cdn.ymaws.com€¦ · Web view2020-03-04 · “For example, IBM has been working to combine its Watson technology with the weather data it . ... The topic’s broad perspective

Running Head: A CASE FOR THE AI-AUGMENTED JOINT INFORMATION CENTER 1

Lessons from the Newsroom:

A Case for the AI-Augmented Joint Information Center

Deborah Grigsby Smith

Centennial Airport (KAPA)

Arapahoe County Public Airport Authority

August 29, 2019

A CASE FOR THE AI-AUGMENTED JOINT INFORMATION CENTER 2

Abstract

Multiple reporters work on independent stories as well as collectively on enterprise or

investigative pieces. Shorter news cycles, staff cuts, and the overwhelming inflow of

information, news tips, fake news, social media, and breaking news mirror the battle rhythm in

most joint information centers. Both must collect, process, vet, repackage, and distribute

information, data, and imagery in rapidly evolving situations. There is one difference, and this is

the fact newsrooms operate collectively with a centralized content management system that

fosters a coordinated, sleek flow of information from the creator to published product.

AI-based systems, fueled by the growing amounts of data now available, are rapidly becoming

competent, autonomous public communicators, able to manage and distribute large amounts of

information with accuracy and ease. As the stakeholder interface with technology deepens and

machine-authored content becomes more prevalent, public information officers must begin to

evaluate how AI and big data will reshape the profession. The challenge for future public

information officers lies not in the ability to communicate, but in keeping up the accelerating

technology used by the populations and stakeholders they serve. This study examines three case

studies of major global newsrooms that employed artificial intelligence-based content creation

and management tools to improve coverage, increase efficiency, and target specific audiences. It

also presents the findings of a survey of 33 public information and communications professionals

and their thoughts on the possible use of such tools in a joint information center environment.

Keywords: artificial intelligence, machine learning, natural language generation, data, big data,

public information, public information officer, joint information system, content management


TABLE OF CONTENTS

I. Introduction Page 4

A. Problem Statement Page 5

B. Research Question Page 6

C. Literature Review Page 8

II. Method: Case Study and Survey Page 15

III. Results Page 22

IV. Conclusion and Recommendations Page 33

A. Recommendation 1: Page 33

B. Recommendation 2: Page 33

C. Recommendation 3: Page 34

V. Discussion Page 34

VI. Conclusion Page 40

VII. References Page 41

VIII. Appendix A: Primary Survey Instrument Page 43

Appendix B: Raw Data from Primary Survey Page 47

Appendix C: Secondary Survey Instrument Page 55

Appendix D: Raw Data from Secondary Survey Page 56


INTRODUCTION

“The only sustainable response to technological disruption is to try to lead it.”

—Enrique Dans (Dans, 2019)

A Case for the AI-Augmented Joint Information Center

In times of disaster, timely and credible public information messaging can save lives and

protect property. Generous and continuous access to data is essential for public information

officers to not only understand a rapidly evolving situation but also to plan and develop the

strategic messaging that supports emergency response. As human interface with smart

technology deepens, and the use of social media and computer generated-content becomes more

prevalent, public information officers and the joint information centers (JICs) they serve can

quickly become overwhelmed parsing high-velocity mountains of information from informal,

official and unofficial sources. This surge, especially in an already understaffed JIC, increases

the probability of mistakes, ineffective, late, overlooked, or misaligned messaging. Artificial

Intelligence (AI)1 with its ability to quickly process large amounts of data, facilitate human

decision-making and even predict human behavior, have already made themselves at home in

almost every aspect of life, to include emergency management, so why has it not found its way

to the JIC? There is a growing number of case studies derived from the news industry, as well as

the marketing and public relations field, that point to promising applications for its use in the

1 Artifical intelligence, for the purpose of this paper will be defines as the ability of computers to gather data, learn from it and perform tasks that would normally require human intelligence such as image and face recognition, speech or the ability to compose sentences and analyze data.


joint information environment. The challenge for future public information officers rests not in

the ability to communicate, but rather, in understanding, leveraging, and keeping pace with

exponential amounts of data driving the technology used by the stakeholders they serve. This

thesis explores how three major news agencies have employed AI technology to expand

coverage, increase accuracy, and better connect with audiences, and how joint information

centers could benefit from adopting similar technology.

This intended audience for this document is the working emergency management and

public information officer community. The researcher presumes the audience has advanced

knowledge of joint information center job assignments, organization, operation, and challenges;

hence there has been no attempt to provide introductory or explanatory material on the subject

for casual readers.

Additionally, discussions around artificial intelligence, within this document, will be kept

at the conceptual level and not delve into specific technical, software, or hardware requirements.

Problem Statement

Examples of news-writing algorithms began to make headlines in 2014 when digital news

editor and computer programmer Ken Schwencke developed a few lines of code that pulled

alerts from the U.S. Geological Survey about earthquakes that exceeded a specified magnitude

threshold. The algorithm, called Quakebot, would then extract the relevant data and plug it into a

pre-written story template, much like a form letter. On the morning of March 17, 2014,

Schwencke was rattled from his slumber by an early morning quake. Instead of diving for cover,

he rushed to his laptop where he found a news story about the 4.7 earthquake, already written

and waiting in the queue. He quickly proofed the text and hit "publish." Within three minutes, the


story appeared online, making the Los Angeles Times the first media outlet to report the

Westwood, California earthquake. It would also make the Los Angeles Times the first major

media outlet to publish a news story written entirely—and autonomously—by a “robot reporter.”

