Model: danicafisher/dfisher-sentence-transformer-fine-tuned2
Author: danicafisher
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:180, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:1705.00652, base_model:sentence-transformers/all-MiniLM-L6-v2, base_model:finetune:sentence-transformers/all-MiniLM-L6-v2, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: sentence-transformers/all-MiniLM-L6-v2
Card metadata: base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:180, loss:MultipleNegativesRankingLoss
widget: source_sentence: How does the National Institute of Standards and Technology (NIST) plan to address risks associated with AI through its risk management framework?
sentences: APPENDIX
Panel 3: Equal Opportunities and Civil Justice. This event explored current and emerging uses of 
technology that impact equity of opportunity in employment, education, and housing. 
Welcome: 
•
Rashida Richardson, Senior Policy Advisor for Data and Democracy, White House Office of Science and
Technology Policy
•
Dominique Harrison, Director for Technology Policy, The Joint Center for Political and Economic
Studies
Moderator: Jenny Yang, Director, Office of Federal Contract Compliance Programs, Department of Labor 
Panelists: 
•
Christo Wilson, Associate Professor of Computer Science, Northeastern University
•
Frida Polli, CEO, Pymetrics
•
Karen Levy, Assistant Professor, Department of Information Science, Cornell University
•
Natasha Duarte, Project Director, Upturn
•
Elana Zeide, Assistant Professor, University of Nebraska College of Law
•
Fabian Rogers, Constituent Advocate, Office of NY State Senator Jabari Brisport and Community
Advocate and Floor Captain, Atlantic Plaza Towers Tenants Association
The individual panelists described the ways in which AI systems and other technologies are increasingly being 
used to limit access to equal opportunities in education, housing, and employment. Education-related 
concerning uses included the increased use of remote proctoring systems, student location and facial 
recognition tracking, teacher evaluation systems, robot teachers, and more. Housing-related concerning uses 
including automated tenant background screening and facial recognition-based controls to enter or exit 
housing complexes. Employment-related concerning uses included discrimination in automated hiring 
screening and workplace surveillance. Various panelists raised the limitations of existing privacy law as a key 
concern, pointing out that students should be able to reinvent themselves and require privacy of their student 
records and education-related data in order to do so. The overarching concerns of surveillance in these 
domains included concerns about the chilling effects of surveillance on student expression, inappropriate 
control of tenants via surveillance, and the way that surveillance of workers blurs the boundary between work 
and life and exerts extreme and potentially damaging control over workers' lives. Additionally, some panelists 
pointed out ways that data from one situation was misapplied in another in a way that limited people's 
opportunities, for example data from criminal justice settings or previous evictions being used to block further 
access to housing. Throughout, various panelists emphasized that these technologies are being used to shift the 
burden of oversight and efficiency from employers to workers, schools to students, and landlords to tenants, in 
ways that diminish and encroach on equality of opportunity; assessment of these technologies should include 
whether they are genuinely helpful in solving an identified problem. 
In discussion of technical and governance interventions that that are needed to protect against the harms of 
these technologies, panelists individually described the importance of: receiving community input into the 
design and use of technologies, public reporting on crucial elements of these systems, better notice and consent 
procedures that ensure privacy based on context and use case, ability to opt-out of using these systems and 
receive a fallback to a human process, providing explanations of decisions and how these systems work, the 
need for governance including training in using these systems, ensuring the technological use cases are 
genuinely related to the goal task and are locally validated to work, and the need for institution and protection 
of third party audits to ensure systems continue to be accountable and valid. 
57, NOTICE & 
EXPLANATION 
WHY THIS PRINCIPLE IS IMPORTANT
This section provides a brief summary of the problems which the principle seeks to address and protect 
against, including illustrative examples. 
Automated systems now determine opportunities, from employment to credit, and directly shape the American 
public’s experiences, from the courtroom to online classrooms, in ways that profoundly impact people’s lives. But this 
expansive impact is not always visible. An applicant might not know whether a person rejected their resume or a 
hiring algorithm moved them to the bottom of the list. A defendant in the courtroom might not know if a judge deny­
ing their bail is informed by an automated system that labeled them “high risk.” From correcting errors to contesting 
decisions, people are often denied the knowledge they need to address the impact of automated systems on their lives. 
