Artificial Intelligence: Background, Risks and Policies

✍ Scribed by Gary Dalton

• Year: 2024
• Tongue: English
• Leaves: 280
• Category: Library

✦ Table of Contents

Contents
Preface
Chapter 1
Artificial Intelligence: Background, Selected Issues, and Policy Considerations(
Summary
Introduction
What Is AI?
AI Terminology
Algorithms and AI
Historical Context of AI
Waves of AI
Recent Growth in the Field of AI
AI Research and Development
Private and Public Funding
Selected Research and Focus Areas
Explainable AI
Data Access
AI Training with Small and Alternative Datasets
AI Hardware
Federal Activity in AI
Executive Branch
Executive Orders on AI
National Science and Technology Council Committees
Select AI Reports and Documents
Federal Agency Activities
Congress
Legislation
Hearings
Selected Issues for Congressional Consideration
Implications for the U.S. Workforce
Job Displacement and Skill Shifts
AI Expert Workforce
International Competition and Federal Investment in AI R&D
Standards Development
Ethics, Bias, Fairness, and Transparency
Types of Bias
Chapter 2
Trustworthy AI: Managing the Risks of Artificial Intelligence *
U.S. House of Representatives, Committee on Science, Space, and Technology, Subcommittee on Research and Technology, Hearing Charter, Trustworthy AI: Managing the Risks of Artificial Intelligence
Purpose
Witnesses
Overarching Questions
Background
AI Risks
Harmful Bias
Explainability and Interpretability
Safety
Cybersecurity and Privacy
Computational Costs
Government Action
OSTP
National Institute of Standards and Technology
National Science Foundation
International
Private Sector Action
Testimony of Ms. Elham Tabassi, Chief of staff, Information Technology Laboratory, National Institute of Standards and Technology
Testimony of Elham Tabassi, Chief of Staff, Information Technology Laboratory, National Institute of Standards and Technology, United States Department of Commerce, before the United States House of Representatives, Committee on Science, Space, and Te...
NIST’s Role in Artificial Intelligence
NIST AI Risk Management Framework
NIST’s Research on AI Trustworthiness Characteristics
AI Trustworthiness Characteristics – Fair and Bias is Managed
AI Trustworthiness Characteristics – Explainable and Interpretable
AI Trustworthiness Characteristics –Secure and Resilient
AI Trustworthiness Characteristics – Privacy-enhanced
Research on Applications of AI
AI Measurement and Evaluation
AI Standards
Interagency Coordination
Conclusion
Elham Tabassi (Fed), Chief of Staff, Information Technology Laboratory
Testimony of Dr. Charles Isbell, Dean and John P. Imlay, Jr. Chair of the College of Computing, Georgia Institute of Technology
Testimony of Mr. Jordan Crenshaw, Vice President of the Chamber Technology Engagement Center, U.S. Chamber of Commerce
Before the U.S. House Research And Technology Subcommittee, Hearing on “Trustworthy AI: Managing the Risks of Artificial Intelligence,” Testimony of Jordan Crenshaw, Vice President, C_TEC, U.S. Chamber of Commerce, September 29, 2022
Opportunities for the Federal Government and Industry to Work Together to Develop Trustworthy AI
Congress Needs to Pass a Preemptive National Data Privacy Law
Support for Alternative Regulatory Pathways Such as Voluntary Consensus Standards
Stakeholder Driven Engagement
Awareness of the Benefits of Artificial Intelligence
Awareness of the Benefits of Artificial Intelligence
How Are Different Sectors Adopting Governance Models and Other Strategies to Mitigate Risks that Arise from AI Systems?
How Should the United States Encourage More Organizations to Think Critically about Risks that Arise from AI Systems, Including by Priortiziing Trustworthy AI from the Earliest Stages of Development of New Systems?
What Recommendations Do You Have for how the Federal Government can Strengthen its Role for the Development and Responsible Deployment of Trustworthy AI Systems?
Conclusion
Testimony of Ms. Navrina Singh, Founder and Chief Executive Officer, Credo AI
Prepared Testimony of Navrina Singh, Founder and CEO, Credo AI, before the House Committee on Science, Space and Technology, Subcommittee on Research and Technology
Introduction
What Is Responsible AI?
