA-C01) Exam Dumps 2024.pdfSkillCertProExams
 
Chiulli_Aurora_Oman_Raffaele_Beowulf.pptx
Chiulli_Aurora_Oman_Raffaele_Beowulf.pptxChiulli_Aurora_Oman_Raffaele_Beowulf.pptx
Chiulli_Aurora_Oman_Raffaele_Beowulf.pptxraffaeleoman
 
Busty Desi⚡Call Girls in Sector 51 Noida Escorts >༒8448380779 Escort Service-...
Busty Desi⚡Call Girls in Sector 51 Noida Escorts >༒8448380779 Escort Service-...Busty Desi⚡Call Girls in Sector 51 Noida Escorts >༒8448380779 Escort Service-...
Busty Desi⚡Call Girls in Sector 51 Noida Escorts >༒8448380779 Escort Service-...Delhi Call girls
 
Dreaming Music Video Treatment _ Project & Portfolio III
Dreaming Music Video Treatment _ Project & Portfolio IIIDreaming Music Video Treatment _ Project & Portfolio III
Dreaming Music Video Treatment _ Project & Portfolio IIINhPhngng3
 
If this Giant Must Walk: A Manifesto for a New Nigeria
If this Giant Must Walk: A Manifesto for a New NigeriaIf this Giant Must Walk: A Manifesto for a New Nigeria
If this Giant Must Walk: A Manifesto for a New NigeriaKayode Fayemi
 
SaaStr Workshop Wednesday w/ Lucas Price, Yardstick
SaaStr Workshop Wednesday w/ Lucas Price, YardstickSaaStr Workshop Wednesday w/ Lucas Price, Yardstick
SaaStr Workshop Wednesday w/ Lucas Price, Yardsticksaastr
 
Call Girl Number in Khar Mumbai📲 9892124323 💞 Full Night Enjoy
Call Girl Number in Khar Mumbai📲 9892124323 💞 Full Night EnjoyCall Girl Number in Khar Mumbai📲 9892124323 💞 Full Night Enjoy
Call Girl Number in Khar Mumbai📲 9892124323 💞 Full Night EnjoyPooja Nehwal
 
Air breathing and respiratory adaptations in diver animals
Air breathing and respiratory adaptations in diver animalsAir breathing and respiratory adaptations in diver animals
Air breathing and respiratory adaptations in diver animalsaqsarehman5055
 
Dreaming Marissa Sánchez Music Video Treatment
Dreaming Marissa Sánchez Music Video TreatmentDreaming Marissa Sánchez Music Video Treatment
Dreaming Marissa Sánchez Music Video Treatmentnswingard
 
The workplace ecosystem of the future 24.4.2024 Fabritius_share ii.pdf
The workplace ecosystem of the future 24.4.2024 Fabritius_share ii.pdfThe workplace ecosystem of the future 24.4.2024 Fabritius_share ii.pdf
The workplace ecosystem of the future 24.4.2024 Fabritius_share ii.pdfSenaatti-kiinteistöt
 
My Presentation "In Your Hands" by Halle Bailey
My Presentation "In Your Hands" by Halle BaileyMy Presentation "In Your Hands" by Halle Bailey
My Presentation "In Your Hands" by Halle Baileyhlharris
 
Thirunelveli call girls Tamil escorts 7877702510
Thirunelveli call girls Tamil escorts 7877702510Thirunelveli call girls Tamil escorts 7877702510
Thirunelveli call girls Tamil escorts 7877702510Vipesco
 

Kürzlich hochgeladen (20)

Introduction to Prompt Engineering (Focusing on ChatGPT)
Introduction to Prompt Engineering (Focusing on ChatGPT)Introduction to Prompt Engineering (Focusing on ChatGPT)
Introduction to Prompt Engineering (Focusing on ChatGPT)
 
Presentation on Engagement in Book Clubs
Presentation on Engagement in Book ClubsPresentation on Engagement in Book Clubs
Presentation on Engagement in Book Clubs
 
BDSM⚡Call Girls in Sector 97 Noida Escorts >༒8448380779 Escort Service
BDSM⚡Call Girls in Sector 97 Noida Escorts >༒8448380779 Escort ServiceBDSM⚡Call Girls in Sector 97 Noida Escorts >༒8448380779 Escort Service
BDSM⚡Call Girls in Sector 97 Noida Escorts >༒8448380779 Escort Service
 
Re-membering the Bard: Revisiting The Compleat Wrks of Wllm Shkspr (Abridged)...
Re-membering the Bard: Revisiting The Compleat Wrks of Wllm Shkspr (Abridged)...Re-membering the Bard: Revisiting The Compleat Wrks of Wllm Shkspr (Abridged)...
Re-membering the Bard: Revisiting The Compleat Wrks of Wllm Shkspr (Abridged)...
 