Notice and explanations also serve an important safety and efficacy purpose, allowing experts to verify the reasonable­
ness of a recommendation before enacting it. 
In order to guard against potential harms, the American public needs to know if an automated system is being used. 
Clear, brief, and understandable notice is a prerequisite for achieving the other protections in this framework. Like­
wise, the public is often unable to ascertain how or why an automated system has made a decision or contributed to a 
particular outcome. The decision-making processes of automated systems tend to be opaque, complex, and, therefore, 
unaccountable, whether by design or by omission. These factors can make explanations both more challenging and 
more important, and should not be used as a pretext to avoid explaining important decisions to the people impacted 
by those choices. In the context of automated systems, clear and valid explanations should be recognized as a baseline 
requirement. 
Providing notice has long been a standard practice, and in many cases is a legal requirement, when, for example, 
making a video recording of someone (outside of a law enforcement or national security context). In some cases, such 
as credit, lenders are required to provide notice and explanation to consumers. Techniques used to automate the 
process of explaining such systems are under active research and improvement and such explanations can take many 
forms. Innovative companies and researchers are rising to the challenge and creating and deploying explanatory 
systems that can help the public better understand decisions that impact them. 
While notice and explanation requirements are already in place in some sectors or situations, the American public 
deserve to know consistently and across sectors if an automated system is being used in a way that impacts their rights, 
opportunities, or access. This knowledge should provide confidence in how the public is being treated, and trust in the 
validity and reasonable use of automated systems. 
•
A lawyer representing an older client with disabilities who had been cut off from Medicaid-funded home
health-care assistance couldn't determine why, especially since the decision went against historical access
practices. In a court hearing, the lawyer learned from a witness that the state in which the older client
lived had recently adopted a new algorithm to determine eligibility.83 The lack of a timely explanation made it
harder to understand and contest the decision.
•
A formal child welfare investigation is opened against a parent based on an algorithm and without the parent
ever being notified that data was being collected and used as part of an algorithmic child maltreatment
risk assessment.84 The lack of notice or an explanation makes it harder for those performing child
maltreatment assessments to validate the risk assessment and denies parents knowledge that could help them
contest a decision.
41, SAFE AND EFFECTIVE 
SYSTEMS 
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. ­­
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 national security or 
defense. These principles—while taking into account the sensitive law enforcement and other contexts in which 
the federal government may use AI, as opposed to private sector use of AI—require that AI is: (a) lawful and 
respectful of our Nation’s values; (b) purposeful and performance-driven; (c) accurate, reliable, and effective; (d) 
safe, secure, and resilient; (e) understandable; (f ) responsible and traceable; (g) regularly monitored; (h) transpar-
ent; and, (i) accountable. The Blueprint for an AI Bill of Rights is consistent with the Executive Order. 
Affected agencies across the federal government have released AI use case inventories13 and are implementing 
plans to bring those AI systems into compliance with the Executive Order or retire them. 
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 com-
plex technologies. Cars, like automated digital systems, comprise a complex collection of components. 
The National Highway Traffic Safety Administration,14 through its rigorous standards and independent 
evaluation, helps make sure vehicles on our roads are safe without limiting manufacturers’ ability to 
innovate.15 At the same time, rules of the road are implemented locally to impose contextually appropriate 
requirements on drivers, such as slowing down near schools or playgrounds.16
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.17 These innovative solutions include risk 
assessments, auditing mechanisms, assessment of organizational procedures, dashboards to allow for ongoing 
monitoring, documentation procedures specific to model assessments, and many other strategies that aim to 
mitigate risks posed by the use of AI to companies’ reputation, legal responsibilities, and other product safety 
and effectiveness concerns. 
