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By Frederick A. Miller,
Judith H. Katz, &
Roger Gans
“The addition of a new class of worker, driven by AI, promises to challenge the path to greater inclusion
by having the potential to exponentially increase disruption not just in organizations but in society,
government, and our everyday lives.”
AI I AI2
The OD Imperative to Add Inclusion to the
Algorithms of Artificial Intelligence
Since its beginnings, one of the functions
of OD has been to create organizations that
enable people to do their best work and
to create workplaces built on principles
of democracy and participation. A major
element of creating such workplaces is
identifying and ameliorating discrimina-
tory practices and cultures in organiza-
tions. Artificial intelligence (AI) is in the
process of complicating and confounding
that function in ways we may not have
seen coming. There are growing concerns
about human (and other) biases being built
into the machine-learning algorithms that
are increasingly impacting our organiza-
tions, their processes, and our lives. But
just as AI has the potential to reify and
magnify the effects of human bias, it also
offers unprecedented opportunity to build
inclusive practices into the fundamental
practices and processes of organizations.
As the following will show, it is clear
that responsible AI developers must find
ways to incorporate awareness of the
potential for bias and the value of inclusion
into the algorithms that guide machine
learning processes. But our experience sug-
gests that if the developers of AI systems
hope to eliminate discrimination and build
inclusion into their software, they first will
need to do those things with their own
culture. In addition, those who are work-
ing in organizations need to be mindful of
the potential for bias in such processes and
software so they can insure the processes
being implemented are not contributing
to biases that may already exist within the
workplace. In this article we discuss some
of the dangers and opportunities presented
by AI, and the implications for organiza-
tions, the people of those organizations,
and OD practitioners tasked with assisting
them to survive and thrive.
A New Class of Worker Brings Danger
and Opportunity
Organizations have been, and continue
to be, disrupted and transformed by the
addition of women, people of color, people
from different countries and ethnic origins,
people with different sexual orientation
and gender identities, and differently-abled
people into the workforce and workplace.
Organizations that have learned to leverage
6OD PRACTITIONER Vol. 50 No. 1 2018
the added skillsets and perspectives of their
increasingly diverse workforces through
building cultures of inclusion have experi-
enced significant gains in productivity and
profitability (Katz & Miller, 2017; Miller &
Katz, 2002; Page, 2007). The addition of a
new class of worker, driven by AI, promises
to challenge the path to greater inclusion
by having the potential to exponentially
increase disruption not just in organiza-
tions but in society, government, and our
everyday lives.
Robots and other machines powered
by computerized algorithms are already
working alongside humans in factories
around the world. Some, with self-pro-
gramming machine-learning capabilities,
are performing customer service functions,
implementing marketing strategies, and
making consequential decisions that can
determine the opportunities we see, the
jobs we get, the products we buy, the prices
we pay, and the treatment we receive from
officers of the law and the courts. Robots
are the visible manifestations of artificial
intelligence—the hands and feet of AI.
Many of the manifestations and influences
of AI are less visible, however, and some of
these are proving to be problematic.
What is Artificial Intelligence Learning
from Humans?
Although feared by some, the great hope
of many people was that AI would give us
faster, wiser, fairer decisions and actions
without the downsides of human error,
fatigue, or bias. Through the magic of
machine learning, it would speed customer
service transactions, unstick the gridlock of
governmental and organizational bureau-
cracies, eliminate traffic jams, improve
medical diagnoses and treatments, and
relieve us of the burden of countless bor-
ingly repetitive tasks.
But who is teaching the machine?
And once activated, what will the machine
teach itself and other machines, especially
if what it learns is based on human history,
the content of the Internet, and the biases,
fears, and unexamined assumptions of its
coders, programmers, and model build-
ers? Many OD practitioners are trained to
identify manifestations of bias, oppression,
and discrimination in organizational
systems and culturally influenced data.
But the program developers who write
the algorithms that drive the machines
rarely receive such training (Mundy, 2017).
Without such knowledge, they can overlook
the danger that the data used to inform
the AI machine-learning process may have
culturally determined biases already baked
in. For example, AI-driven risk-assessment
tools currently in use in some places sift
through racially biased arrest records and
historical crime data to help courts make
decisions and police departments deter-
mine which neighborhoods should receive
greater scrutiny and coverage. In doing so,
they are actively reflecting, perpetuating,
and magnifying racial inequities caused by
societal prejudice (Crawford, 2016).