(Oremus, 2014). Since then, more prominent news agencies have also employed similar AI-

based technology to create content, post to social media, enhance customer engagement, increase

revenue by customizing content to consumer preferences, and by driving article and advertising

clicks. According to research studies, AI-based systems offer many promising applications for

the news and other public communications field, automating tasks and augmenting human

capacity, in particular. Information parsing and content creation are essential aspects of the

public information officer’s job. Most spend a significant amount of the operational period

gathering, writing, and repackaging information across multiple platforms to targeted stakeholder

audiences.

AI-based content creation systems or “robot reporters” as they are, sometimes, called are

becoming increasingly competent, autonomous publishers and public communicators, capable of

plowing through mounds of data and authoring stories with increasing accuracy and speed. They

can quickly create infographics, short video clips, add captions, identify faces in images and

quickly translate between languages—to including many regional dialects. While stakeholder

demand for a steady feed of current information increases—particularly during emergencies—

public information officers working large-scale disasters, both in their home agencies and in a

joint information center, risk data saturation and must triage certain types of stakeholder

engagements over others. Public information officers become relegated to keeping up with

essential tasks, keeping them from more strategic and high-value activities. This practice can

compromise the quality of service to the public.


This thesis explores and offers a case for the use of AI-based technology in the joint

information center environment, specifically automated content creation and management tools,

similar to those used by newsrooms. By comparing the functions of a large newsroom with that

of a large working joint information center, the benefits of AI augmentation to improve accuracy,

drive productivity and enhance public messaging become more evident. In addition to everyday

social media monitoring, these systems, when integrated with credible data sources, tirelessly

mine data, schedule appointments, create reports, press releases, develop social media content,

blogs, video, and other public information products autonomously. When networked, these

systems may also help structure JIC public information products that are supported, and in step

with, the individual agency or organization—and the emergency operations center. Using a

small, well-qualified population, this research will also gauge how public information officers

feel with regards to working with such technology.

Research Question

While a large segment of current AI research falls into the military, scientific, media,

finance, and marketing category, the use of AI in the public information has received little

scholarly attention. As marketing firms, national and international newsrooms—as well as

nefarious and disruptive entities—grow more adept at using AI to engage audiences and

influence human behavior, its potential for application in the public information field remains

woefully untapped. This thesis presents the following research question: How can AI-based

content creation tools used in newsrooms can be adapted to improve general information

messaging and joint information center operation? This thesis does not purport AI or AI-based


systems as a single, emergency-ready solution. It does, however, offer foresight of its potential to

improve joint information center performance.

Literature Review

Artificial intelligence and AI-based algorithms have long been used by marketing

research firms to gauge, anticipate, target, and influence audience behavior. AI has transformed,

in just a few short years, the way information is collected, categorized, integrated, and

redistributed into new products, services, and tools that support critical business and decision-

making functions. (Kazuo Yano, 2017). As newsroom financial challenges force deep staff cuts

and reorganization, AI technology and “robot writers” have stepped in to fill the gap. However,

when it comes to AI, government entities, at all levels, often lag behind private sector successes.

Federal Chief Information Officer Suzette Kent recalled a time when the government was

a “leader in technological innovation.” She suggests federal agencies have fallen so far behind

that they will have to work aggressively just to catch up with basic private sector practices.

(Burr, 2018) Kent states, “agencies will have to pursue decades-long data and IT modernization

plans so that government services meet the expectations that citizens have cultivated from

commercial tech experiences.” (Burr, 2018) Failure to keep up with technology, according to

Sam Blakeslee, head of the Cal Poly Institute for Advanced Technology and Public Policy, can

result in a troubling disconnect between government and its citizens. Referring to the ubiquity of

technology, such as smartphones, Blakeslee points out, “this technology revolution goes far

beyond the amazing new gadgets we wear, carry, and touch almost every hour of every day. The

revolution didn’t stop at giving us neat new George Jetson-type devices. It went quickly toward


deconstructing the entire economic ecosystem that we once knew as our status quo.” (Eidam,

2015)

Artificial Intelligence a National Priority

Driven by large amounts of available data and increased computing horsepower, AI has

developed wide-reaching applications from marketing, to finance, news, transportation, critical

infrastructure, science, health care, and the military—all of which drive the American economy.

In February of 2019, President Donald J. Trump signed an executive order that laid out a broad

plan for American dominance in AI. The presidential directive, which comes at a time when

relations between China and the U.S. are strained, does not allocate any funding but does call on

federal agencies to prioritize their existing funding for AI projects. In 2016, the Obama

administration released a report on the future of artificial intelligence and a strategic plan for

federally-funded AI research, titled “The National Artificial Intelligence Research and

Development Strategic Plan.” The National Science Foundation announced on June 21, 2019,

that it would join federal partners in reevaluating the plan’s priorities to add a partnership

building element. According to Jim Kurose, NSF assistant director for Computer and

Information Science and Engineering and the co-chair of the AI Subcommittee, the AI field is

interdisciplinary and cross-sector by nature. (NSF, 2019) Kurose asserts public and private

partnerships provide much-needed opportunities for organizations to leverage the flow of people

and knowledge among academia, industry, and government to enhance economic growth and

global competitiveness as well as emphasizes the value of expanding research and education in

AI. (NSF, 2019)


Artificial intelligence (AI) is, according to former FEMA Deputy Administrator Richard

Serino, perhaps the most powerful of emerging new technologies and it is increasingly being put

to use in the service of emergency management. (Serino, 2018) He states, “in fact, there has

been such a profusion of disaster-related solutions imbued with various artificial intelligence and

data capabilities that emergency managers are under increasing obligation to develop the skills to

effectively assess the relative merits of various technologies.”