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. OMB also 
points to numerous examples of effective and proactive stakeholder engagement, including the Community-
Based Participatory Research Program developed by the National Institutes of Health and the participatory 
technology assessments developed by the National Oceanic and Atmospheric Administration.18
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.19 The NIST AI Risk Management Framework, as mandated by Congress, is intended for 
voluntary use to help incorporate trustworthiness considerations into the design, development, use, and 
evaluation of AI products, services, and systems. The NIST framework is being developed through a consensus-
driven, open, transparent, and collaborative process that includes workshops and other opportunities to provide 
input. The NIST framework aims to foster the development of innovative approaches to address 
characteristics of trustworthiness including accuracy, explainability and interpretability, reliability, privacy, 
robustness, safety, security (resilience), and mitigation of unintended and/or harmful bias, as well as of 
harmful 
uses. 
The 
NIST 
framework 
will 
consider 
and 
encompass 
principles 
such 
as 
transparency, accountability, and fairness during pre-design, design and development, deployment, use, 
and testing and evaluation of AI technologies and systems. It is expected to be released in the winter of 2022-23. 
21, source_sentence: How should explanations provided by automated systems be tailored to meet the needs of different audiences?
sentences: You should know that an automated system is being used, 
and understand how and why it contributes to outcomes 
that impact you. Designers, developers, and deployers of automat­
ed systems should provide generally accessible plain language docu­
mentation including clear descriptions of the overall system func­
tioning and the role automation plays, notice that such systems are in 
use, the individual or organization responsible for the system, and ex­
planations of outcomes that are clear, timely, and accessible. Such 
notice should be kept up-to-date and people impacted by the system 
should be notified of significant use case or key functionality chang­
es. You should know how and why an outcome impacting you was de­
termined by an automated system, including when the automated 
system is not the sole input determining the outcome. Automated 
systems should provide explanations that are technically valid, 
meaningful and useful to you and to any operators or others who 
need to understand the system, and calibrated to the level of risk 
based on the context. Reporting that includes summary information 
about these automated systems in plain language and assessments of 
the clarity and quality of the notice and explanations should be made 
public whenever possible.   
NOTICE AND EXPLANATION
40, WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint for the development of additional 
technical standards and practices that are tailored for particular sectors and contexts. 
Demonstrate that the system protects against algorithmic discrimination 
Independent evaluation. As described in the section on Safe and Effective Systems, entities should allow 
independent evaluation of potential algorithmic discrimination caused by automated systems they use or 
oversee. In the case of public sector uses, these independent evaluations should be made public unless law 
enforcement or national security restrictions prevent doing so. Care should be taken to balance individual 
privacy with evaluation data access needs; in many cases, policy-based and/or technological innovations and 
controls allow access to such data without compromising privacy. 
Reporting. Entities responsible for the development or use of automated systems should provide 
reporting of an appropriately designed algorithmic impact assessment,50 with clear specification of who 
performs the assessment, who evaluates the system, and how corrective actions are taken (if necessary) in 
response to the assessment. This algorithmic impact assessment should include at least: the results of any 
consultation, design stage equity assessments (potentially including qualitative analysis), accessibility 
designs and testing, disparity testing, document any remaining disparities, and detail any mitigation 
implementation and assessments. This algorithmic impact assessment should be made public whenever 
possible. Reporting should be provided in a clear and machine-readable manner using plain language to 
allow for more straightforward public accountability. 
28
Algorithmic 
Discrimination 
Protections, NOTICE & 
EXPLANATION 
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint for the development of additional 
technical standards and practices that are tailored for particular sectors and contexts. 
An automated system should provide demonstrably clear, timely, understandable, and accessible notice of use, and 
explanations as to how and why a decision was made or an action was taken by the system. These expectations are 
explained below. 
Provide clear, timely, understandable, and accessible notice of use and explanations ­
Generally accessible plain language documentation. The entity responsible for using the automated 
system should ensure that documentation describing the overall system (including any human components) is 
public and easy to find. The documentation should describe, in plain language, how the system works and how 
any automated component is used to determine an action or decision. It should also include expectations about 
reporting described throughout this framework, such as the algorithmic impact assessments described as 
part of Algorithmic Discrimination Protections. 
Accountable. Notices should clearly identify the entity responsible for designing each component of the 
system and the entity using it. 
Timely and up-to-date. Users should receive notice of the use of automated systems in advance of using or 
while being impacted by the technology. An explanation should be available with the decision itself, or soon 
thereafter. Notice should be kept up-to-date and people impacted by the system should be notified of use case 
or key functionality changes. 
