# Atomist or Holist?
A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics
Dialogue

Travis Greene  National Tsing Hua University, Institute of Service Science,
Hsinchu, 30013, Taiwan  corresponding author(s): Travis Greene
<EMAIL_ADDRESS> Amit Dhurandhar  IBM Research, Yorktown
Heights, New York, USA  Galit Shmueli  National Tsing Hua University,
Institute of Service Science, Hsinchu, 30013, Taiwan

###### Abstract

In response to growing recognition of the social, legal, and ethical impacts
of new AI-based technologies, major AI and ML conferences and journals now
encourage or require submitted papers to include ethics impact statements and
undergo ethics reviews. This move has sparked heated debate concerning the
role of ethics in AI and data science research, at times devolving into
counter-productive name-calling and threats of “cancellation.” We diagnose
this deep ideological conflict as one between _atomists_ and _holists_. Among
other things, atomists espouse the idea that facts are and should be kept
separate from values, while holists believe facts and values are and should be
inextricable from one another. With the goals of encouraging civil discourse
across disciplines and reducing disciplinary polarization, we draw on a
variety of historical sources ranging from philosophy and law, to social
theory and humanistic psychology, to describe each ideology’s beliefs and
assumptions. Finally, we call on atomists and holists within the data science
community to exhibit greater empathy during ethical disagreements and propose
four targeted strategies to ensure data science research benefits society.

###### keywords:

ethics impact statements, AI ethics, dialogue, interdisciplinary research,
moral education, civil discourse, atomism, holism, empathy

## Introduction

In his 1917 lecture Science as a Vocation, Max Weber argues that questions of
fact are separate from questions of value [1]. Given pre-specified ends, the
task of science is to find the best means of achieving them. But asking which
ends we ought to achieve is a question of values, answerable only by
philosophy or religion. Weber was not alone on this point. Eminent
mathematicians, physicists, and economists espouse similar views on the
separation of science and values. But a growing number of data science
researchers see things differently. They argue that values, particularly those
related to the desirability of social and political goals, implicitly
influence the foundations of data science practices [2, 3, 4].

As public concern mounts over the use of AI-driven research to enable
surveillance technologies, deep fakes, biased language models [5],
misinformation, addictive behaviors, and the discriminatory use of facial
recognition and emotion detection algorithms [6], data scientists appear
divided along ideological lines about what to do. While some may view the
inclusion of ethics impact statements in several AI conferences and journals
as a sign that the value-neutral ideal of science is no longer tenable, a
vocal cadre of data scientists has pushed back against this conclusion,
asserting the importance of academic freedom and value-neutrality[7]. Yet the
conflict between these two ideological camps threatens to polarize the data
science community, lowering the prospects that new AI-based technologies will
contribute to our collective well-being.

This largely historical and conceptual paper is organized as follows. Section
1 lays out the conflicting ideological views— _atomism_ and _holism_
—underlying AI ethics debates. Section 2 contrasts the “two cultures” of
atomism and holism by presenting a guiding metaphor for each that captures key
aspects of each culture’s self-concept. Section 3 relates philosophical debate
on facts and values to community concerns about the ethical evaluation of AI-
based technologies. Section 4 considers how ethics impact statements embody a
shift towards a more holist data science culture and discusses the social
benefits of value-based discussions. Section 5, in response, identifies key
sticking points of ethics reviews from the perspective of an atomist data
scientist. Finally, Section 6 presents a broad vision for more empathetic
discussion of value-related issues and proposes four targeted strategies for
more civil discourse.

##  1 Atomism vs. holism: An overview of conflicting ideologies

An _ideology_ is an all-encompassing world-view advancing a political and
ethical vision of the good society [8]. To better understand the nature of
ethical disagreements in data science, we introduce a simple ideological
taxonomy we call _atomism_ and _holism_ , inspired by philosophers and
intellectual historians such as Popper [9], Bunge[10], Sowell[11], and
Skinner[12]. The purpose of the taxonomy is to make each ideology’s implicit
beliefs, assumptions, and historical foundations more explicit so they can be
reflected on, refined, and more openly discussed within the data science
community should ethical conflict arise [13] (see Table 1).