Bias Is Baked into the Data
It is too late to merely worry that human
biases might cross over into the computer-
ized programs affecting many individual
lives and organizational functions. Our
biases are baked right into our language
and the language-usage data AI systems
learn from (Caliskan, Bryson, & Narayanan,
2017). To cite a readily observable phe-
nomenon, AI-driven language translation
tools routinely add gendered stereotypes in
translating from gender-neutral languages:
Google Translate converts these
Turkish sentences with gender-
neutral pronouns: “O bir doctor. O bir
hemşire.” to these English sentences:
“He is a doctor. She is a nurse.” We
see the same behavior for Finnish,
Estonian, Hungarian, and Persian in
place of Turkish. Similarly, translat-
ing the above two Turkish sentences
into several of the most commonly
spoken languages (Spanish, English,
Portuguese, Russian, German, and
French) results in gender-stereotyped
pronouns in every case (Caliskan et
al., 2017).
In 2015, Google’s photo app—powered
by AI and machine learning processes—
identified black people in some photos
as gorillas (Barr, 2015). That same year,
a Carnegie Mellon University study
determined that AI-driven, search-based
advertising promising employment
assistance for obtaining high-paying
jobs—for $200,000 and higher—targeted
significantly fewer women than men
(Spice, 2015).
In bail and sentencing hearings in
courtrooms across the U.S., AI-driven
software systematically—and mistakenly—
rates black people as higher recidivism
risks than white people (Angwin, Larson,
Mattu, & Kirchner, 2016). Based on AI-
driven calculations, insurance companies
routinely charge residents of zip codes
with large minority populations up to 30%
more than residents from whiter neighbor-
hoods with similar accident costs (Angwin,
Larson, Kirchner, & Mattu, 2017).
Outcomes like these violate our expec-
tations. We assume machines must be
inherently fair and objective, that they can-
not help but analyze data without bias or
malice. But it is easy to forget that the pro-
gramming that drives the way AI analyze
data is originally created by humans. The
people who create the algorithms belong
to an industry culture that has bias against
women and African Americans, even if
based solely on their conspicuous under-
representation (Clark, 2016; Mundy, 2017).
Undoubtedly, few programmers would
intentionally embed bias in their work, but
it is hard to address problems you do not
see, and impossible to avoid doing things
you do not even know you are doing. Racist
and sexist assumptions are ingrained in the
wider societal culture, and perhaps even
more so in the tech industry subculture
(Mundy, 2017; Tiku, 2017).
Computers Learn Bias the Same Way
People Do
Machine learning is a process by which
computers sift through and process
enormous amounts of data with a goal of
identifying underlying patterns in the data,
which is basically the same way humans
learn (Emspak, 2016). In both cases, the
results are most often used to predict
future actions and behaviors. For early
human learning, the prediction can involve
what kinds of vocalizations and facial
expressions are most likely to elicit a hug,
7AI x I = AI2: The OD Imperative to Add Inclusion to the Algorithms of Artificial Intelligence
food, or a diaper-change. For a machine-
learning computer, the prediction is likely
to involve which humans to target for prod-
uct advertising and which advertising mes-
sages are most likely to produce sales, but
it can also involve who to loan money to,
who to hire, who to promote, and who are
the greatest risks for committing crimes or
appearing for trials.
Humans start processing data as
infants, and we learn the expectations of
our society from the actions and words of
all the people with whom we come into
contact. If there are biases in our upbring-
ing, we can sometimes learn to overcome
them if we consciously decide to do so. We
can learn to identify patterns of unfair-
ness and discrimination in other people’s
attitudes and behavior, and we can seek
out additional sources of information to
fact-check biased claims and act to cor-
rect them. But with up to 98% of our own
attitudes and decisions arrived at through
unconscious processes, it is harder to
identify the biases we hold implicitly (Sta-
ats, Capatosto, Wright, & Jackson, 2016).
Without training and vigilance, AI pro-
grammers and model-builders cannot help
but perpetuate these implicit, unconscious
biases in their work.
In machine learning, computers can
only process the data they receive, and
they may be restricted to considering
only specific facets of that data as part of
their initial human-sourced program-
ming. Add in the fact that virtually all data
available for analysis, including language
itself, has roots in human perception and
interpretation, and it becomes clear that
bias in machine learning is inevitable.