Private Sector Investment in AI Expected to Continue

As the cost of computing comes down, private equity investment in artificial intelligence

is expected to soar. Pull-through market technologies such as self-driving cars, digital assistants,

and virtual reality gear will continue to drive new technology development. The Organization for

Economic Co-operation and Development, (OECD) released a report in 2018 that studied global

investment trends in AI technology. It states venture capitalists are “stepping up equity

investments in artificial intelligence (AI) start-ups, reflecting a growing interest in AI

technologies and their commercial applications.” (OECD, 2018)

After five years of steady increase, private equity investment in AI has accelerated since

2016, with the amount of private equity funding doubling from 2016 to 2017. In total, OECD

estimates that “more than $50 billion was invested in AI start-ups during the period 2011 through

to mid-2018. This surge in private investment suggests investors are increasingly aware of the

potential of AI and are crafting their investment strategies accordingly.” (OECD, 2018)

FEMA Deputy Administrator Richard Serino notes corporate endeavors are “emerging in

support of the development of new (AI) tools and opportunities for emergency managers. “For

example, IBM has been working to combine its Watson technology with the weather data it


acquired with its purchase of The Weather Company to enable third-party developers to

experiment with a range of disaster-related solutions. Facebook has aggregated geolocation data

from its users and provided it to assist humanitarian organizations after natural disasters. Google

is using AI to develop enhanced flood warnings in India as part of its Google Public Alerts

program. These solutions are agile and have the potential to reach millions of people with their

light touch and mass-market appeal.” (Serino, 2018)

Traditional Newsrooms Find Hope In Artificial Intelligence

Newsrooms around the globe underwent a dramatic shift when news began to move

online. Pocket-sized digital stories became the norm with longer in-depth pieces slowly

dissolving into micro-blogs, podcasts, or other mobile-compatible formats. For some time,

statistic-rich stories such as sports, weather, corporate earnings reports, and crime have been

written by computers. Short, factual, and far from Pulitzer contenders, they get the job done.

Robot reporters can be more thorough than human reporters as the Global Investigative

Journalism Network found. Human journalists tend to single-source stories while AI-based

software can import multiple sources of data, compare them, and identify trends and recognize

patterns. (Ronderos, 2019) Using Natural Language Processing (NLP), robot reporters humanize

data into sophisticated sentences with adjectives, metaphors, and similes. Using data points from

social media posts, they can even report on crowd emotions at an unusually close sporting event.

(Ronderos, 2019)

While the rise of robot reporters may sound the alarm to many, a growing number of

journalists seem to embrace the use of AI in the newsroom. According to the Global

Investigative Journalism Network, AI could “become the savior of the trade— making it possible


to better cover the increasingly complex, globalized and information-rich world we live in.”

(Ronderos, 2019)

AI-based tools in the newsroom are adept at more than just churning through information

and writing stories. They also are employed to fact-check. Reuters has employed News Tracer, a

system that automates news production using Twitter data to identifying emerging conversations

from more than 12 million tweets per day. It then contextualizes the story with a summary and

topic. (Liu, et al., 2017) News Tracer helps identify news stories by looking for patterns in

emerging tweets. AI-based image recognition tools help newsrooms identify objects, locations,

and even recognize faces. For example, The New York Times uses an AI program called

Rekognition to identify members of congress in photos. Wibbitz is a software program that

automatically creates scripts and can produce short videos in rough form. (Ronderos, 2019).

How It Works: Data Gets Sucked In, a News Story Gets Pushed Out

Automated journalism relies on four major components: available data, algorithms,

machine learning, and natural language processing. The algorithm component is the engine and

coordinating filter for the incoming information. Machine learning, as best described by the SAS

Institute, a global leader in analytical software, is a data analysis method that has the ability to

automate analytical model building; it is a branch of artificial intelligence based on the premise

computer systems can learn by using data to identify patterns and make decisions with limited

intervention by humans. (SAS Institute, 2019)

Natural language processing is a tool that helps computers “understand, interpret and

manipulate human language.” It incorporates elements from several disciplines, including

computer science and linguistics. In short, machine learning works to fill the gap between human


language and computer understanding. (SAS Institute, 2019)

Figure 1. Graphic representation of automated journalism.

In a 2016 paper published by the Columbia School of Journalism, Andreas Graefe breaks

down available technology solutions from simple code that extracts numbers from a database,

which are then used to fill in data field in pre-written templated news stories, to more

sophisticated approaches that analyze data that would make stories more compelling narratives.

She adds that “the latter relied on big data analytics and natural language generation technology

and emerged from the data-heavy sports reporting and both major providers of natural language

generation technology in the United States, Automated Insights and Narrative Science, began by

developing algorithms to write recaps of sporting events automatically.” (Graefe, 2016)


Figure 2. Illustration of how automated journalism works. (Graefe, 2016)SOURCE: https://pdfs.semanticscholar.org/c56d/609b3cb2ff85a3e657d2614a6de45ad2d583.pdf

Literature Review Conclusion

Artificial intelligence is a fast-moving disrupter. Fueled by faster computing power and

increased private sector investment, organizations without the capacity to handle the forthcoming

torrent of information will struggle. AI-based systems will soon produce information at such a

rate and volume, non-AI entities like the joint information center will require AI-based systems

of their own to keep up, as well as control the narrative. Advancement in artificial intelligence

has now risen to become a national directive, as well as a prominent component in the future of

emergency management, but not its potential to support large scale JIC operations. While a

growing amount of research on automated journalism can be found, there is little specific

research on how these AI systems may be adopted for use in the joint information center, making

this research somewhat unique. Because large newsrooms and joint information centers bear

many similarities in organizations, as well as function, understanding how they have leveraged

https://pdfs.semanticscholar.org/c56d/609b3cb2ff85a3e657d2614a6de45ad2d583.pdf


the power of artificial intelligence to increase productivity and support decision-making provides

a foundation from which to begin exploration.

