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Automation + AI

How to responsibly, ethically, and equitably use technology, including artificial intelligence (AI), to automate certain processes is an increasingly important question for governments at every level, including benefits agencies.

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Sifting through complexity with a focus on equity and access.

Properly designed and implemented, automated solutions can help ease case worker loads and speed up the benefits delivery process. However, automated approaches can also create potential harms for beneficiaries, create new biases in systems, and raise issues around transparency and accountability.

It can be difficult to parse through the different terms used to describe the technology behind automated processes and systems, from artificial intelligence, to robotic process automation or algorithm-driven decision-making, and automated decision-making systems. It can also be challenging to sort through information about what a given technology can and cannot do today. Benefits practitioners need clear information to evaluate new technologies, including AI and other approaches to automation. This page contains resources to illuminate the landscape of automated technology to help benefits practitioners understand and assess automated systems and tools.

Featured Content

AI Guide for Government: A Living and Evolving Guide to the Application of Artificial Intelligence for the U.S. Federal Government

AI Guide for Government: A Living and Evolving Guide to the Application of Artificial Intelligence for the U.S. Federal Government

This guide is intended to help government decision makers clearly see what AI means for their agencies and how to invest and build AI capabilities.

General Services Administration
Resource
Use Cases for Robotic Process Automation in UI Claims Processing

Use Cases for Robotic Process Automation in UI Claims Processing

For the past year, modernization teams at the Department of Labor (DOL) have been helping states identify opportunities to automate rote, non-discretionary, manual tasks, with the goal of helping them speed up the time that it takes to process claims. This post provides more context on Robotic Process Automation (RPA) and potential use cases in unemployment insurance.

U.S. Department of Labor
Resource
What the Digital Benefits Network is Reading on Automation

What the Digital Benefits Network is Reading on Automation

In this piece, the Digital Benefits Network shares several sources—from journalistic pieces, to reports and academic articles—we’ve found useful and interesting in our reading on automation and artificial intelligence.

Beeck Center for Social Impact + Innovation at Georgetown University
Resource

Case studies

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Providers App: Timely, Localized Updates to Benefits Information

Providers App: Timely, Localized Updates to Benefits Information

Low-income Americans struggle to monitor their benefits and learn about new benefit programs because each state maintains its own systems for accessing this information. These websites are often convoluted and hard to find.

Beeck Center for Social Impact + Innovation at Georgetown University
2021
Project Re:new — Redesigning Michigan's Benefits Renewal Forms

Project Re:new — Redesigning Michigan's Benefits Renewal Forms

Michigan residents must renew their benefits annually to continue receiving public assistance. Unfortunately, each assistance program had its own renewal form, and the process was plagued by complicated language and undelivered forms. As a result, many residents lost their benefits and the state spent about $25 million annually correcting errors.

Beeck Center for Social Impact + Innovation at Georgetown University
2020
Redesigning Michigan's Benefits Correspondence

Redesigning Michigan's Benefits Correspondence

The correspondence sent by Michigan to its residents containing benefits programs updates and action items continues to be too long, too vague, and too reliant on legal jargon. This prevents residents from getting the help they need and forces agency staff to spend time explaining the letters.

Beeck Center for Social Impact + Innovation at Georgetown University
2021

All Automation + AI Content

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A list of open LLMs

These LLMs (Large Language Models) are all licensed for commercial use (e.g., Apache 2.0, MIT, OpenRAIL-M).

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Blueprint for an AI Bill of Rights

The White House Office of Science and Technology Policy has identified five principles that should guide the design, use, and deployment of automated systems to protect the American public in the age of artificial intelligence. These principles help provide guidance whenever automated systems can meaningfully impact the public’s rights, opportunities, or access to critical needs.

White House Office of Science and Technology Policy
Resource

OECD AI Principles Overview

The OECD AI Principles promote use of AI that is innovative and trustworthy and that respects human rights and democratic values. The principles were adopted in 2019; this webpage provides an overview of the principles and key terms.

Organisation for Economic Co-operation and Development (OECD)
Resource

I Am Not a Number

In early 2023, Wired magazine ran four pieces exploring the use of algorithms to identify fraud in public benefits and potential harms, deeply exploring cases from Europe.

Wired
Resource

Algorithmic Accountability for the Public Sector

This report presents evidence on the use of algorithmic accountability policies in different contexts from the perspective of those implementing these tools, and explores the limits of legal and policy mechanisms in ensuring safe and accountable algorithmic systems.