Mohammad_Alnahdi_Oral_Presentation_Assignment.pptx
Mohammad_Alnahdi_Oral_Presentation_Assignment.pptxMohammad_Alnahdi_Oral_Presentation_Assignment.pptx
Mohammad_Alnahdi_Oral_Presentation_Assignment.pptx
 
Report Writing Webinar Training
Report Writing Webinar TrainingReport Writing Webinar Training
Report Writing Webinar Training
 
lONG QUESTION ANSWER PAKISTAN STUDIES10.
lONG QUESTION ANSWER PAKISTAN STUDIES10.lONG QUESTION ANSWER PAKISTAN STUDIES10.
lONG QUESTION ANSWER PAKISTAN STUDIES10.
 
AWS Data Engineer Associate (DEA-C01) Exam Dumps 2024.pdf
AWS Data Engineer Associate (DEA-C01) Exam Dumps 2024.pdfAWS Data Engineer Associate (DEA-C01) Exam Dumps 2024.pdf
AWS Data Engineer Associate (DEA-C01) Exam Dumps 2024.pdf
 
Chiulli_Aurora_Oman_Raffaele_Beowulf.pptx
Chiulli_Aurora_Oman_Raffaele_Beowulf.pptxChiulli_Aurora_Oman_Raffaele_Beowulf.pptx
Chiulli_Aurora_Oman_Raffaele_Beowulf.pptx
 
Busty Desi⚡Call Girls in Sector 51 Noida Escorts >༒8448380779 Escort Service-...
Busty Desi⚡Call Girls in Sector 51 Noida Escorts >༒8448380779 Escort Service-...Busty Desi⚡Call Girls in Sector 51 Noida Escorts >༒8448380779 Escort Service-...
Busty Desi⚡Call Girls in Sector 51 Noida Escorts >༒8448380779 Escort Service-...
 
Dreaming Music Video Treatment _ Project & Portfolio III
Dreaming Music Video Treatment _ Project & Portfolio IIIDreaming Music Video Treatment _ Project & Portfolio III
Dreaming Music Video Treatment _ Project & Portfolio III
 
If this Giant Must Walk: A Manifesto for a New Nigeria
If this Giant Must Walk: A Manifesto for a New NigeriaIf this Giant Must Walk: A Manifesto for a New Nigeria
If this Giant Must Walk: A Manifesto for a New Nigeria
 
SaaStr Workshop Wednesday w/ Lucas Price, Yardstick
SaaStr Workshop Wednesday w/ Lucas Price, YardstickSaaStr Workshop Wednesday w/ Lucas Price, Yardstick
SaaStr Workshop Wednesday w/ Lucas Price, Yardstick
 
Call Girl Number in Khar Mumbai📲 9892124323 💞 Full Night Enjoy
Call Girl Number in Khar Mumbai📲 9892124323 💞 Full Night EnjoyCall Girl Number in Khar Mumbai📲 9892124323 💞 Full Night Enjoy
Call Girl Number in Khar Mumbai📲 9892124323 💞 Full Night Enjoy
 
Air breathing and respiratory adaptations in diver animals
Air breathing and respiratory adaptations in diver animalsAir breathing and respiratory adaptations in diver animals
Air breathing and respiratory adaptations in diver animals
 
Dreaming Marissa Sánchez Music Video Treatment
Dreaming Marissa Sánchez Music Video TreatmentDreaming Marissa Sánchez Music Video Treatment
Dreaming Marissa Sánchez Music Video Treatment
 
The workplace ecosystem of the future 24.4.2024 Fabritius_share ii.pdf
The workplace ecosystem of the future 24.4.2024 Fabritius_share ii.pdfThe workplace ecosystem of the future 24.4.2024 Fabritius_share ii.pdf
The workplace ecosystem of the future 24.4.2024 Fabritius_share ii.pdf
 