METHODOLOGY: CASE STUDY AND SURVEY

This thesis explores the implementation of AI-based content creation technologies to

improve joint information center accuracy and efficiency by examining three cases of automated

journalism implemented in three major newsrooms: The Washington Post, Associated Press, and

The Guardian research method includes a review of published literature, technology white

papers, and analysis of resulting media coverage. While the literature review helps establish the

viability of said technology within a fluid and high-volume content producing operation, a

standard survey was conducted among working public information officers and communications

professionals to measure awareness of and general interest in the application of similar AI-

augmented technology in the joint information environment.

Survey population sample

This study sought to gather a diverse population of professional communications

professionals as possible, and in such a manner, the sample represents the total population as

closely as possible. An eight-question standard survey was distributed to a nationwide population

comprised of public information officers, public affairs officers, and public relations

professionals, and emergency managers or specialists that often step into the role of public

communicators. The potential sample was vetted by distributing the survey through professional

organizations that represent professional public communicators.


Those organizations were:

2019 EMI Master Public Information Officer Program Cohorts I, II, III

Public Relations Society of America

Defense Information School Public Affairs Officers

American Association of Airport Executives Digital Media Summit

LinkedIn (Online)

The survey instrument was designed to identify each respondent in their specific role to

ensure respondent qualification. The option to determine one’s position as “other” was included

to catch respondents that may not be qualified to participate. Those responses designated as

“other,” if complete, were recorded and included in this study, but are kept separate from the

primary body of data. While primarily designed to be a quantitative survey instrument,

opportunities for respondents to provide qualitative feedback were included. These opportunities

were marked as optional, and unlike the qualitative questions, were not set as required responses.

Incomplete or abandoned surveys were not considered qualified were discarded. Those

respondents that did provide substantial qualitative feedback were invited to participate in an

optional narrative interview.

Case Study: The Washington Post’s Heliograf

In September of 2016, The Washington Post launched its own AI storytelling technology

to create included 300 short stories and alerts on the Summer Olympics in Rio de Janeiro and

500 articles around the 2016 U.S. election, generating more than 500,000 article clicks. (Moses,

2017). In its first year on the job, the AI-system, known as Heliograf, part of The Post’s Arc

Publishing Platform, autonomously produced and published 850 articles. It also covers D.C- area


high school football games, and along with creating stories, Heliograf generates its own tweets.

(Moses, 2017).

Figure 3. Screenshot of an AI-generated tweet. (Moses, 2017). ©2017 Washington PostSOURCE: https://digiday.com/media/washington-posts-robot-reporter-published-500-articles-last-year/

Screenshot by author

In 2016, The Washington Post was able to cover every major race on Election Day—

every House, Senate and gubernatorial race in the nation—with the help of Heliograf. Lukas I.

Alpert writes about the feat in a Wall Street Journal article, published on Oct. 19, 2016. Alpert

explains that The Post’s idea was to use artificial intelligence to bolster the work of some 60

reporters already assigned to election coverage. The reporters focused their attention on covering

the high-profile races and those expected to be pivotal, while AI filled in the gaps. (Alpert, 2016)

Using templates and pre-written previews, Heliograf automatically updated stories as the

results came in. Human journalists would then added analysis and color, along with using geo-

https://digiday.com/media/washington-posts-robot-reporter-published-500-articles-last-year/


targeting to position stories at the top of the page when viewed by a reader from a specified area,

customizing content, making it more engaging for the audience. Heliograf, according to Alpert’s

article, would also alert the newsroom if a candidate lagged behind or if results concluded or

started trending in an unexpected direction. (Alpert, 2016)

Case Study: The Associated Press

In 2014, the Associated Press (AP) began its digital transformation using news writing

algorithms, realizing it had to not only do more—it had to do it better. (Stroh, 2017)

The AP needed volume to enable more choice and satisfy additional needs from affiliate

news organizations, (Stroh, 2017) They also needed a way to differentiate themselves, and AI

was the tool that enabled them to do both. (Stroh, 2017)

Stroh adds that before the AP’s partnership with Automated Insights, the company that

powers their AI-enabled content management system, “an AP staff of 65 business reporters could

write about 6 percent of earnings reports possible of America’s 5,300 publicly held companies.”