How to Create an Environment that Fosters RAI
Companies Are Seeking Guidance
Key Challenges to Overcome in the Development and Use of Responsible AI
Context Is Critical: Metrics for Each Tenant of RAI Vary
Addressing Risk Now Ensures Leadership in the Long Run
Conclusion
Appendix I: Answers to Post-Hearing Questions
Appendix II: Additional Material for the Record
Engineered Intelligence: Creating a Successor Species, Congressman Brad Sherman, Statement for the Committee on Science, Space, & Technology, May 17, 2019
Chapter 3
Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People, October 2022*
Foreword
About This Framework
Listening to the American Public
Blueprint for an AI Bill of Rights
Safe and Effective Systems
You Should Be Protected from Unsafe or Ineffective Systems
Algorithmic Discrimination Protections
You Should Not Face Discrimination by Algorithms and Systems Should Be Used and Designed in an Equitable Way
Data Privacy
You Should Be Protected from Abusive Data Practices via Built-In Protections and You Should Have Agency over How Data About You Is Used
Notice and Explanation
You Should Know That an Automated System Is Being Used and Understand How and Why It Contributes to Outcomes That Impact You
Human Alternatives, Consideration, and Fallback
You Should Be Able to Opt out, Where Appropriate, and Have Access to a Person Who Can Quickly Consider and Remedy Problems You Encounter
Applying the Blueprint for an AI Bill of Rights
Rights, Opportunities, or Access
Relationship to Existing Law and Policy
Applying the Blueprint for an AI Bill of Rights
Relationship to Existing Law and Policy
Definitions
Algorithmic Discrimination
Automated System
Communities
Equity
Rights, Opportunities, or Access
Sensitive Data
Sensitive Domains
Surveillance Technology
Underserved Communities
From Principles to Practice: A Techincal Companion to the Blueprint for an AI Bill of Rights
Using This Technical Companion
Safe and Effective Systems
You Should Be Protected from Unsafe or Ineffective Systems
Why This Principle Is Important
What Should Be Expected of Automated Systems
Protect the Public from Harm in a Proactive and Ongoing Manner
Consultation
Testing
Risk Identification and Mitigation
Ongoing Monitoring
Clear Organizational Oversight
Avoid Inappropriate, Low-Quality, or Irrelevant Data Use and the Compounded Harm of Its Reuse
Relevant and High-Quality Data
Derived Data Sources Tracked and Reviewed Carefully
Data Reuse Limits in Sensitive Domains
Demonstrate the Safety and Effectiveness of the System
Independent Evaluation
Reporting
How These Principles Can Move into Practice
Executive Order 13960 on Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government Requires That Certain Federal Agencies Adhere to Nine Principles When Designing, Developing, Acquiring, or Using AI for Purposes Other Than Nat...
The Law and Policy Landscape for Motor Vehicles Shows That Strong Safety Regulations—and Measures to Address Harms When They Occur—Can Enhance Innovation in the Context of Complex Technologies
From Large Companies to Start-Ups, Industry Is Providing Innovative Solutions That Allow Organizations to Mitigate Risks to the Safety and Efficacy of AI Systems, Both before Deployment and through Monitoring over Time
The Office of Management and Budget (OMB) Has Called for an Expansion of Opportunities for Meaningful Stakeholder Engagement in the Design of Programs and Services
The National Institute of Standards and Technology (NIST) Is Developing a Risk Management Framework to Better Manage Risks Posed to Individuals, Organizations, and Society by AI
Some U.S Government Agencies Have Developed Specific Frameworks for Ethical Use of AI Systems
The National Science Foundation (NSF) Funds Extensive Research to Help Foster the Development of Automated Systems That Adhere to and Advance Their Safety, Security and Effectiveness
Some State Legislatures Have Placed Strong Transparency and Validity Requirements on the Use of Pretrial Risk Assessments
Algorithmic Discrimination Protections
You Should Not Face Discrimination by Algorithms and Systems Should Be Used and Designed in an Equitable Way
Why This Principle Is Important
What Should Be Expected of Automated Systems
Protect the Public from Algorithmic Discrimination in a Proactive and Ongoing Manner
Proactive Assessment of Equity in Design
Representative and Robust Data
Guarding against Proxies
Ensuring Accessibility during Design, Development, and Deployment
Disparity Assessment
Disparity Mitigation
Ongoing Monitoring and Mitigation
Demonstrate That the System Protects against Algorithmic Discrimination
Independent Evaluation
Reporting
How These Principles Can Move into Practice
The Federal Government Is Working to Combat Discrimination in Mortgage Lending
The Equal Employment Opportunity Commission and the Department of Justice Have Clearly Laid out How Employers’ Use of AI and Other Automated Systems Can Result in Discrimination against Job Applicants and Employees with disabilities
Disparity Assessments Identified Harms to Black Patients' Healthcare Access
Large Employers Have Developed Best Practices to Scrutinize the Data and Models Used for Hiring
Standards Organizations Have Developed Guidelines to Incorporate Accessibility Criteria into Technology Design Processes
NIST Has Released Special Publication 1270, towards a Standard for Identifying and Managing Bias in Artificial Intelligence
Data Privacy
You Should Be Protected from Abusive Data Practices via Built-in Protections and You Should Have Agency over How Data About You Is Used
Why This Principle Is Important
What Should Be Expected of Automated Systems
Protect Privacy by Design and by Default
Privacy by Design and by Default
Data Collection and Use-Case Scope Limits
Risk Identification and Mitigation