Like a child, a machine-learning computer
builds its vocabulary and “intelligence”
through pattern recognition (Bornstein,
2016)—for instance, in how often terms
and value judgments appear together on
the Internet and other sources (Caliskan, et
al., 2017). The word “nurse” is vastly more
often accompanied by female gendered
pronouns than by male gendered pro-
nouns. African-American names are often
surrounded by words that connote unpleas-
antness because people on the Internet say
awful things, not because African Ameri-
cans are unpleasant.
Prejudices produce actions that,
in turn, produce data. For instance, it
is widely acknowledged that arrest and
incarceration data reflect societal biases
against people of color, a pattern that is
readily seen in the way drug laws have
been enforced. While whites and African
Americans are equally likely to use illegal
drugs (Lopez, 2015), African Americans are
roughly three times as likely to be arrested
and prosecuted for possession of illegal
drugs (Common Sense for Drug Policy,
2014). A similar skewing of “objective”
data can be seen in percentages of women
serving on corporate boards and in senior
management positions (Warner, 2014).
Without specific instructions to consider
these kinds of patterns as evidence of bias,
machine-learning computers are likely to
use these data to predict that African Amer-
icans are three times as likely as whites to
be carrying illicit drugs (which can be used
as a justification for racial profiling and
stop-and-frisk practices), and that women
lack certain leadership qualities.
Don’t Ask, Because We Can’t Tell
Because machines are assumed to be fair
and unbiased, machine-produced predic-
tions, and the resulting recommendations
and decisions, are less likely to be ques-
tioned as biased than if they had come
from human agents (The AI Now Report,
2016). Not only is it less likely a machine’s
decision will be questioned, its decision is
also significantly harder to question than
a human’s. AI-developers such as Google
and Amazon consider their algorithms to
be proprietary information, and they pro-
tect them vigorously. Moreover, particularly
in advanced machine-learning systems, the
details of any individual prediction may
be based on literally billions of individual
digital processes and, as such, are opaque
even to the original coders (Bornstein,
2016; Knight, 2017). In other words, while
humans may be asked to account for and
justify what seem like biased decisions,
machines may not be able to provide
such explanations—and neither will their
creators.
1
Companies that offer AI services to
other companies may tout the speed and
capability of their processes, but unless
they offer transparency in the development
of their algorithms and the training of
their people, there is no way for their client
organizations to know if the AI package
includes baked-in biases. OD practitioners
working to eliminate institutionalized
“isms” in organizational interactions and
systems need to be aware of the potential of
AI to institutionalize those “isms” in ways
1. The European Union's General Data Protection
Regulation (GDPR), which goes into effect in May
2018, is meant to protect the right of individuals to
know how their personal data is used. There is a
view that the GDPR includes a "right of explana-
tion" as to how outputs are generated from machine
learning models. If true, companies that are build-
ing these models may need to demonstrate that
they have removed bias from those outputs. More
information is available at: http://www.eugdpr.org/
Companies that offer AI services to other companies may tout
the speed and capability of their processes, but unless they
offer transparency in the development of their algorithms and
the training of their people, there is no way for their client
organizations to know if the AI package includes baked-in
biases. OD practitioners working to eliminate institutionalized
“isms” in organizational interactions and systems need to be
aware of the potential of AI to institutionalize those “isms” in
ways that are much harder to detect, challenge, or change.
OD PRACTITIONER Vol. 50 No. 1 20188
that are much harder to detect, challenge,
or change.
Bias In, Bias Out:
Coder Culture Resists Change
As detailed above, AI-driven decision-mak-
ing processes can produce biased outcomes
that reflect the same sets of “isms” OD
practitioners and others have been work-
ing to ameliorate for decades. The evidence
suggests that if the biases exist in the
wider society, they will be “learned” by AI
systems that use the collective behavior and
data of the wider society to learn from.
This would be less of a problem if
the programmers writing the algorithms
on which machine-learning systems run
were more aware of the biases that exist in
the wider society, and by extension, in the
data sets produced by that society. Greater
awareness would make them better able
to ensure their coding efforts include
strategies for identifying patterns of bias in
societally-influenced data and safeguards
against existing, documented biases.