To delineate the scope of the arguments and examples, we use the label _data
scientist_ to cover both industry data scientists who design and deploy
socially-impactful AI/ML-based systems, and data scientists—often in academia,
but also in industry—whose job descriptions includes publishing research
articles in peer-reviewed conferences and journals. Our broad definition
includes _AI/ML researchers_ and _AI/ML engineers_. We use the terms _data
science_ and _AI/ML_ interchangeably.

Table 1: A taxonomy of two basic but often conflicting ideologies in the data science community along several key dimensions. | Atomist | Holist
---|---|---
Guiding metaphor | Tool-maker | Social steward
Facts and values | Separate | Inseparable
Associated “Isms” |  | (Neo)liberalism, libertarianism,
---
logical positivism, modernism
| Communitarianism, feminism,
---
post-positivism, post-modernism
Social orientation | Centripetal/individualist | Centrifugal/collectivist
Self-concept | Autonomous | Relational
Means of social coordination | Incentives and markets | Shared moral values and dialogic exchange
Key moral concepts |  | Rights, duties, contracts,
---
impartial justice
| Empathy, caring, connection,
---
responsivity to vulnerable others
Vision of the good life | Neutral and realistic | Distinct and idealistic
Scientific methodology | Data-driven, empiricist, neutral | Theory-laden, rationalist, perspectival
Extreme form leads to | Technocracy/nihilism/alienation | Totalitarianism/dogmatism/tribalism

###  1.1 Conflicting visions of self, society, and politics

Our diagnosis starts from the observation that the conflict between atomist
and holist data scientists echoes classic debates between traditional liberals
and communitarians and care ethicists [14, 15, 16]. The conflict centers on
the nature of and relationship between the self and society [17], and whether
a just society should advance any particular view of the good life [18].
Atomists embody more of an individualist culture, holists more of a
collectivist culture [19]. The atomist is more inwardly focused on his or her
individual needs and motivations, whereas the holist is more outwardly-focused
on the needs, interests, and goals of the social collective. One helpful way
of summarizing the atomist and holist conception of the self is with analogy
to centripetal and centrifugal forces, respectively [17]. Notably, both
atomists and holists claim the opposing ideology—when taken to the
extreme—leads to various social and individual pathologies.

Holist data scientists view their identities _relationally_ , as derived from
their being embedded in a larger society with a particular and unique history
and culture. They are data scientists, but also concerned citizens. These
social, political, and cultural relations entail various relationships of
responsibility and caring for others [16, 15]. Holists stress that the self is
embedded in and constituted through caring relations to particular others. As
such, holists believe that a good life and society should foster attentive,
caring relations and cultivating empathy for the experiences and suffering of
vulnerable others [14, 15]. Holist data scientists recognize that their
technical expertise allows them to understand AI technologies in a way that
politicians and ordinary citizens might not. Their privileged knowledge
creates an obligation to communicate these concerns publicly so that citizens
and policy-makers can act appropriately [20]. Holists thus see themselves as
social stewards or fiduciaries acting on behalf of society. Limiting the
freedom of other data scientists in order to protect and prevent harm to
vulnerable third parties, marginalized groups, and the environment is a
necessary evil in the quest towards the ideal society.

Atomists, as the name implies, tend to see the world _reductively_ [10] and
believe that what separates us is prior to what connects us [15]. In political
matters, atomists emphasize our separateness and independence from others
while focusing on abstract issues of impartial justice, duties and rights.
They stress the importance of formal equality under the law and the freedom to
enter into voluntary contracts of exchange in markets; they prefer economic
systems in which social coordination is achieved through the pursuit of
enlightened self-interest, rather than imposed by an external authority [21].
Finally, atomists value personal autonomy and take violations of their
personal integrity seriously [22]. Personal integrity refers to the general
class of life projects to which a person is committed [23]. Atomist data
scientists feel the imposition of moral responsibility to others unfairly
infringes on their freedom to pursue their life projects as they see fit, a
view which reflects their centripetal view of self and society.