Ada Lovelace Institute (Ada), AI Now Institute (AI Now), Open Government Partnership (OGP)
Resource

Domain Shift and Emerging Questions in Facial Recognition Technology

This policy brief offers recommendations to policymakers relating to the computational and human sides of facial recognition technologies based on a May 2020 workshop with leading computer scientists, legal scholars, and representatives from industry, government, and civil society

Stanford University, Institute for Human-Centered Artificial Intelligence
Resource

Controlling Large Language Models: A Primer

Concerns over risks from generative artificial intelligence systems have increased significantly over the past year, driven in large part by the advent of increasingly capable large language models. But, how do AI developers attempt to control the outputs of these models? This primer outlines four commonly used techniques and explains why this objective is so challenging.

Center for Security and Emerging Technology (CSET), Georgetown University
Resource

Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing

This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.

ACM Conference on Fairness, Accountability, and Transparency
Resource

Algorithmic Accountability: A Primer

The primer–originally prepared for the Progressive Congressional Caucus’ Tech Algorithm Briefing–explores the trade-offs and debates about algorithms and accountability across several key ethical dimensions, including fairness and bias; opacity and transparency; and lack of standards for auditing.

Data & Society
Resource

Artifice and Intelligence

This piece outlines the Privacy Center’s decision to stop using the words “artificial intelligence,” “AI,” and “machine learning” in their work.

Center on Privacy & Technology at Georgetown Law
Resource

Popular Support for Balancing Equity and Efficiency in Resource Allocation

This article explores how online advertising algorithms bias between Spanish and English speakers for SNAP in California.

Cornell University, Code for America, Stanford University, Harvard Kennedy School
Resource

The Equitable Tech Horizon in Digital Benefits Panel

Hear perspectives on topics including centering beneficiaries and workers in new ways, digital service delivery, digital identity, and automation.This video was recorded at the Digital Benefits Conference (BenCon) on June 14, 2023.

Beeck Center for Social Impact + Innovation at Georgetown University
Resource

POVERTY LAWGORITHMS: A Poverty Lawyer’s Guide to Fighting Automated Decision-Making Harms on Low-Income Communities

This guide, directed at poverty lawyers, explains automated decision-making systems so lawyers and advocates can better identify the source of their clients' problems and advocate on their behalf. Relevant for practitioners, this report covers key questions around automated decision-making systems.

Data & Society
Resource

Dispelling Myths About Artificial Intelligence for Government Service Delivery

Directed at government practitioners, this resource addresses common misconceptions about artificial intelligence (AI) and explains the current state of technology.

Center for Democracy and Technology
Resource

Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. This academic study presents an approach to evaluate bias present in automated facial analysis algorithms and datasets.

Proceedings of Machine Learning Research
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Envisioning a Human-AI collaborative system to transform policies into decision models

This paper introduces the problem of semi-automatically building decision models from eligibility policies for social services, and presents an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. There is enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.

IBM Research
Resource

State of State AI Laws

This resource tracks state laws on AI at various stages of development and passage.

Electronic Privacy Information Center (EPIC)
Resource

Agency Inventories of AI Use Cases

In accordance with Executive Order 13960, Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government, Federal agencies began publishing their first annual inventories of artificial intelligence (AI) use cases in June 2022 and the following months. On this page, users can access inventories published to date.

National Artificial Intelligence Initiative
Resource

Disability, Bias, and AI

This report explores key questions that a focus on disability raises for the project of understanding the social implications of AI, and for ensuring that AI technologies don’t reproduce and extend histories of marginalization.

AI Now Institute
Resource

Parking signs and possible futures for LLMs in government

Government agencies adopting generative AI tools seems inevitable at this point. But there is more than one possible future for how agencies use generative AI to simplify complex government information.

Ad Hoc
Resource

Looking before we leap: Exploring AI and data science ethics review process

This report explores the role that academic and corporate Research Ethics Committees play in evaluating AI and data science research for ethical issues, and also investigates the kinds of common challenges these bodies face.

Ada Lovelace Institute, University of Exeter’s Institute for Data Science and Artificial Intelligence, Alan Turing Institute
Resource

Surveillance, Discretion and Governance in Automated Welfare

This academic article develops a framework for evaluating whether and how automated decision-making welfare systems introduce new harms and burdens for claimants, focusing on an example case from Germany.

Science and Technology Studies
Resource

The Ohio Benefits Program is “BOT” In

This award documentation from the National Association of State Chief Information Officers (NASCIO) explains how agencies in Ohio used automation to support administration of public benefits programs.

National Association of State Chief Information Officers (NASCIO)
Resource

State of California: Benefits and Risks of Generative Artificial Intelligence Report

This report on the use of Generative AI in State government presents an initial analysis of the potential benefits to individuals, communities, government and State government workers, while also exploring potential risks.