Making such awareness more normative
within the tech industry will be a challeng-
ing undertaking. Of course, as might be
expected in the tech industry, “there’s an
app for that,” with a proliferation of anti-
bias apps and training workshops that try
to reduce bias itself to an algorithm. But
there continues to be unwillingness among
some tech companies to change core parts
of their culture (Mundy, 2017).
Celebration of the tech industry’s
coding community as an elite, exclusive,
meritocratic club seems to be a deeply
entrenched ethos, sometimes defended by
claims that the sparse numbers of women
and African Americans are a consequence
of a reluctance to “lower our standards”
(Mundy, 2017). Racial stereotyping is a
well-acknowledged problem within the
software industry (Tiku, 2017). Gender
stereotyping, in contrast, seems to attract
more attention as well as greater pushback
when attempts are made to address it
(Wakabayashi, 2017). In recent years, the
tech industry has produced an increasing
number of reports on their companies’
diversity numbers, but little in the way of
positive change in those numbers or the
cultures that have produced and sustained
them. Studies have shown that women
leave the tech industry at twice the rate that
men do, and that the percentage of com-
puter science degrees earned by women
has decreased from 37% in 1984 to 18% in
2014 (Alba, 2017). Some diversity educa-
tion programs at tech companies have
seemed to produce boomerang effects, with
declines in diversity at some of the compa-
nies in which such programs were enacted
(Alba, 2017).
Not Just a Tech Issue:
AI’s Expanding Presence
It may seem tempting to focus warnings
about bias and discriminatory implications
of AI solely on the tech industry, but AI-
driven processes and services are already
part of the routine experience of everyday
life inside organizations of all sizes in all
industries. (How many times have you
Googled something today?) In fact, people
in organizations outside the tech indus-
try are even less likely to question the
algorithms and machine-logic on which
AI-influenced decisions are made than
within the tech industry. Without a keen
awareness of the potential for baked-in bias
in their AI-driven systems, some organiza-
tions are at risk of inadvertently becoming
party to actions that have a discriminatory
effect on their customers or their team
members, with potentially dire bottom-
line consequences in either case. This may
already be influencing hiring practices,
in which AI is increasingly used in talent
sourcing and acquisition. AI is being used
to make the candidate-selection process
faster and more efficient (Alsever, 2017),
and to root out human biases (Captain,
2016), but because it relies on human-pro-
grammed choice trees and human-gener-
ated data in deciding which candidates are
the best “fits,” the process also can rule out
some of the diversity organizations are—or
ought to be—seeking (Ghosh, 2017).
There is an upside in all this for those
seeking to address issues of inclusion and
diversity in organizations, however. The
potential for bias in AI systems can actu-
ally be a useful tool for OD practitioners.
By raising concerns about machine-based
biases in organizational practices, we may
also be able to raise awareness of how
unconscious bias is carried like an “equal
opportunity virus” (Dasgupta, 2013) by all
the humans of the organization. Consid-
ering its effects, of course, bias might be
more accurately considered an “un-equal
opportunity virus.”
AI and OD:
What’s Around the Corner
The rippling effects of AI promise to
impact virtually all facets of organizational
life, from decisions about who to hire and
promote, to design and marketing of prod-
ucts and services, to each organization’s
competitive position and reputation in the
global marketplace. Instead of disregard-
ing it as too technical for our purview, OD
practitioners need to see AI as a critical
element of the organization that needs to
be analyzed and addressed in regard to
its effects on institutionalized “isms” and
people’s ability to do their best work.
There are more implications for the
role of OD in addressing issues of AI than
can be covered in any single article. Some
of the AI-related issues OD practitioners
should anticipate facing include:
AI-fueled entrepreneurship. As access to
the tools of AI becomes more widespread,
it is likely to spur the growth of entrepre-
neurial start-ups that focus on applying the
potential of AI to solve an ever-widening
array of personal and commercial needs
(Lee, 2017). The role of OD will be criti-
cal in assisting these start-ups to avoid the
toxic-culture missteps of tech start-ups like
Uber (Noguchi, 2017) and SoFi (O’Connor,
2017).
Worker disruption and displacement.
Robots powered by AI-systems are already
replacing people in manufacturing plants,
warehouses, banks, and supermarkets
throughout the world. Other types of jobs
will inevitably be replaced or displaced as
AI systems become more sophisticated.