###  1.2 Conflicting visions of the role of science in society

Mirroring economic arguments for specialization and trade, atomists support a
gap between fact-based inquiry and value-based inquiry. The job of the
technically-trained scientist is to conduct fact-based inquiry and report
experimental results. In contrast, the job of policy-makers is to advance the
state of society by acting on these results according to political and ethical
values [24]. How others interpret the facts, and what they do on the basis of
these interpretations, is out of the control of the scientist. The atomist
thus defends the empiricist position that a judgment of fact entails nothing
about value, and vice versa [25]. Factual statements, strictly speaking, do
not motivate action or decision [26] (i.e., _ought_ cannot be derived from
_is_). Atomists argue that intellectual specialization and division of
labor—i.e., a logical gap between value-neutral fact finding and value-based
decision-making [9]—is a feature, not a bug, of the arrangement. Hence,
atomists see the imposition of ethics impact statements as hampering
scientific fact-finding, which is essential to innovation and economic
progress.

Atomists are also concerned about how the injection of values can lead to—in
the best case—wishful thinking and—in the worst case—to dogmatism and
totalitarianism [9]. Atomists cite how Soviet science was notoriously
influenced by its interpretation of Marxist philosophy, or how Galileo was
forced to recant his support of Copernicanism under the inquisition of the
Catholic church. The dogmatic and totalitarian tendencies of holists were
recently summarized by one data science academic who condemned “the increasing
use of repressive actions aimed at limiting the free and unfettered conduct of
scientific research and debate" [7]. By claiming that science is and ought to
be value-free, or at least value-neutral [27], atomists advocate a form of
inductive empiricism, tracing back to Francis Bacon and Galileo [28].

Meanwhile, holists advocate a post-positivist attitude to science, where facts
and values mutually inform one another [29]. They doubt there is a uniquely
correct or absolute view of reality [30] and emphasize the role of
interpretation and perspective [31]. Holists thus reject the value-free ideal
of science and the atomist ban on deriving an _ought_ from an _is_. Instead,
holists believe facts discovered by social/data science ought to be put
towards realizing a distinct vision of the good life and society [32, 33].
Free and open discussion of values allows for dialogue aimed at finding
overlapping consensus about which values are and should be embodied in data
science research and applications. Without a clear articulation of and
commitment to shared moral values, new AI technologies may impede the
achievement of the good life [34]. In particular, holists fear the creation of
a faceless and unaccountable _technocracy_ narrowly aimed at prediction and
control of human behavior resulting from the “value-neutral,” instrumental
application of science [35]. In short, holists view ethics impact statements
as tools for clarifying shared action-guiding values, developing moral
character, and inducing democratic deliberation on the responsibility of data
scientists to society.

Figure 1: The data scientist community risks splitting into two opposing
factions— _atomists_ vs. _holists_ —with conflicting ideologies and self-
concepts. We propose several targeted recipes designed to help bridge these
divisions and promote more empathetic, fruitful, and civil debate on AI ethics
issues. (Images courtesy of daphnesembroidery.com)

##  2 The “two cultures” within the data science community

In 1959, at the height of Cold War, and as the US military-industrial complex
established itself, scientist and writer C.P. Snow worried about a growing
divide between two academic cultures—those from the “hard sciences” and the
”humanities“—whose specialization rendered them increasingly hostile and
unmotivated to communicate with one another[36]. We suggest a similar dynamic
between may be stoking division within the larger data science community, and
here sketch two guiding metaphors capturing key differences in the self-
concept of atomists and holists (see Figure 1).