California Government Operations Agency
Resource

The Privacy-Bias Trade-Off

Safeguarding privacy and addressing algorithmic bias can pose an under-recognized trade-off. This brief documents tradeoffs by examining the U.S. government’s recent efforts to introduce government-wide equity assessments of federal programs. The authors propose a range of policy solutions that would enable agencies to navigate the privacy-bias trade-off.

Stanford University, Institute for Human-Centered Artificial Intelligence
Resource

Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy

This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.

Princeton University, Microsoft Research
Resource

Digital Welfare States and Human Rights

In this report, the UN Special Rapporteur critically examines uses of digital technologies for administration of welfare programs across international contexts, and makes recommendations for using technology responsibly and ethically.

United Nations
Resource

NIST: AI Risk Management Framework

In collaboration with the private and public sectors, NIST has developed a framework to better manage risks to individuals, organizations, and society associated with artificial intelligence (AI). The framework is intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.

National Institute of Standards and Technology
Resource

Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing

This paper explores design considerations and ethical tensions related to auditing of commercial facial processing technology.

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
Resource

Unpacking the White House blueprint for an AI Bill of Rights

On December 5, 2022, an expert panel, including representatives from the White House, unpacked what’s included in the AI Bill of Rights, and explored how to operationalize such guidance among consumers, developers, and other users designing and implementing automated decisions.

Brookings Institution
Resource

Government AI Readiness Index 2023

For 2023, Oxford Insights assesses the AI readiness of 193 governments across the world.

Oxford Insights
Resource

Government By Algorithm: Artificial Intelligence In Federal Administrative Agencies

Little is known about how agencies are currently using AI systems, and little attention has been devoted to how agencies acquire such tools or oversee their use.

Stanford RegLab
Resource

Better Together? An Evaluation of AI-Supported Code Translation

This research explores how software engineers are able to work with generative machine learning models. The results explore the benefits of generative code models and the challenges software engineers face when working with their outputs. The authors also argue for the need for intelligent user interfaces that help software engineers effectively work with generative code models.

IBM Research
Resource

Human-Centered, Machine-Assisted: Ethically Deploying AI to Improve the Client Experience

In this interview, Code for America staff members share how client success, data science, and qualitative research teams work together to consider the responsible deployment of artificial intelligence (AI) in responding to clients who seek assistance with three products.

Code for America
Resource

ITEM 10: How a Small Legal Aid Team Took on Algorithmic Black Boxing at Their State’s Employment Agency (And Won)

In June 2022, Legal Aid of Arkansas won a significant victory in their ongoing work to compel Arkansas’ employment agency to disclose crucial details about how it uses automated decision-making systems to detect and adjudicate fraud.

Electronic Privacy Information Center (EPIC)
Resource

What Are Generative AI, Large Language Models, and Foundation Models?

What exactly are the differences between generative AI, large language models, and foundation models? This post aims to clarify what each of these three terms mean, how they overlap, and how they differ.

Center for Security and Emerging Technology
Resource

The Adoption and Implementation of Artificial Intelligence Chatbots in Public Organizations: Evidence from U.S. State Governments

Although the use of artificial intelligence (AI) chatbots in public organizations has increased in recent years, crucial gaps remain unresolved, and this paper explores adoption and implementation of chatbots in state government contexts.

The American Review of Public Administration
Resource

Communicating About the Social Implications of AI: A FrameWorks Strategic Brief

Research identified five key obstacles that researchers, activists, and advocates face in efforts to open critical public conversations about AI’s relationship with inequity and advance needed policies.

Frameworks Initiative
Resource

AI and the Future of Government Services: A conversation with Afua Bruce

Trooper Sanders interviews Afua Bruce, a leading public interest technologis to examine the pros and cons of AI and key considerations for when humans rely on technological solutions.

Benefits Data Trust
Resource

Technology in the public sector and the future of government work

This report explores technologies that have the potential to significantly affect employment and job quality in the public sector, the factors that drive choices about which technologies are adopted and how they are implemented, how technology will change the experience of public sector work, and what kinds of interventions can protect against potential downsides of technology use in the public sector. The report categories technologies into five overlapping categories including manual task automation, process automation, automated decision-making systems, integrated data systems, and electronic monitoring.

UC Berkeley Labor Center
Resource

Access Denied: Faulty Automated Background Checks Freeze Out Renters

This reporting explores how algorithms used to screen prospective tenants, including those waiting for public housing, can block renters from housing based on faulty information.

The Markup
Resource

OECD Framework for the Classification of AI systems

To help policy makers, regulators, legislators and others characterize AI systems deployed in specific contexts, the OECD has developed a user-friendly tool to evaluate AI systems from a policy perspective.