Twenty-four months later, the AP’s AI system was able to write 3,700 quarterly earnings

stories. (Stroh, 2017) The AP estimates that artificial intelligence tools have freed up 20 percent

of their reporters’ time spent covering stories on corporate earnings, and that AI is also moving

the needle on accuracy. (Moses, 2017) In the case of AI-augmented financial news coverage by

AP, the error rate in the copy decreased, even as the output of stories increased more than

tenfold, said Francesco Marconi, AP’s strategy manager and AI co-lead. (Moses, 2017) Marconi

continues that in addition to generating news coverage, AI has helped the well-known global

wire service extract hidden insights from data and improve the editorial workflow process by


automating such tasks as tagging photos, writing cations for videos and even deploying AI-

power cameras able to photograph angles not available to human journalists. (Stroh, 2017)

Figure 4. Associated Press news story written by robot reporter

Case Study: Guardian Australia's Reporter Mate

In late January of 2019, Guardian Australia published its first robot-written news using

an AI-powered open source stem called ReporterMate. The creation of Nick Evershed, Guardian

Australia’s data interactives editor, is an open-source application that gathers data, analyzes it,

and then spits out a news-style report in natural language, complete with publication-ready

graphics. (Evershed, 2019)


Evershed admits that ReporterMate is not an original idea, besides being based on one of

his previous projects called DisclosureBot, it differs in the fact it is not a proprietary commercial

service. Evershed feels that if news media want to control how AI technology is used within the

industry, then an open-source2 construct is the best way forward. Evershed has made the source

code for ReporterMare available at https://github.com/nickjevershed/Reportermate-Lib .

Bots3 often get a bad reputation, stemming from malicious attacks on websites and the

non-human traffic they generate on social media. While the use of bots in the newsroom is

nothing new, bots like those created by Evershed have disrupted the news industry and forced

them to tap into the raw information readily available on social media. Guardian Australia's

DiscloureBot (@AusDisclosure), for example, tweets anytime an Australian politician or

political party amends their donations or gift and business interest list. In also may include a link

to the document.

2 Open source is a term to describe software for which the original source code is freely available and may be resdiributed and modified, thus improving the overall product.3 Bots are autonoumous programs residing on a network or on the internet that may interact with other systems or with human users, especially those designed to behave like a human user.

https://github.com/nickjevershed/Reportermate-Lib


Figure 5. Sample of Guardian tweet. ©2019 The GuardianSOURCE: https://twitter.com/ausdisclosure?lang=en

Screenshot by author

As with other AI-automated journalism tools in other newsrooms, ReporterMate has

achieved similar boots in coverage, as well as efficiency, generating publication-ready stories

complete with explanatory graphics.

Figure 6. Sample of Guardian news story published Jan. 31, 2019 and written by ReporterMate, an artificial intelligence storytelling platform.

SOURCE: https://www.theguardian.com/australia-news/2019/feb/01/political-donations-plunge-to-167m-down-from-average-25m-a-year. Screenshot by author.

https://www.theguardian.com/australia-news/2019/feb/01/political-donations-plunge-to-167m-down-from-average-25m-a-year

https://www.theguardian.com/australia-news/2019/feb/01/political-donations-plunge-to-167m-down-from-average-25m-a-year

https://twitter.com/ausdisclosure?lang=en


RESULTS

Primary Survey Results

The researcher distributed the first of two survey instruments to the selected population in

late June of 2019. Responses were suspended Aug. 12, 2019. Because the survey was distributed

electronically with a sharable link, the total population number is not quantifiable. However,

there 42 responses with 33 of those responses qualified for inclusion in the study. Late,

incomplete, or abandoned surveys were discarded. The population accepted for this research is

33, of which 31 have worked in an active joint information center and the remaining two in an

exercise-driven joint information center.

The population, comprised of professional public information

officers and other public communicators, of which 51 % identified as full-time public

information of public affairs officers with a third working at the city or county level, and 18% at

the state or federal level. The population break-out is as follows:

Figure 7. Respondent self-assessment of knowledge of AI-based content creation systems.



To further gauge qualification, the survey asked respondents to provide a self-assessment

of their knowledge of artificial intelligence-based content creation systems. The results indicated

of the 33 respondents, 24% indicated they had either a “strong” or “extremely strong”

understanding of the concept of artificial intelligence, with 30% identifying as having a “basic

understanding: and another 30% admitting “no understanding” of artificial intelligence or its

application to create content.



Moving forward, the survey then asked a series of questions about individual comfort

levels with AI-based technology performing specific tasks within a joint information center

environment. These tasks included the autonomous writing of reports and press releases, social

listening, social media monitoring, social media creation and autonomous posting of said social

media, automated translation between language, and the independent engagement of special

needs populations.

A qualifying option of “human-in-the-loop” was added to the survey choices to identify

those interested and open to AI technology, but with the caveat of human oversight.


Figure 10. Degree of comfort with reports and press releases written by artificial intelligence.

Figure 11. Degree of comfort with artificial intelligence engaging in social listening.


Figure 12. Degree of comfort with social media content written and scheduled by artificial intelligence.

Figure 13. Degree of comfort with social media posted by artificial intelligence.


Figure 14. Degree of comfort with artificial intelligence directly engaging stakeholders

and directing them to vetted resources

Figure 15. Degree of comfort with artificial intelligence providing emergency information

such as boil water alerts, road closures, evacuation information.


Figure 16. Degree of comfort with artificial intelligence translating between languages

Figure 17. Degree of comfort with artificial intelligence directly engaging special needs populations.


Overall, results from the preliminary survey illustrate a relative degree of comfort among

the respondents when it comes to working with AI-based technology. While respondents seemed

somewhat open to using the technology, it was evident that most are far more comfortable with

the technology itself than they are relinquishing full message and publishing control to it. The

high number of responses indicating a preference for human-in-the-loop (HITL) technology

shows promise for future application in the joint information center, but at a more incremental

and graduated pace.

Secondary Survey Results

Results from a second survey, which gauged experience in a joint information center, as

well as whether or not the respondent finds the concept of an AI-augmented content management

tool beneficial, revealed an overwhelming 100 percent interest. Additionally, two respondents

indicated interest in being on the development team if the concept gains momentum.