Challenges for the practice of OD are likely
to include working to create a culture that
enables people to work effectively with
robots and advanced AI: How will workers
9AI x I = AI2: The OD Imperative to Add Inclusion to the Algorithms of Artificial Intelligence
react and relate to non-human co-workers?
Will work teams accept an AI as a team-
mate or an agent of management? OD
practitioners will almost certainly need to
prepare the organization and its people for
widespread role-changes and potentially
stressful rounds of outplacement and
downsizing. The shapes of the changes to
come are difficult to predict, but preparing
organizations and the people in them for
inevitable and increasingly rapid AI-related
change is a necessity.
How to Add Inclusion to the AI Algorithm:
AI x I = AI2
To address the issue of bias in AI, it will
be essential to address the culture of the
coders as well as the code. Following are a
few suggestions for changing the culture of
the tech industry to be more inclusive and
more aware of the potential for bias in its
members and their code.
A strategy for creating culture change
within tech organizations and among cod-
ers. Before AI model builders—and those
working in partnership with them—can be
expected to root out biases and inequities
from their algorithms and AI-based prod-
ucts, they will need the competence and
capability to recognize and address those
biases and inequities. They will also need
to accept that those biases and inequities
are real, harmful, and consequential. Any
efforts to address the prevailing practices
and mindsets of the tech industry in this
regard must start with awareness that some
aspects of coder-culture have deep-seated
resistance to change, as noted above. The
following elements might better position
such a culture-change strategy for success.
Education. This may be an occasion
to brandish Churchill’s “those who fail to
learn from history are doomed to repeat it.”
Claims regarding “lowering our standards”
were exposed decades ago as pretexts for
excusing the exclusion of women, people
of color, and other undesirables (Cross,
Katz, Miller, & Seashore, 1994). It will be
vital to help those involved with AI to gain
greater competence in recognizing bias in
themselves and societally produced data.
Although many organizations are doing
education/training on unconscious bias,
that alone will not solve this issue. It has to
go beyond personal awareness to scrutiny
of how the data itself may be reflecting
biases and therefore to reimagine how to
use AI’s data-crunching abilities to avoid
perpetuating longstanding patterns of
discrimination.
Education in this direction could
include exposing tech industry members
to evidence of their own biases, as well
as documentation of biases in the data
used in machine-learning applications.
Motivation for change could be addressed
with additional education regarding the
value-added and return-on-investment
of inclusive practices (e.g., Katz & Miller,
2017; Page, 2007) as well as the costs
of bias-centered lawsuits and public
relations disasters.
Socialization. People cannot adopt a
cultural norm of inclusive behaviors until
they experience that norm. To accomplish
this, it will be necessary to establish pilot
groups that practice and model inclusive
mindsets and actions, and to nurture these
groups with education and organizational
support. Ideally, these pilot groups will
grow and eventually form the core of each
organization’s new culture.
Certification. A program that requires
and provides certification of competence
for recognizing bias and practicing inclu-
sive behaviors seems a particularly apt
accountability tool for the software indus-
try. AI programmers could be required
to pass multicultural competence tests or
attend education programs that address
bias, diversity, inclusion, and the practice
of self-as-instrument. They might also
undergo periodic recertification processes
that could include 360-degree reviews from
a diverse group including their team lead-
ers, colleagues, and direct reports.
A strategy for overseeing code quality and
addressing grievances. Because of the
specialized nature of this field, few people
possess the competence to recognize
defects or flaws in computer programs, and
fewer can trace potential problems with the
deep processes involved in machine learn-
ing. This has created problems with regard
to accountability and redress of issues that
affect people’s lives and livelihoods, and
suggests a need for creation of at least two
sets of human-staffed resources:
Organizational and industry-wide
peer-review boards. To protect the integrity
of the organizations producing the code,
there needs to be a process for some of
the AI-based products to have their code
(and the results of pilot runs for deep-
process machine-learning applications)
reviewed by an independent diverse panel
of experts before being released into the
public sphere.
Organizational and industry-wide AI
grievance panels. It should be assumed
that AI applications will produce unex-
pected and unintended inequities. Each
organization that produces AI-based
products could establish a standing panel
to address grievances from consumers
and others affected by their products,
either directly or indirectly. For consum-
ers who are not satisfied with the redress
given them by the manufacturing orga-
nization, there could be an industry-wide
For practitioners of OD, the challenge will be not just to assist
organizations to recognize and address the inherent dangers
presented by AI, but also to recognize the potential of AI to
integrate inclusive algorithms into the fabric of their existence.