Organisation for Economic Co-operation and Development (OECD)
Resource

Guide to robotic process automation

The Guide to Robotic Process Automation, including the RPA Playbook provides detailed guidance for federal agencies starting a new RPA program or evolving an existing one.

Digital.gov
Resource

Artificial Intelligence: Background, Selected Issues, and Policy Considerations

This report aims to help congressional leaders understand AI, and provide key terms and definitions that would be important in crafting or understanding potential legislation on the topic.

Congressional Research Service
Resource

The Social Life of Algorithmic Harms

This series of essays seeks to expand our vocabulary of algorithmic harms to help protect against them.

Data & Society
Resource

Use Cases for Robotic Process Automation in UI Claims Processing

For the past year, modernization teams at the Department of Labor (DOL) have been helping states identify opportunities to automate rote, non-discretionary, manual tasks, with the goal of helping them speed up the time that it takes to process claims. This post provides more context on Robotic Process Automation (RPA) and potential use cases in unemployment insurance.

U.S. Department of Labor
Resource

Automated Decision-Making Systems and Discrimination

This guidebook offers an introduction to the risks of discrimination when using automated decision-making systems. This report also includes helpful definitions related to automation.

AlgorithmWatch
Resource

Updates to the OECD’s definition of an AI system explained

In 2023, OECD member countries approved a revised version of the Organisation’s definition of an AI system. This post explains the reasoning behind the updated definition.

Organisation for Economic Co-operation and Development (OECD)
Resource

Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts

Algorithmic impact assessments (AIAs) are an emergent form of accountability for organizations that build and deploy automated decision-support systems. This academic paper explores how to co-construct impacts that closely reflects harms, and emphasizes the need for input of various types of expertise and affected communities.

FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
Resource

Evaluating Facial Recognition Technology: A Protocol for Performance Assessment in New Domains

In May 2020, Stanford's HAI hosted a workshop to discuss the performance of facial recognition technologies that included leading computer scientists, legal scholars, and representatives from industry, government, and civil society. The white paper this workshop produced seeks to answer key questions in improving understandings of this rapidly changing space.

Stanford University, Institute for Human-Centered Artificial Intelligence
Resource

Use of Advanced Automation in SNAP

This memo provides state agencies with guidance on allowable use of advanced automation technologies. FNS encourages and supports state agencies’ use of advanced automation technologies to enhance the administration of SNAP and foster public trust, both in SNAP and in the state agencies’ systems, within the framework of statutory and regulatory requirements.

Food and Nutrition Service, U.S. Department of Agriculture
Resource

A Guide to Responsible and Efficient Use of Generative Tools

This video shows you how to get started with using Generative AI tools, including Bard, Bing, and ChatGPT, in your work as public sector professionals.

Innovate US
Resource

A Human Rights-Based Approach to Responsible AI

This paper argues that a human rights framework could help orient the research on artificial intelligence away from machines and the risks of their biases, and towards humans and the risks to their rights, helping to center the conversation around who is harmed, what harms they face, and how those harms may be mitigated.

2022 ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization
Resource

What’s in a name? A survey of strong regulatory definitions of automated decision-making systems

This post outlines different definitions of automated decision-making systems proposed by scholars and government entities.

Electronic Privacy Information Center (EPIC)
Resource

Challenging the Use of Algorithm-driven Decision-making in Benefits Determinations Affecting People with Disabilities

This report analyzes lawsuits that have been filed within the past 10 years arising from the use of algorithm-driven systems to assess people’s eligibility for, or the distribution of, public benefits. It identifies key insights from the various cases into what went wrong and analyzes the legal arguments that plaintiffs have used to challenge those systems in court.

Center for Democracy and Technology
Resource

Who Audits the Auditors? Recommendations from a Field Scan of the Algorithmic Auditing Ecosystem

Through a field scan, this paper identifies emerging best practices as well as methods and tools that are becoming commonplace, and enumerates common barriers to leveraging algorithmic audits as effective accountability mechanisms.

FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency
Resource

Regulating Biometrics: Taking Stock of a Rapidly Changing Landscape

This post reflects on and excerpts from AI Now's 2020 report on biometrics regulation.

AI Now Institute
Resource

What the Digital Benefits Network is Reading on Automation

In this piece, the Digital Benefits Network shares several sources—from journalistic pieces, to reports and academic articles—we’ve found useful and interesting in our reading on automation and artificial intelligence.

Beeck Center for Social Impact + Innovation at Georgetown University
Resource

Assembling Accountability: Algorithmic Impact Assessment for the Public Interest

This project maps the challenges of constructing algorithmic impact assessments (AIAs) by analyzing impact assessments in other domains—from the environment to human rights to privacy and identifies ten needed components for a robust impact assessment.