###  2.1 Atomists: Data scientists as value-neutral toolmakers

Echoing Max Weber’s position on the separation of facts and values, atomists
see themselves as autonomous and rational optimizers of a more or less pre-
specified objective function provided by their disciplinary paradigm. The
paradigm supplies a constellation of accepted beliefs, values, and techniques
with which the community members can solve the puzzles identified in the
paradigm [37]. Shared values, such as that quantitative are preferred over
qualitative predictions, are implicitly embedded within the paradigm, but
largely left unquestioned so that community members can better attend to the
facts. The resulting precision contributes to scientific progress as consensus
around clearly defined criteria [38] permits puzzle-formulation and solution.
In AI and data science research, for instance, these shared standards might
take the form of the 0.05 threshold for p-values in statistical hypothesis
testing [24], or the use of several standard benchmark datasets (e.g.,
ImageNet, CIFAR-10, Fashion MNIST, etc.) and performance metrics (e.g.,
precision, recall, or F1 score).

The atomist’s belief in the value-neutrality of science suggests the data
scientist’s job consists in the search for more efficient means of reaching
the shared ends of the paradigm, not in proposing new ends or critiquing the
legitimacy of the paradigm (e.g., claiming the current paradigm may be
overfitting to certain benchmark datasets [39]). Larger questions of goals and
purposes are external to the paradigm—and hence the paradigm remains silent on
them, as it has no specialized tools to objectively settle such questions, let
alone precisely formulate them. Just as an expert craftsman or toolmaker does
not generally inquire into the final purposes of the tools he or she produces,
the atomist data scientist focuses on what he or she knows best, as determined
by the paradigm, and leaves larger questions of purpose, goals, and ends to
the philosophers, theologians, and politicians. The atomist data scientist
thus holds that the results and artifacts of AI research may be later be used
for good or for bad, but in themselves are value-neutral.

###  2.2 Holists: Data scientists as social stewards

In contrast, holists see themselves as social stewards or fiduciaries working
on behalf of society. The conviction that data scientists ought to act as
social stewards or fiduciaries is advocated by a growing number of data
scientists [40], particularly in high-stakes healthcare domains [41]. Holists
believe that data scientists’ fiduciary responsibility to society derives from
their capacity to “produce consequences that matter to others” [42, pg. 110].
In particular, holists are concerned about unjust power differentials and
coercive dependency relationships [15] that may arise due to applications of
AI-based technologies. The vocational role of steward or fiduciary thus aligns
with the holist belief that the self is constituted through social relations.

A fiduciary relation is a special kind of social relation involving
discretion, power, inequality, dependence, vulnerability, trust, and
confidence [43]. The word fiduciary itself stems from _fide_ , meaning
“trustworthy.” Fiduciaries have the power and authority to make decisions on
behalf of the beneficiary. Examples of fiduciary relations include trustee-
beneficiary, principal-agent, manager-shareholder, lawyer-client, doctor-
patient, and parent-child relations.

The presumed fiduciary power of holists may be justified on the basis of their
unequal access to knowledge of technical details and social applications of
emerging AI-based technologies. As a result of this knowledge asymmetry,
fiduciaries abide by a duty of loyalty to act in the best interests of the
beneficiary and refrain from opportunism and conflicts of interest [43]. An
open question, however, is whether holists can rightly claim to know what is
in the best interests of society. Another concern is that fiduciary
relationships are typically created by law or consented to by the beneficiary
[43], but atomists might argue that holists have unilaterally imposed upon
themselves the fiduciary duty to act on behalf of society.

##  3 The separation of facts and values

Atomists and holists disagree about how facts and values relate and their role
in science. To better clarify the fact-value debate, this section examines the
issues in more detail. At stake is whether values can be subjected to rational
scrutiny and thereby provide objective grounds for ethical criticism of AI
technologies.

###  3.1 The fact-value controversy: A brief historical interlude

The crux of the fact-value debate centers on whether it is possible to
rationally criticize (i.e., by appeal to logic or empirical evidence) both the
means used to achieve an end, and the end itself. Empiricists, following David
Hume tend to argue it is not possible to rationally critique ends;
rationalists, following Immanuel Kant, disagree.