Brief and clear. Notices and explanations should be assessed, such as by research on users’ experiences, 
including user testing, to ensure that the people using or impacted by the automated system are able to easily 
find notices and explanations, read them quickly, and understand and act on them. This includes ensuring that 
notices and explanations are accessible to users with disabilities and are available in the language(s) and read-
ing level appropriate for the audience. Notices and explanations may need to be available in multiple forms, 
(e.g., on paper, on a physical sign, or online), in order to meet these expectations and to be accessible to the 
American public. 
Provide explanations as to how and why a decision was made or an action was taken by an 
automated system 
Tailored to the purpose. Explanations should be tailored to the specific purpose for which the user is 
expected to use the explanation, and should clearly state that purpose. An informational explanation might 
differ from an explanation provided to allow for the possibility of recourse, an appeal, or one provided in the 
context of a dispute or contestation process. For the purposes of this framework, 'explanation' should be 
construed broadly. An explanation need not be a plain-language statement about causality but could consist of 
any mechanism that allows the recipient to build the necessary understanding and intuitions to achieve the 
stated purpose. Tailoring should be assessed (e.g., via user experience research). 
Tailored to the target of the explanation. Explanations should be targeted to specific audiences and 
clearly state that audience. An explanation provided to the subject of a decision might differ from one provided 
to an advocate, or to a domain expert or decision maker. Tailoring should be assessed (e.g., via user experience 
research). 
43, source_sentence: What is the purpose of the email address ai-equity@ostpeopgov created by OSTP?
sentences: NOTICE & 
EXPLANATION 
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint for the development of additional 
technical standards and practices that are tailored for particular sectors and contexts. 
An automated system should provide demonstrably clear, timely, understandable, and accessible notice of use, and 
explanations as to how and why a decision was made or an action was taken by the system. These expectations are 
explained below. 
Provide clear, timely, understandable, and accessible notice of use and explanations ­
Generally accessible plain language documentation. The entity responsible for using the automated 
system should ensure that documentation describing the overall system (including any human components) is 
public and easy to find. The documentation should describe, in plain language, how the system works and how 
any automated component is used to determine an action or decision. It should also include expectations about 
reporting described throughout this framework, such as the algorithmic impact assessments described as 
part of Algorithmic Discrimination Protections. 
Accountable. Notices should clearly identify the entity responsible for designing each component of the 
system and the entity using it. 
Timely and up-to-date. Users should receive notice of the use of automated systems in advance of using or 
while being impacted by the technology. An explanation should be available with the decision itself, or soon 
thereafter. Notice should be kept up-to-date and people impacted by the system should be notified of use case 
or key functionality changes. 
Brief and clear. Notices and explanations should be assessed, such as by research on users’ experiences, 
including user testing, to ensure that the people using or impacted by the automated system are able to easily 
find notices and explanations, read them quickly, and understand and act on them. This includes ensuring that 
notices and explanations are accessible to users with disabilities and are available in the language(s) and read-
ing level appropriate for the audience. Notices and explanations may need to be available in multiple forms, 
(e.g., on paper, on a physical sign, or online), in order to meet these expectations and to be accessible to the 
American public. 
Provide explanations as to how and why a decision was made or an action was taken by an 
automated system 
Tailored to the purpose. Explanations should be tailored to the specific purpose for which the user is 
expected to use the explanation, and should clearly state that purpose. An informational explanation might 
differ from an explanation provided to allow for the possibility of recourse, an appeal, or one provided in the 
context of a dispute or contestation process. For the purposes of this framework, 'explanation' should be 
construed broadly. An explanation need not be a plain-language statement about causality but could consist of 
any mechanism that allows the recipient to build the necessary understanding and intuitions to achieve the 
stated purpose. Tailoring should be assessed (e.g., via user experience research). 
Tailored to the target of the explanation. Explanations should be targeted to specific audiences and 
clearly state that audience. An explanation provided to the subject of a decision might differ from one provided 
to an advocate, or to a domain expert or decision maker. Tailoring should be assessed (e.g., via user experience 
research). 