ICT role in 21st century education and it's challenges.pdf
ICT role in 21st century education and it's challenges.pdfICT role in 21st century education and it's challenges.pdf
ICT role in 21st century education and it's challenges.pdf
 
My Presentation "In Your Hands" by Halle Bailey
My Presentation "In Your Hands" by Halle BaileyMy Presentation "In Your Hands" by Halle Bailey
My Presentation "In Your Hands" by Halle Bailey
 
Thirunelveli call girls Tamil escorts 7877702510
Thirunelveli call girls Tamil escorts 7877702510Thirunelveli call girls Tamil escorts 7877702510
Thirunelveli call girls Tamil escorts 7877702510
 

AI Governance and Ethics - Industry Standards

  • 1. AI Governance and Ethics: Industry Standards as vehicle to address socio-technical challenges from AI Ansgar Koene, University of Nottingham IEEE P7003 Standard for Algorithmic Bias Considerations
  • 2. Undesired impact The need for ethics, legal, social, economic intervention
  • 4. 3 Algorithmic Discrimination Machine Bias: There’s software used across the country to predict future criminals. Propublica
  • 5. Algorithmic systems are socio-technical ▸Algorithmic systems do not exist in a vacuum ▸They are built, deployed and used: - by people, - within organizations, - within a social, political, legal and cultural context. ▸The outcomes of algorithmic decisions can have significant impacts on real, and possibly vulnerable, people. 4
  • 6. Governance frameworks for algorithmic systems Public and Private sector responses to Undesired impacts of AI
  • 7. EU response (in addition to GDPR) EU Parliament Science and Technology Options Assessment (STOA) panel request for study on “Algorithmic Opportunities and Accountability” 6
  • 8. Governance options 7 Florian Saurwein, Natascha Just, Michael Latzer, (2015) "Governance of algorithms: options and limitations", info, Vol. 17 Issue: 6, pp.35-49, doi: 10.1108/info-05-2015- 0025
  • 9. Demand side Market solutions 8
  • 10. Supply side Market solutions 9
  • 13. Branches self-regulations: Industry standards ▸British Standards Institute (BSI) – BS 8611 Ethics design and application of robots ▸IEEE P70xx Ethics of Autonomous and Intelligent Systems standards ▸ISO/IEC JTC1 SC42 (launched at start of 2018) - Artificial Intelligence Concepts and Terminology - Framework for Artificial Intelligence Systems Using Machine Learning • SG 2 on Trustworthiness  transparency, verifiability, explainability, controllability, etc.  robustness, resiliency, reliability, accuracy, safety, security, privacy, etc. ▸The EU standards bodies CEN and CENELEC officially created a Focus Group on AI, in support of ISO/IEC SC42 (December 2018). ▸Jan 2018 China published “Artificial Intelligence Standardization White Paper.” 12
  • 16. Oversight by regulatory organisations ▸An FDA for algorithms – Andrew Tutt (2016) ▸An FAA for algorithms – paraphrasing Alan Winfield (Chair of IEEE P7001 Standard for Algorithm Transparency) 15
  • 18. Industry Standards IEEE P70xx Standards, developed as part of the Global Initiative for Ethics of Autonomous and Intelligent Systems 17
  • 19. 18
  • 20. IEEE P70xx Standards Projects IEEE P7000: Model Process for Addressing Ethical Concerns During System Design IEEE P7001: Transparency of Autonomous Systems IEEE P7002: Data Privacy Process IEEE P7003: Algorithmic Bias Considerations IEEE P7004: Child and Student Data Governance IEEE P7005: Employer Data Governance IEEE P7006: Personal Data AI Agent Working Group IEEE P7007: Ontological Standard for Ethically Driven Robotics and Automation Systems IEEE P7008: Ethically Driven Nudging for Robotic, Intelligent and Autonomous Systems IEEE P7009: Fail-Safe Design of Autonomous and Semi-Autonomous Systems IEEE P7010: Wellbeing Metrics Standard for Ethical AI and Autonomous Systems IEEE P7011: Process of Identifying and Rating the Trustworthiness of News Sources IEEE P7012: Standard for Machines Readable Personal Privacy Terms IEEE P7013: Inclusion and Application Standards for Automated Facial Analysis Technology 19
  • 21.
  • 22. P7003 - Algorithmic Bias Considerations ▸All non-trivial* decisions are biased ▸We seek to minimize bias that is: - Unintended - Unjustified - Unacceptable ▸as defined by the context where the system is used. *Non-trivial means the decision space has more than one possible outcome and the choice is not uniformly random.
  • 23. Causes of algorithmic bias ▸Insufficient understanding of the context of use. ▸Failure to rigorously map decision criteria. ▸Failure to have explicit justifications for the chosen criteria.
  • 24. Key question when developing or deploying an algorithmic system 23  Who will be affected?  What are the decision/optimization criteria?  How are these criteria justified?  Are these justifications acceptable in the context where the system is used?
  • 25. IEEE P7003 general structure 24
  • 26. 5Rights Universal Standards for childhood and adolescence A collaboration between 5Rights and IEEE-SA 25
  • 27. Why these Standards are needed ▸That presence of children in the digital environment must be anticipated - by design - by default ▸Technology must be provided in children in a way that upholds their rights and meets their needs. - Not simply about content - The ways it uses their data - The behaviours it encourages - The responsibility it takes for the impact of its services. 26
  • 28. The approach Look systemically for the drivers and inhibitors in technological systems, to create a good environment for child ▸How does the system impact on, or promote the autonomy of a child? ▸What effect might a child’s engagement have on their health or well being? ▸Have you considered both their physical and emotional wellbeing? ▸What processes are in place to inform the child of the likely impact of using your service? From the big ‘is it fair’ and ‘does it uphold the rights of the child’ to the entirely granular about where on the screen a button might be better placed. 27
  • 29. A suite of Standards/guidance - industry connections group ▸Age Appropriate Contract – that is to determine what terms and conditions, or community rules should offer when the end user is a child. ▸Standards that cover: - security of IoT, - Child Online Protection Issues, - Privacy differentials, - context capacity authentication, - guidance for duty of care, - appropriate governance structures, - reporting standards, - flagging systems, - data minimization standards, - best practice geolocation - etc. 28
  • 30. Interplay between Standards and Legislation ▸5rights Founder and Chair, Baroness Beeban Kidron is the architect of ground-breaking new Age Appropriate Design Code, an enhanced GDPR for children under 18. ▸5Rights as an organization, believes that standards should both anticipate and be an alternative to legislation. ▸Creating standards from a trusted source allows all businesses – small and big – access to the thoughtful and ethical digital services for children. 29
  • 31. Thank you! ansgar.koene@Nottingham.ac.uk IEEE P7003 Standard for Algorithmic Bias Considerations project site: http://sites.ieee.org/sagroups-7003/ https://5rightsfoundation.com/