Hume’s empiricism was revived in the 20th century in the philosophy of science
known as _logical positivism_ or _logical empiricism_ , which asserted that
philosophical problems ultimately stemmed from ambiguities in ordinary
language [44]. This highly ambitious project aimed to unify all sciences using
formal logic and derive experimentally-verifiable hypotheses from formalized
versions of scientific theories [45]. Asserting empirical verification as the
criterion for meaning was an important methodological move that paved the way
for _behaviorism_ in psychology, _revealed preference theory_ in economics
[46], and _operationalism_ in psychometrics [47].

The focus on empirical verification of scientific statements challenged the
idea of objectivity in ethics. What methods of proof or evidence could support
ethical judgments? To overcome this problem, some philosophers suggested that
ethical judgments were actually composed of two separate components: a
descriptive or factual part, and a command-like prescriptive component [48].
Others concluded that values were emotive or attitudinal in nature and lacking
in descriptive, factual content, analogous to cries of pain, groans, or
shrieks [44]. Thus to say “X is good” is nothing more than a fancy way of
saying “hurrah for X!”, or “I like X; you should too" [49]. Logical
positivists, in short, claimed moral language is simply not “about” anything
at all, casting serious doubt on efforts to resolve moral disagreements.

Despite its onetime popularity, philosophers of science abandoned the logical
positivist project as early as the 1960s [50]. Most philosophers today accept
that facts and values are not so cleanly separable [51]. Still, vestiges of
the logical positivist project remain influential in other disciplines [52],
particularly those fond of axiomatic formalization such as economics and
computer science.

###  3.2 Implications for moral expertise and objectivity

Evaluating AI-based technologies presumably requires moral expertise. But if
the notion of a moral fact is incoherent, how can we make sense of the idea of
moral expertise? Does moral expertise imply the existence of objective moral
facts, such as "killing is wrong" [25]? Even if we are willing to grant the
existence of such facts, must moral experts not only possess _theoretical_ ,
but also _practical_ moral knowledge—the real-world skills needed to correctly
apply moral concepts in the right situations [53]? Although developing a
procedure for testing the validity of moral judgments appears difficult, if
not impossible, moral philosophers nevertheless routinely undertake this task
[54, 55].

Another problem is that in most scientific domains, we expect experts to reach
consensus on key facts, as it indicates convergence on the truth [51]. Yet
moral disagreement is common, and value statements seemingly do not achieve a
high degree of consensus, even among experts [56]. Nearly all data scientists
will agree that _when training a machine learning model, both bias and
variance cannot be minimized simultaneously_ , but presumably fewer agree with
“value-laden” statements such as _a predictive model’s accuracy is more
important than its interpretability_. The lack of consensus around value
statements, especially in an era of globalized data science, could be a sign
that objective knowledge in the domain of AI ethics may not be possible.
Without such objectivity, the legitimacy of publication decisions that include
ethical evaluation may be disputed.

##  4 Social Benefits of Ethics Impact Statements

Although not without controversy, one major step towards a more holist culture
was the decision by the _Neural Information Processing Systems_ (NeurIPS)
conference in 2020 to introduce a new ethics review process. The process
requires submitting authors to include a “broader impact” section while a
group of ethics experts review papers flagged as problematic by technical
reviewers [57]. Following the example of NeurIPS, impact statements and ethics
reviews by experts are spreading to other conferences and journals [58]. The
_IEEE/CVF Computer Vision and Pattern Recognition Conference_ (CVPR) in 2022
adapted the NeurIPS ethics guidelines and “strongly encourages” authors to
“discuss the ethical and societal consequences of their work in their papers
in a concrete manner” [59]. But creating effective ethics impact statements
remains challenging. Impact statements are plagued by a variety issues
including their complexity, lack of guidance and best practices, lack of
purpose, lack of procedural transparency, high opportunity costs,
institutional pressure, and various social and cognitive biases affecting
authors and referees [60].