43, APPENDIX
Summaries of Additional Engagements: 
• OSTP created an email address (ai-equity@ostp.eop.gov) to solicit comments from the public on the use of
artificial intelligence and other data-driven technologies in their lives.
• OSTP issued a Request For Information (RFI) on the use and governance of biometric technologies.113 The
purpose of this RFI was to understand the extent and variety of biometric technologies in past, current, or
planned use; the domains in which these technologies are being used; the entities making use of them; current
principles, practices, or policies governing their use; and the stakeholders that are, or may be, impacted by their
use or regulation. The 130 responses to this RFI are available in full online114 and were submitted by the below
listed organizations and individuals:
Accenture 
Access Now 
ACT | The App Association 
AHIP 
AIethicist.org 
Airlines for America 
Alliance for Automotive Innovation 
Amelia Winger-Bearskin 
American Civil Liberties Union 
American Civil Liberties Union of 
Massachusetts 
American Medical Association 
ARTICLE19 
Attorneys General of the District of 
Columbia, Illinois, Maryland, 
Michigan, Minnesota, New York, 
North Carolina, Oregon, Vermont, 
and Washington 
Avanade 
Aware 
Barbara Evans 
Better Identity Coalition 
Bipartisan Policy Center 
Brandon L. Garrett and Cynthia 
Rudin 
Brian Krupp 
Brooklyn Defender Services 
BSA | The Software Alliance 
Carnegie Mellon University 
Center for Democracy & 
Technology 
Center for New Democratic 
Processes 
Center for Research and Education 
on Accessible Technology and 
Experiences at University of 
Washington, Devva Kasnitz, L Jean 
Camp, Jonathan Lazar, Harry 
Hochheiser 
Center on Privacy & Technology at 
Georgetown Law 
Cisco Systems 
City of Portland Smart City PDX 
Program 
CLEAR 
Clearview AI 
Cognoa 
Color of Change 
Common Sense Media 
Computing Community Consortium 
at Computing Research Association 
Connected Health Initiative 
Consumer Technology Association 
Courtney Radsch 
Coworker 
Cyber Farm Labs 
Data & Society Research Institute 
Data for Black Lives 
Data to Actionable Knowledge Lab 
at Harvard University 
Deloitte 
Dev Technology Group 
Digital Therapeutics Alliance 
Digital Welfare State & Human 
Rights Project and Center for 
Human Rights and Global Justice at 
New York University School of 
Law, and Temple University 
Institute for Law, Innovation & 
Technology 
Dignari 
Douglas Goddard 
Edgar Dworsky 
Electronic Frontier Foundation 
Electronic Privacy Information 
Center, Center for Digital 
Democracy, and Consumer 
Federation of America 
FaceTec 
Fight for the Future 
Ganesh Mani 
Georgia Tech Research Institute 
Google 
Health Information Technology 
Research and Development 
Interagency Working Group 
HireVue 
HR Policy Association 
ID.me 
Identity and Data Sciences 
Laboratory at Science Applications 
International Corporation 
Information Technology and 
Innovation Foundation 
Information Technology Industry 
Council 
Innocence Project 
Institute for Human-Centered 
Artificial Intelligence at Stanford 
University 
Integrated Justice Information 
Systems Institute 
International Association of Chiefs 
of Police 
International Biometrics + Identity 
Association 
International Business Machines 
Corporation 
International Committee of the Red 
Cross 
Inventionphysics 
iProov 
Jacob Boudreau 
Jennifer K. Wagner, Dan Berger, 
Margaret Hu, and Sara Katsanis 
Jonathan Barry-Blocker 
Joseph Turow 
Joy Buolamwini 
Joy Mack 
Karen Bureau 
Lamont Gholston 
Lawyers’ Committee for Civil 
Rights Under Law 
60, NOTICE & 
EXPLANATION 
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint for the development of additional 
technical standards and practices that are tailored for particular sectors and contexts. 
Tailored to the level of risk. An assessment should be done to determine the level of risk of the auto­
mated system. In settings where the consequences are high as determined by a risk assessment, or extensive 
oversight is expected (e.g., in criminal justice or some public sector settings), explanatory mechanisms should 
be built into the system design so that the system’s full behavior can be explained in advance (i.e., only fully 
transparent models should be used), rather than as an after-the-decision interpretation. In other settings, the 
extent of explanation provided should be tailored to the risk level. 