Figure 18. Would you find an AI-augmented content creation and management

tool be of benefit in the joint information center?


Some respondents completed the optional “Thoughts and Suggestion” box and provided

additional ideas and consideration. They include:

Such a tool should be able to interface with WebEOC.

Using a standard (but configurable) content management tool would be helpful as it

would allow for required training and proficiency before an emergency.

The tool would need to be scalable and flexible to meet the needs of different sized JICs

and emergencies.

The process for writing and reviewing and approving communications within the JIC is

challenging. The tool should include content review and approval capabilities.

This tool could provide a good structure for setting up and organizing a JIC, as well as

managing—and extracting value from—the volumes of incoming information generated

during an emergency.

A content management tool or software package that allows those working on a JIC to

communicate, interact, store, and generate information is a great idea since many times,

communications within the JIC can be difficult.

The large newsroom and the joint information center are close cousins. To survive,

newsrooms have bet their future on AI and AI-based news writing “robots” to augment human

capability, improve efficiency, decrease errors, and reach specialized populations. Joint

information centers, also faced staffing challenges, increasing amounts of information to parse

and the requirement to write and produce formulaic products echo a similar need for data in and

written content out. Findings from this study include:


Significant advances in AI content creation and management technologies are readily

available.

Private-sector use of AI-based news writing algorithms (robot reporters) has improved

news coverage, reduced errors, and identified critical information trends that may have

otherwise gone unnoticed.

AI technology is already a significant component in the emergency management field,

but there are no components that support the joint information system.

Public information officers indicate they are comfortable working with AI and AI-based

technologies, but are not ready to relinquish content creation or total control of public

information messaging, yet. Human-in-the-loop protocol can help.

Public information officers indicate they use a variety of tools to accomplish their

mission. In addition to WebEOC, multiple tools such as Google, Trello, Slack,

TweetDeck, and a variety of content management and web tools such as Drupal and

Joomla are used—creating a fragmented and very siloed flow of information. The

information gleaned from these tools still must be extracted and repackaged by hand, by

the public information officer before release.

Public information officers have expressed enthusiastic support for the development of an

AI-based content creation and management tool—particularly one that would interface

with WebEOC.

Cost, politics, and agency liability rank among the top concerns with deploying and using

AI technologies in the emergency public messaging environment.

RECOMMENDATIONS


This thesis offers three case studies demonstrating how major newsrooms have

successfully integrated AI-based news writing algorithms—automated journalism tools—into

their workflow.

The findings of these case studies present some convincing evidence for the application

of similar AI technology in the joint information center. While the researcher does not proport AI

to be an all-inclusive solution to improving JIC performance, it does offer a pathway for further

consideration, investigation, and application.

Recommendations

Recommendation 1: Joint Information Centers should be looking at private-sector

successes, such as major newsrooms, for ideas to organize and modernize operations. Automated

journalism, fueled by social media, will exponentially increase the amount of information created

before, during, and after an emergency. Existing joint information center operations will require

more scalable and agile processes to remain relevant to audiences and ahead of the non-official

narrative.

Recommendation 2: Development of as standard, yet scalable content management

system for the joint information center, based on automated journalism technologies. The

system’s user interface would be designed on standard newsroom content management systems

where documents are shared, and published either directly to a JIC human editor or be

automatically published. Additionally, this package would include a user-configurable dashboard

to quickly deploy and organize the joint information center, from scheduling to resource

requests, documentation, and archival of work products created, to name a few. This AI

platform, as identified nearly unanimously by the secondary survey results, should be directly


able to access and data and events from WebEOC master events logs. The concept would be to

create information unity and efficiency among multiple sources, much like a working newsroom.

Recommendation 3: Develop public-private partnerships among FEMA, DHS Science

and Technology Directorate and private-sector AI automated journalism companies, as well as

the users and developers of WebEOC to form an exploratory product development team to

discuss a way forward for a joint information content creation and management tool.

DISCUSSION

Advances in the application of artificial intelligence have made significant strides over

the past decade, entering almost every facet of the global economy, as well as everyday life. As

the next decade opens the doors to self-driving cars, self-flying planes, encrypted currencies, city

planning, environmental stewardship, virtual surgery, and among many, writing news, the world

sits on the edge of an unprecedented shift in the value of data. For status quo organizations that

cling to the traditional “wait and see” attitude, they may find it much harder to catch up. With

that in mind, the researcher feels it is of value for the public information field, its professionals

as well as the agencies they support—and those that train them— to make an effort to understand

how artificial intelligence will shape the joint information center of the future. Emergencies may

not change, but how the news agencies generate information, and how stakeholders receive it

will.

Along with communication, high-volume information scrubbing will become essential, as

well as the ability and extract relevant data from the information available will become a

necessary skill. Large international newsrooms, with shrinking budgets, are now leveraging the


power of artificial intelligence to power automated journalism. Tireless, these AI-based robot

scribes have helped redefine newsroom workflow and productivity. Joint information centers,

which share many traits with newsrooms, could learn from recent newsroom technology shifts;

thus the researcher poses an exploration of how AI-based content creation—automated

journalism—tools used in newsrooms may be adapted to improve general information messaging

and joint information center operation.