Hinweis der Redaktion

  1. Automated decisions are not defined by algorithms alone. Rather, they emerge from automated systems that mix human judgment, conventional software, and statistical models, all designed to serve human goals and purposes. Discerning and debating the social impact of these systems requires a holistic approach that considers: Computational and statistical aspects of the algorithmic processing; Power dynamics between the service provider and the customer; The social-political-legal-cultural context within which the system is used;
  2. All non-trivial decisions are biased. For example, a good results from a search engine should be biased to match the interests of the user as expressed by the search-term, and possibly refined based on personalization data. When we say we want ‘no Bias’ we mean we want to minimize unintended, unjustified and unacceptable bias, as defined by the context within which the algorithmic system is being used.
  3. In the absence of malicious intent, bias in algorithmic system is generally caused by: Insufficient understanding of the context that the system is part of. This includes lack of understanding who will be affected by the algorithmic decision outcomes, resulting in a failure to test how the system performs for specific groups, who are often minorities. Diversity in the development team can partially help to address this. Failure to rigorously map decision criteria. When people think of algorithmic decisions as being more ‘objectively trustworthy’ than human decisions, more often than not they are referring to the idea that algorithmic systems follow a clearly defined set of criteria with no ‘hidden agenda’. The complexity of system development challenges, however, can easily introduce ‘hidden decision criteria’ introduced as a quick fix during debugging or embedded within Machine Learning training data. Failure to explicitly define and examine the justifications for the decision criteria. Given the context within which the system is used, are these justifications acceptable? For example, in a given context is it OK to treat high correlation as evidence of causation?