Valid. The explanation provided by a system should accurately reflect the factors and the influences that led 
to a particular decision, and should be meaningful for the particular customization based on purpose, target, 
and level of risk. While approximation and simplification may be necessary for the system to succeed based on 
the explanatory purpose and target of the explanation, or to account for the risk of fraud or other concerns 
related to revealing decision-making information, such simplifications should be done in a scientifically 
supportable way. Where appropriate based on the explanatory system, error ranges for the explanation should 
be calculated and included in the explanation, with the choice of presentation of such information balanced 
with usability and overall interface complexity concerns. 
Demonstrate protections for notice and explanation 
Reporting. Summary reporting should document the determinations made based on the above consider­
ations, including: the responsible entities for accountability purposes; the goal and use cases for the system, 
identified users, and impacted populations; the assessment of notice clarity and timeliness; the assessment of 
the explanation's validity and accessibility; the assessment of the level of risk; and the account and assessment 
of how explanations are tailored, including to the purpose, the recipient of the explanation, and the level of 
risk. Individualized profile information should be made readily available to the greatest extent possible that 
includes explanations for any system impacts or inferences. Reporting should be provided in a clear plain 
language and machine-readable manner. 
44, source_sentence: How did the lack of explanation in the benefits awarding system contribute to the denial of benefits for individuals?
sentences: APPENDIX
Summaries of Additional Engagements: 
• OSTP created an email address (ai-equity@ostp.eop.gov) to solicit comments from the public on the use of
artificial intelligence and other data-driven technologies in their lives.
• OSTP issued a Request For Information (RFI) on the use and governance of biometric technologies.113 The
purpose of this RFI was to understand the extent and variety of biometric technologies in past, current, or
planned use; the domains in which these technologies are being used; the entities making use of them; current
principles, practices, or policies governing their use; and the stakeholders that are, or may be, impacted by their
use or regulation. The 130 responses to this RFI are available in full online114 and were submitted by the below
listed organizations and individuals:
Accenture 
Access Now 
ACT | The App Association 
AHIP 
AIethicist.org 
Airlines for America 
Alliance for Automotive Innovation 
Amelia Winger-Bearskin 
American Civil Liberties Union 
American Civil Liberties Union of 
Massachusetts 
American Medical Association 
ARTICLE19 
Attorneys General of the District of 
Columbia, Illinois, Maryland, 
Michigan, Minnesota, New York, 
North Carolina, Oregon, Vermont, 
and Washington 
Avanade 
Aware 
Barbara Evans 
Better Identity Coalition 
Bipartisan Policy Center 
Brandon L. Garrett and Cynthia 
Rudin 
Brian Krupp 
Brooklyn Defender Services 
BSA | The Software Alliance 
Carnegie Mellon University 
Center for Democracy & 
Technology 
Center for New Democratic 
Processes 
Center for Research and Education 
on Accessible Technology and 
Experiences at University of 
Washington, Devva Kasnitz, L Jean 
Camp, Jonathan Lazar, Harry 
Hochheiser 
Center on Privacy & Technology at 
Georgetown Law 
Cisco Systems 
City of Portland Smart City PDX 
Program 
CLEAR 
Clearview AI 
Cognoa 
Color of Change 
Common Sense Media 
Computing Community Consortium 
at Computing Research Association 
Connected Health Initiative 
Consumer Technology Association 
Courtney Radsch 
Coworker 
Cyber Farm Labs 
Data & Society Research Institute 
Data for Black Lives 
Data to Actionable Knowledge Lab 
at Harvard University 
Deloitte 
Dev Technology Group 
Digital Therapeutics Alliance 
Digital Welfare State & Human 
Rights Project and Center for 
Human Rights and Global Justice at 
New York University School of 
Law, and Temple University 
Institute for Law, Innovation & 
Technology 
Dignari 
Douglas Goddard 
Edgar Dworsky 
Electronic Frontier Foundation 
Electronic Privacy Information 
Center, Center for Digital 
Democracy, and Consumer 
Federation of America 
FaceTec 
Fight for the Future 
Ganesh Mani 
Georgia Tech Research Institute 
Google 
Health Information Technology 
Research and Development 
Interagency Working Group 
HireVue 