One of the most cumbersome challenges with this project was the fact, little scholarly

research specific to the application of artificial intelligence exists. Sources within the literature

review are derived from technology or emergency management sector publications, white

papers, and from actual news coverage of the systems from the owning newsroom or other

credentialed news entities—in some cases, the actual software creator. The researcher’s goal is to

provide the reader with a basic understanding of automated journalism and the movement to

make AI a national priority, then walk them through three case studies to illustrate successful

implementation. The researcher then asks the reader to explore the concept of an AI-augmented

tool for use in the joint information center environment. The topic’s broad perspective and the

multitude of definitions made it challenging to communicate the concept and word survey

questions. The researcher identifies the following weaknesses in this thesis:

Significant lack of existing academic research or case studies on artificial intelligence

used in the joint information center environment.

The subject of artificial intelligence, itself, may have intimidated the prospective survey

population resulting in many not attempting to complete the survey at all. Analytical

software associated with the survey link indicated 248 pageviews with only a 13.7%


conversion rate4 A larger population would have yielded far better results.

The continuing advancements in technology presented challenges in researching, as well

as finalizing recommendations. While the researcher does offer specific

recommendations, they are provided in a conceptual form rather than supported with

particular technologies, hardware, and software.

While specific potentials and limitations of AI-based technologies are identified within

this thesis, it lacks a rigorous discussion of each. To include such would require a subject

less conceptual and one with more specified technologies, hardware, and software.

Table 1

Comparison of benefits and challenges of AI-augmented JIC, prepared by the author

Promising Benefits for AI-Augmented JIC Challenges, Limits for AI-Augmented JIC

Data automation Routinely scrubs multiple data sources for specific details, looks for patterns, anomalies, powers decision making.

Expand fact-checking

The technology available, but no particular applications for JIC

The technology and tools are employed in the private sector, and some aspects of emergency management, but no movement toward proving tools for ESF 15 or JIC.

Task automation Uses templates to generate reports, fact sheets, social media analytics, briefings, updates, expenditures, calculates personnel costs, media analysis reports.

Development cost Costs to license existing technology. The Washington Post sells Heliograf’s technology through Arc Publishing, which starts at $10,000 per month. (McCoy, 2017) Open-source software programs are

4 Conversion rate is a term used in social media or web analytics to refer to the ratio of visitors to the number of completed online activities.



available but would require funding to develop.

Helps shape a standard workflow for JIC operation

PIOs arrive at a JIC, each with a toolbox. Duplicative efforts waste time and risk error. A standard (yet scalable content system) can improve operational performance.

Program sustainability cost

Once developed, the funding required to sustain the program, software integrity, initial set up, and compatibility with OEM partners.

Image recognition Identifies locations, persons, structures, and events. Aids in the discovery of emerging problems or emergencies.

Training A substantial training program would need to be considered.

Social listening Able to detect emerging conversations, concerns, inaccuracies. Identifies and can help pre-empt disruptive threads.

The legality of data use Careful vetting of proprietary data should be considered. Although the AI program may be an open-source or public domain entity, running proprietary data (subscription-paid) through it may pose legal concerns.

Commentary moderation

Able to detect emerging conversations, areas of concern, improvement, as well as mitigate inflammatory, inappropriate discussions and imagery, as well as to detect other bot activity. Identifies and can help pre-empt disruptive threads.

Standard cyber issues As with any software system, the general site and network security concerns ensue.

Content creation Can create new stories, press releases, reports,

Program sustainability cost

Once developed, the funding required to



social media posts, limited videos, social media art. Can recommend Prepare for human review.

sustain the program, software integrity, initial set up, and compatibility with OEM partners.

Automates event documentation

Can create, as well as automates document storage, back up and archival of JIC activities and work products

Training and familiarization

A substantial training program would need to be considered. Research indicates there is a knowledge gap, as well as trust issues among PIOs and AI.

Translation between languages

Can easily translate between multiple languages, to include regional dialects

The legality/privacy of data use

Careful vetting of proprietary data should be considered. Although the AI program may be an open-source or public domain entity, running proprietary data (subscription-paid) through it may pose legal concerns.

Accessibility AI may assist in creating inclusive designs—rather than expecting people to provide something in an accessible format; everything may become accessible in the future.

Agency liability Who is at fault and who pays in the event something goes wrong? Multiple possibilities from the end-user to the software developer.

Special needs populations and disabled persons

Voice recognition, text-to-speech, and biometric login for those working in the JIC as well those seeking assistance from emergency resources.

Reduce/identify gender, ethnic, religious bias

AI algorithms can identify and help reduce gender, ethnic, and religious bias



in public communication products and activities.

Facilitates transparency

Automated tasks can show a transparent chain of custody for information, documents, and information. Digital forensics helps identify information sources.

Speeds up open records requests

Searchable documents and work products can be easily retrieved, screened, and perhaps even redacted for release to the media and public. Better tracking of open records requests.

Suggestions for Future Work and Research

Recommendations for future work on this topic would include:

Development of a working, small-scale, open-source prototype tool to facilitate the

evaluation of potential JIC application

Exploring available grant funding for joint information center technology and technology

training programs

Re-evaluation of current PIO skillset to build leaders for the data-driven JIC of the future

CONCLUSION


The potential for customized AI-based content tools in the joint information center offers

many promising benefits, as well as some new—and familiar challenges. Technology

investments within the private sector and emergency management field are increasing, but

investment in compatible tools for joint information center operation is minimal. Public

information officers report to the joint information center with individual toolboxes of

proprietary applications. In many cases, they use a tool such as Google Docs in an attempt to

unify themselves and create a quasi-content management system. Private sector case studies have

proven successful with the implementation of AI-based content creation and management tools.