Data & Society
Resource

OECD AI Policy Observatory

The OECD AI Policy Observatory offers detailed information about AI policy and research globally.

Organisation for Economic Co-operation and Development (OECD)
Resource

AI Guide for Government: A Living and Evolving Guide to the Application of Artificial Intelligence for the U.S. Federal Government

This guide is intended to help government decision makers clearly see what AI means for their agencies and how to invest and build AI capabilities.

General Services Administration
Resource

Analysis of Robotic Process Automation in SNAP: Three Case Studies

This study examines the use of robotic process automation (RPA) technologies by three state agencies—Georgia, New Mexico, and Connecticut—to administer the Supplemental Nutrition Assistance Program (SNAP). RPA is software that integrates with other programs to automatically complete repetitive processes that normally are performed by humans.

USDA Food and Nutrition Service, Insight Policy Research, Inc.
Resource

Combatting Identify Fraud in Government Benefits Programs

This post argues that for the types of large-scale, organized fraud attacks that many state benefits systems saw during the pandemic, solutions grounded in cybersecurity methods may be far more effective than creating or adopting automated systems.

Center for Democracy and Technology (CDT)
Resource

A Snapshot of Artificial Intelligence Procurement Challenges

Artificial intelligence promises exciting new opportunities for the government to make policy, deliver services and engage with residents. But government procurement practices need to adapt if we are to ensure that rapidly-evolving AI tools meet intended purposes, avoid bias, and minimize risks to people, organizations, and communities. This report lays out five distinct challenges related to procuring AI in government.

The GovLab
Resource

Trustworthy AI (TAI) Playbook

This playbook is an initial step by the Office of the Chief AI Officer (OCAIO) to support trustworthy AI development.

U.S. Department of Health and Human Services
Resource

Defining and Demystifying Automated Decision Systems

This article suggests that a lack of clear, shared definitions makes it harder for the public and policymakers to evaluate and regulate technical systems that may have significant impacts on communities and individuals by shaping access to benefits, opportunities, and liberty. It presents and evaluates a definition for automated decision systems, developed through workshops with interdisciplinary scholars and practitioners.

Maryland Law Review
Resource

Algorithmic Accountability: Moving Beyond Audits

This report explores how despite unresolved concerns, an audit-centered algorithmic accountability approach is being rapidly mainstreamed into voluntary frameworks and regulations.

AI Now Institute
Resource

Framing the Risk Management Framework: Actionable Instructions by NIST in their “Govern” Section

This post introduces EPIC's exploration of actionable recommendations and points of agreement from leading A.I. frameworks, beginning with the National Institute of Standards and Technology's AI Risk Management Framework.

Electronic Privacy Information Center (EPIC)
Resource

Screened & Scored in the District of Columbia

This report explores how automated decision-making systems are being used in one jurisdiction: Washington, D.C.

Electronic Privacy Information Center (EPIC)
Resource

The Privacy-Bias Tradeoff: Data Minimization and Racial Disparity Assessments in U.S. Government

An emerging concern in algorithmic fairness is the tension with privacy interests. Data minimization can restrict access to protected attributes, such as race and ethnicity, for bias assessment and mitigation. This paper examines how this “privacy-bias tradeoff” has become an important battleground for fairness assessments in the U.S. government and provides rich lessons for resolving these tradeoffs.

2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’23)
Resource
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Automation + AI
Text Messaging
Data Sharing
Outreach + Awareness
Eligibility
Applying + Enrolling
Buying Design + Technology Services
Maintaining Benefits
Human-Centered Design
Digitizing Policy
Digital Services
Diversity, Equity + Inclusion
Policy Analysis
Digital Identity
Basic Income
CCDF: Child Care and Development Fund
COVID-19 Assistance
CTC: Child Tax Credit
EITC: Earned Income Tax Credit
Housing
LIHEAP
Medicaid/CHIP
Other Supplemental Nutrition Programs
P-EBT
Paid Leave
SSDI: Social Security Disability Insurance
SSI: Supplemental Security Income
TANF: Temporary Assistance for Needy Families
Tax Assistance
UI: Unemployment Insurance
Veterans Benefits
WIC: Special Supplemental Nutrition Program for Women, Infants and Children
SNAP: Supplemental Nutrition Assistance Program
Executive Branch
Academic
Community Members
Community-based Organization
Legislative Branch
Non-profit
Private
Government Agency
Federal/National
State/Provincial
County
Local/Municipal
Territorial
Tribal/Indigenous