HR Policy Association 
ID.me 
Identity and Data Sciences 
Laboratory at Science Applications 
International Corporation 
Information Technology and 
Innovation Foundation 
Information Technology Industry 
Council 
Innocence Project 
Institute for Human-Centered 
Artificial Intelligence at Stanford 
University 
Integrated Justice Information 
Systems Institute 
International Association of Chiefs 
of Police 
International Biometrics + Identity 
Association 
International Business Machines 
Corporation 
International Committee of the Red 
Cross 
Inventionphysics 
iProov 
Jacob Boudreau 
Jennifer K. Wagner, Dan Berger, 
Margaret Hu, and Sara Katsanis 
Jonathan Barry-Blocker 
Joseph Turow 
Joy Buolamwini 
Joy Mack 
Karen Bureau 
Lamont Gholston 
Lawyers’ Committee for Civil 
Rights Under Law 
60, APPENDIX
Systems that impact the safety of communities such as automated traffic control systems, elec 
-ctrical grid controls, smart city technologies, and industrial emissions and environmental
impact control algorithms; and
Systems related to access to benefits or services or assignment of penalties such as systems that
support decision-makers who adjudicate benefits such as collating or analyzing information or
matching records, systems which similarly assist in the adjudication of administrative or criminal
penalties, fraud detection algorithms, services or benefits access control algorithms, biometric
systems used as access control, and systems which make benefits or services related decisions on a
fully or partially autonomous basis (such as a determination to revoke benefits).
54, NOTICE & 
EXPLANATION 
WHY THIS PRINCIPLE IS IMPORTANT
This section provides a brief summary of the problems which the principle seeks to address and protect 
against, including illustrative examples. 
•
A predictive policing system claimed to identify individuals at greatest risk to commit or become the victim of
gun violence (based on automated analysis of social ties to gang members, criminal histories, previous experi­
ences of gun violence, and other factors) and led to individuals being placed on a watch list with no
explanation or public transparency regarding how the system came to its conclusions.85 Both police and
the public deserve to understand why and how such a system is making these determinations.
•
A system awarding benefits changed its criteria invisibly. Individuals were denied benefits due to data entry
errors and other system flaws. These flaws were only revealed when an explanation of the system
was demanded and produced.86 The lack of an explanation made it harder for errors to be corrected in a
timely manner.
42, source_sentence: What is the title of the publication related to Artificial Intelligence Risk Management by NIST?
sentences: 8 
Trustworthy AI Characteristics: Accountable and Transparent, Privacy Enhanced, Safe, Secure and 
Resilient 
2.5. Environmental Impacts 
Training, maintaining, and operating (running inference on) GAI systems are resource-intensive activities, 
with potentially large energy and environmental footprints. Energy and carbon emissions vary based on 
what is being done with the GAI model (i.e., pre-training, ﬁne-tuning, inference), the modality of the 
content, hardware used, and type of task or application. 
Current estimates suggest that training a single transformer LLM can emit as much carbon as 300 round-
trip ﬂights between San Francisco and New York. In a study comparing energy consumption and carbon 
emissions for LLM inference, generative tasks (e.g., text summarization) were found to be more energy- 
and carbon-intensive than discriminative or non-generative tasks (e.g., text classiﬁcation).  
Methods for creating smaller versions of trained models, such as model distillation or compression, 
could reduce environmental impacts at inference time, but training and tuning such models may still 
contribute to their environmental impacts. Currently there is no agreed upon method to estimate 
environmental impacts from GAI.  
Trustworthy AI Characteristics: Accountable and Transparent, Safe 
2.6. Harmful Bias and Homogenization 
Bias exists in many forms and can become ingrained in automated systems. AI systems, including GAI 
systems, can increase the speed and scale at which harmful biases manifest and are acted upon, 
potentially perpetuating and amplifying harms to individuals, groups, communities, organizations, and 
society. For example, when prompted to generate images of CEOs, doctors, lawyers, and judges, current 
text-to-image models underrepresent women and/or racial minorities, and people with disabilities. 