Privacy-Preserving Security
Protect the Public from Unchecked Surveillance
Heightened Oversight of Surveillance
Limited and Proportionate Surveillance
Scope Limits on Surveillance to Protect Rights and Democratic Values
Provide the Public with Mechanisms for Appropriate and Meaningful Consent, Access, and Control over Their Data
Use-Specific Consent
Brief and Direct Consent Requests
Data Access and Correction
Consent Withdrawal and Data Deletion
Automated System Support
Demonstrate That Data Privacy and User Control Are Protected
Independent Evaluation
Reporting
Extra Protections for Data Related to Sensitive Domains
What Should Be Expected of Automated Systems
Provide Enhanced Protections for Data Related to Sensitive Domains
Necessary Functions Only
Ethical Review and Use Prohibitions
Data Quality
Limit Access to Sensitive Data and Derived Data
Reporting
How These Principles Can Move into Practice
The Privacy Act of 1974 Requires Privacy Protections for Personal Information in Federal Records Systems, Including Limits on Data Retention, and Also Provides Individuals a General Right to Access and Correct Their Data
NIST’s Privacy Framework Provides a Comprehensive, Detailed and Actionable Approach for Organizations to Manage Privacy Risks
A School Board’s Attempt to Surveil Public School Students—Undertaken without Adequate Community Input—Sparked a State-Wide Biometrics Moratorium
Federal Law Requires Employers, and Any Consultants They May Retain, to Report the Costs of Surveilling Employees in the Context of a Labor Dispute, Providing a Transparency Mechanism to Help Protect Worker Organizing
Privacy Choices on Smartphones Show That When Technologies Are Well Designed, Privacy and Data Agency Can Be Meaningful and Not Overwhelming
Notice and Explanation
You Should Know That an Automated System Is Being Used, and Understand How and Why It Contributes to Outcomes That Impact You
Why This Principle Is Important
What Should Be Expected of Automated Systems
Provide Clear, Timely, Understandable, and Accessible Notice of Use and Explanations
Generally Accessible Plain Language Documentation
Accountable
Timely and up-to-Date
Brief and Clear
Provide Explanations as to How and Why a Decision Was Made or an Action Was Taken by an Automated System
Tailored to the Purpose
Tailored to the Target of the Explanation
Tailored to the Level of Risk
Valid
Demonstrate Protections for Notice and Explanation
Reporting
How These Principles Can Move into Practice
Real-Life Examples of How These Principles Can Become Reality, Through Laws, Policies, and Practical Technical and Sociotechnical Approaches to Protecting Rights, Opportunities, and Access
People in Illinois Are Given Written Notice by the Private Sector if Their Biometric Information Is Used
Major Technology Companies Are Piloting New Ways to Communicate with the Public About Their Automated Technologies
Lenders Are Required by Federal Law to Notify Consumers About Certain Decisions Made About Them
A California Law Requires That Warehouse Employees Are Provided with Notice and Explanation About Quotas, Potentially Facilitated by Automated Systems, That Apply to Them
Across the Federal Government, Agencies Are Conducting and Supporting Research on Explainable AI Systems
Human Alternatives, Consideration, and Fallback
You Should Be Able to Opt out, Where Appropriate, and Have Access to a Person Who Can Quickly Consider and Remedy Problems You Encounter
Why This Principle Is Important
What Should Be Expected of Automated Systems
Provide a Mechanism to Conveniently Opt out from Automated Systems in Favor of a Human Alternative, Where Appropriate
Brief, Clear, Accessible Notice and Instructions
Human Alternatives Provided When Appropriate
Timely and Not Burdensome Human Alternative
Provide Timely Human Consideration and Remedy by a Fallback and Escalation System in the Event That an Automated System Fails, Produces Error, or You Would Like to Appeal or Contest Its Impacts on You
Proportionate
Accessible
Convenient
Equitable
Timely
Effective
Maintained
Institute Training, Assessment, and Oversight to Combat Automation Bias and Ensure any Human-Based Components of a System Are Effective
Training and Assessment
Oversight
Implement Additional Human Oversight and Safeguards for Automated Systems Related to Sensitive Domains
Narrowly Scoped Data and Inferences
Tailored to the Situation
Human Consideration before Any High-Risk Decision
Meaningful Access to Examine the System
Demonstrate Access to Human Alternatives, Consideration, and Fallback
Reporting
How These Principles Can Move into Practice
Healthcare “Navigators” Help People Find Their Way through Online Signup Forms to Choose and Obtain Healthcare
The Customer Service Industry Has Successfully Integrated Automated Services Such as Chat-Bots and AI-Driven Call Response Systems with Escalation to a Human Support Team
Ballot Curing Laws in at Least 24 States Require a Fallback System That Allows Voters to Correct Their Ballot and Have It Counted in the Case That a Voter Signature Matching Algorithm Incorrectly Flags Their Ballot as Invalid or There Is Another Issue...
Appendix
Examples of Automated Systems
Listening to the American People
Panel Discussions to Inform the Blueprint for an AI Bill of Rights
Summaries of Panel Discussions
Panel 1: Consumer Rights and Protections
Welcome
Moderator
Panelists
Panel 2: The Criminal Justice System
Welcome
Moderator
Panelists
Panel 3: Equal Opportunities and Civil Justice
Welcome
Moderator
Panelists
Panel 4: Artificial Intelligence and Democratic Values
Welcome
Moderator
Panelists
Panel 5: Social Welfare and Development
Welcome
Moderator
Panelists
Panel 6: The Healthcare System
Welcome
Moderator
Panelists
Index
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