Today, our task is to identify and root out the biases and
inequities of human society that are being absorbed through
machine-learning processes and presented as objective and
unquestionable reality. This is no small task! However, we would
be remiss if we did not also address the positive potential of AI.
OD PRACTITIONER Vol. 50 No. 1 201810
appeals panel that would hold organiza-
tions accountable.
A strategy that requires immediate action.
Regardless of the industry, OD practitio-
ners cannot wait for a world-changing
robot apocalypse to sound the alarm or to
start addressing the issues of AI. We need
to be mindful that this is happening now,
and at a pace that is accelerating. We can-
not settle for a “let the buyer beware” mar-
ket for AI products. We must enable the
buyer to beware when the organizations
we support are purchasing such products
until we are sure anti-bias safeguards are
in place and the awareness of the program-
mers and sellers is at a level that they
have made their products “safe” for our
diverse world.
We need to be willing to get into the
messy work of understanding how bias is
being built into these systems. We need to
be willing to venture outside our com-
fort zones in questioning the fitness and
objectivity of algorithms we may not have
the technological savvy to understand,
but whose biased effects we can and need
to identify.
Conclusion: This is Just the Beginning
Whether you believe AI has the potential
to create an Eden-like utopia (Lee, 2017) or
bring about the extinction of humankind
(Dowd, 2017) or something in between,
it is clear that AI will exert greater and
greater influence over virtually all aspects
of individual and organizational life (The
AI Now Report, 2016). For practitioners of
OD, the challenge will be not just to assist
organizations to recognize and address the
inherent dangers presented by AI, but also
to recognize the potential of AI to integrate
inclusive algorithms into the fabric of
their existence.
Today, our task is to identify and root
out the biases and inequities of human
society that are being absorbed through
machine-learning processes and presented
as objective and unquestionable reality.
This is no small task! However, we would
be remiss if we did not also address the
positive potential of AI. Consider applying
the power of AI to any of these “what ifs”:
»What if, instead of equating data with
purely objective facts, AI routinely iden-
tified patterns that could be the result
of societal or organizational biases and
discrimination, and sounded alarm
bells?
»What if, instead of selecting only the job
candidates who fit our existing organi-
zation profile, AI selected an array of
candidates who provide the perspec-
tives we currently lack?
»What if, instead of showing us only the
news we are likely to be most interested
in, AI showed us the news we most
need to see to be well-rounded, respon-
sible citizens?
These are the kinds of questions an
inclusive, culturally competent AI coding
and consuming community would ask
about how AI could enhance human inter-
action. What the results might be, we can
only imagine.
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Frederick A. Miller and Judith H. Katz are CEO and Executive Vice President
(respectively) of The Kaleel Jamison Consulting Group, Inc., one of Consulting
Magazine’s Seven Small Jewels in 2010. They have partnered with Fortune 50
companies globally to elevate the quality of interactions, leverage people’s
differences, and transform workplaces. Katz sits on the Dean’s Council, Col-
lege of Education at the University of Massachusetts, Amherst, and the Board
of Trustees of Fielding Graduate University. Miller serves on the boards of
Day & Zimmermann, Rensselaer Polytechnic Institute’s Center for Automated
Technology Systems, and Hudson Partners. Both are recipients of the OD
Network’s Lifetime Achievement Award and have co-authored several books,
including Opening Doors to Teamwork and Collaboration: 4 Keys that Change
EVERYTHING (Berrett-Koehler, 2013) as well as a book on workplace psy-
chological and emotional safety, to be published in Fall 2018. Miller can be
reached at fred411@kjcg.com. Katz can be reached at judithkatz@kjcg.com.
Roger Gans, MA, ABD, is a writer, consultant, and educator who specializes
in strategic communication. He has been a long-time thinking and writing
partner of Miller, Katz, and KJCG. An adjunct professor in the management and
communication departments of the Sage Colleges, his doctoral dissertation
examines how pro-social advocacy campaigns can exacerbate engagement
disparities in civic affairs, health care, and the workplace. His current consult-
ing projects include promoting health care services on Eastern Long Island
(NY) and development of a youth addiction services program in Iowa. Gans
can be reached at rgans@albany.edu.
OD PRACTITIONER Vol. 50 No. 1 201812