Image generator models have also produced biased or stereotyped output for various demographic 
groups and have diﬃculty producing non-stereotyped content even when the prompt speciﬁcally 
requests image features that are inconsistent with the stereotypes. Harmful bias in GAI models, which 
may stem from their training data, can also cause representational harms or perpetuate or exacerbate 
bias based on race, gender, disability, or other protected classes.  
Harmful bias in GAI systems can also lead to harms via disparities between how a model performs for 
diﬀerent subgroups or languages (e.g., an LLM may perform less well for non-English languages or 
certain dialects). Such disparities can contribute to discriminatory decision-making or ampliﬁcation of 
existing societal biases. In addition, GAI systems may be inappropriately trusted to perform similarly 
across all subgroups, which could leave the groups facing underperformance with worse outcomes than 
if no GAI system were used. Disparate or reduced performance for lower-resource languages also 
presents challenges to model adoption, inclusion, and accessibility, and may make preservation of 
endangered languages more diﬃcult if GAI systems become embedded in everyday processes that would 
otherwise have been opportunities to use these languages.  
Bias is mutually reinforcing with the problem of undesired homogenization, in which GAI systems 
produce skewed distributions of outputs that are overly uniform (for example, repetitive aesthetic styles, HUMAN ALTERNATIVES, 
CONSIDERATION, AND 
FALLBACK 
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint for the development of additional 
technical standards and practices that are tailored for particular sectors and contexts. 
An automated system should provide demonstrably effective mechanisms to opt out in favor of a human alterna­
tive, where appropriate, as well as timely human consideration and remedy by a fallback system, with additional 
human oversight and safeguards for systems used in sensitive domains, and with training and assessment for any 
human-based portions of the system to ensure effectiveness. 
Provide a mechanism to conveniently opt out from automated systems in favor of a human 
alternative, where appropriate 
Brief, clear, accessible notice and instructions. Those impacted by an automated system should be 
given a brief, clear notice that they are entitled to opt-out, along with clear instructions for how to opt-out. 
Instructions should be provided in an accessible form and should be easily findable by those impacted by the 
automated system. The brevity, clarity, and accessibility of the notice and instructions should be assessed (e.g., 
via user experience research). 
Human alternatives provided when appropriate. In many scenarios, there is a reasonable expectation 
of human involvement in attaining rights, opportunities, or access. When automated systems make up part of 
the attainment process, alternative timely human-driven processes should be provided. The use of a human 
alternative should be triggered by an opt-out process. 
Timely and not burdensome human alternative. Opting out should be timely and not unreasonably 
burdensome in both the process of requesting to opt-out and the human-driven alternative provided. 
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 con­
test its impacts on you 
Proportionate. The availability of human consideration and fallback, along with associated training and 
safeguards against human bias, should be proportionate to the potential of the automated system to meaning­
fully impact rights, opportunities, or access. Automated systems that have greater control over outcomes, 
provide input to high-stakes decisions, relate to sensitive domains, or otherwise have a greater potential to 
meaningfully impact rights, opportunities, or access should have greater availability (e.g., staffing) and over­
sight of human consideration and fallback mechanisms. 
Accessible. Mechanisms for human consideration and fallback, whether in-person, on paper, by phone, or 
otherwise provided, should be easy to find and use. These mechanisms should be tested to ensure that users 
who have trouble with the automated system are able to use human consideration and fallback, with the under­
standing that it may be these users who are most likely to need the human assistance. Similarly, it should be 
tested to ensure that users with disabilities are able to find and use human consideration and fallback and also 
request reasonable accommodations or modifications. 
Convenient. Mechanisms for human consideration and fallback should not be unreasonably burdensome as 
compared to the automated system’s equivalent. 
49, NIST Trustworthy and Responsible AI  
NIST AI 600-1 
Artificial Intelligence Risk Management 
Framework: Generative Artificial 
Intelligence Profile 
 
 
 
This publication is available free of charge from: 
https://doi.org/10.6028/NIST.AI.600-1
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