Survey results from this research indicate public information officers have a keen interest in a

digital transformation that could, possibly, include similar AI-based tools. Whether this is a

project best suited to a private entity or perhaps a public-private partnership would be best suited

for this, is yet to be determined. However; if this thesis does nothing more than raise awareness

to and open a discussion exploring the need for a specialized content management tool that can

be deployed into joint information centers, much like WebEOC, and based on AI-tools used in

newsrooms, the researcher will consider it a success.

REFERENCES


Alpert, L. I. (2016, October 19). The Wall Street Journal. Retrieved August 27, 2019, from Washington Post to cover every major race on election day with help of artificial intelligence: https://www.wsj.com/articles/washington-post-to-cover-every-major-race-on-election-day-with-help-of-artificial-intelligence-1476871202

Burr, J. (2018, May 24). These tech problems could hurt the government for years. Retrieved June 10, 2019, from Federal Times: https://www.federaltimes.com/it-networks/2018/05/24/why-the-government-is-playing-technology-catch-up/

Copeland, B. (2019, May 9). Artificial Intelligence. Retrieved July 22, 2019, from Encyclopædia Britannica: https://www.britannica.com/technology/artificial-intelligence

Dans, E. (2019, February 6). Meet Bertie, Heliograf and Cyborg, the new journalists on the block. Retrieved July 6, 2019, from Forbes: https://www.forbes.com/sites/enriquedans/2019/02/06/meet-bertie-heliograf-and-cyborg-the-new-journalists-on-the-block/#2a71e6e8138d

Eidam, E. (2015, August 13). Governments could pay high price for lagging on tech. Retrieved May 6, 2019, from Government Technology: https://www.govtech.com/state/Dangers-Lurk-in-Business-as-Usual-Expert-Warns.html

Elizabeth Blankespoor, E. d. (2017, August 14). Capital market effects of media synthesis and dissemination. Review of Accounting Studies, Forthcoming, 1-2. Retrieved May 30, 2019, from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2872784&download=yes

Evershed, N. (2019, January 23). Why I created a robot to write news stories. Retrieved from The Guardian: https://www.theguardian.com/commentisfree/2019/feb/01/why-i-created-a-robot-to-write-news-stories

Graefe, A. (2016). Guide to automated journalism. Columbia School of Journalism.

Kazuo Yano, P. I. (2017). Artificial intelligence for happiness of people.

Liu, X., Nourbakhsh, A., Li, Q., Shah, S., Martin, R., & Duprey, J. (2017). Reuters Tracer: Toward Automated News Production. Retrieved August 22, 2019, from https://arxiv.org/pdf/1711.04068.pdf

McCoy, J. (2017, November 12). Competent robots are here. Retrieved August 29, 2019, from Content Marketing Institute: https://contentmarketinginstitute.com/2017/11/content-creation-robots-examples/

Moses, L. (2017, September 14). The Washington Post's robot reporter has published 850 articles in the past year. DigiDay. Retrieved May 30, 2019, from https://digiday.com/media/washington-posts-robot-reporter-published-500-articles-last-year/


NSF. (2019, July 21). NSF joins federal partners in announcing update to national AI research and development strategy. Retrieved July 24, 2019, from National Science Foundation: https://www.nsf.gov/news/news_summ.jsp?cntn_id=298770&org=CISE&from=news

OECD. (2018). Private equity investment in artificial intelligence. OECD Going Digital Policy Note. Paris, France: Organisation for Economic Co-operation and Development. Retrieved April 10, 2019, from https://www.oecd.org/going-digital/ai/private-equity-investment-in-artificial-intelligence.pdf

Oremus, W. (2014, March 17). The first news report on the L.A. earthquake was written by a robot. Slate Magazine. Washington, D.C., U.S. Retrieved Jun 6, 2019, from https://slate.com/technology/2014/03/quakebot-los-angeles-times-robot-journalist-writes-article-on-la-earthquake.html

Ronderos, M. T. (2019, January 22). How innovative newsrooms are using artificial intelligence. Global Investigative Journalism Network. Retrieved August 22, 2019, from https://gijn.org/2019/01/22/artificial-intelligence-demands-genuine-journalism/

SAS Institute. (2019). Natural Language Processing (IoT). Retrieved August 24, 2019, from SAS: https://www.sas.com/en_us/insights/analytics/what-is-natural-language-processing-nlp.html

SAS Institute. (2019). What is machine learning, and why does it matter. Retrieved August 21, 2019, from SAS: https://www.sas.com/en_us/insights/analytics/machine-learning.html

Serino, R. (2018, September 29). The role of AI in preparedness: practical advice from former FEMA deputy administrator. Homeland Security Today. Retrieved August 3, 2019, from https://www.hstoday.us/subject-matter-areas/emergency-preparedness/the-role-of-artificial-intelligence-in-preparedness-practical-and-policy-advice-from-former-fema-deputy-administrator/

Stroh, S. (2017, December 1). As AI technology advances, so does its ability to assist journalists. Retrieved from Editor and Publisher: https://www.editorandpublisher.com/feature/as-ai-technology-advances-so-does-its-ability-to-assist-journalists/

Ulanoff, L. (2014, July 1). Need to write 5 million stories a week? Robot reporters to the rescue. Mashable. Retrieved May 30, 2019, from https://mashable.com/2014/07/01/robot-reporters-add-data-to-the-five-ws/

Documents

cdn.ymaws.com€¦ · Web view2020-03-04 · “For example, IBM has been working to combine its Watson technology with the weather data it . ... The topic’s broad perspective