Nevertheless, data scientists can no longer presume their research will have a
net positive impact on the world [61]. The inclusion of impact statements
could be a positive step towards recognition of the social responsibility of
those who design, research, and deploy AI technologies. Below we identify
several ways in which impact statements may benefit data science research and
society in general.

#### Train “citizen” data scientists and cultivate moral and intellectual
virtues

Writing and critiquing an ethics statement could become a core part of the
moral education and training of future data scientists. Impact statements
encourage moral reflection and the cultivation of civic and intellectual
virtues, such as attentive observation, open-minded imagination, patient
reflection, careful analysis, and fair-minded interpretation and assessment
[62]. The development of moral and intellectual virtues could break down
interdisciplinary academic barriers isolating researchers from public debates
on AI-based technologies [63]. Hagendorff [64], for instance, suggests
cultivating four basic virtues: justice, honesty, responsibility, and care,
along with secondary virtues of prudence and fortitude (to speak truth to
power). To develop these virtues, data science curricula might offer _Embedded
EthiCS_[65] courses to foster discussion on the ethical implications of AI-
based technologies and provide training in writing ethics impact statements.

#### Promote disciplinary cross-pollination

The big-picture scope of ethics reviews and the diversity of the review groups
can contribute to disciplinary cross-pollination. Novel dilemmas,
formalizations, and technologies can be brought back and analyzed in different
academic communities. These issues can then become part of the ethical
training of data scientists. Data science research also confronts legal
scholars and regulators with thorny problems of interpretation and application
of existing laws, which could lead to better lawmaking. Lastly, cross-
pollination can occur when data science researchers experimentally test the
assumptions of traditional ethical theories [66], leading to theory refinement
in ethics and moral psychology. For example, deep neural nets can make
accurate predictions about the morality of various novel acts (e.g., “it’s
rude to mow the lawn late at night”) as judged by human evaluators [67, 68].

#### Spur new debates on the purpose and applications of corporate data
science research

Technologists increasingly voice their concerns when corporate applications of
technology conflict with their ethical values [69, 70]. Value-based discourse
allows debate on the social and ethical impact of corporate-funded data
science applications. One AI-driven application likely to lead to moral
disagreement in the data science community is digital advertising [71]. Jeff
Hammerbacher, one of Facebook’s first data scientists, once said, “The best
minds of my generation are thinking about how to make people click ads…That
sucks” [72]. Indeed, Google and Facebook are two of the world’s largest
technology companies employing data scientists and also two of the biggest
players in digital advertising. Massive industry investment reflects the
allocation of vast amounts of data scientist labor to solving problems related
to digital advertising. But beyond the obvious economic benefits, what values
justify this enormous outlay of human energy and attention?

#### Identify and inspire new forms of data science for social good projects

A more transparent debate about values can expand the scope of data
scientists’ responsibility to new moral communities and stimulate new ideas
for data science for social good. Ethics debates can identify new forms of
harm before they become systematically entrenched in business practices and
research pipelines. Interesting ethical blindspots of AI concern how computer
vision, natural language processing (NLP), and other technologies contribute
to environmental degradation [4], the exploitation of human data
labelers—particularly in low-income countries [73] and in NLP crowdsourcing
tasks [74, 75], and the mistreatment of animals in factory farms [76]. Another
angle involves developing algorithms to “boost” [77, 78] critical thinking
about the quality and veracity of news stories shared on social media in an
effort to foster democratic values.

#### Reveal the sociotechnical, political, and value-laden nature of applied
data science

Ethics impact statements can illuminate the sociotechnical nature of data
science. In real-world institutions such as courts or corrections facilities,
legal terms and predictive relationships evolve, actors act adversarially, and
numerous non-sampling errors can affect algorithmic performance [79]. Not only
that, many decisions in machine learning pipelines involve “essentially
contested” and value-laden constructs such as fairness and justice [80].
Sociotechnically-oriented data science recognizes that fairness is more than
just a property of an algorithm [81]: AI can be used as part of a political
agenda [3]. One noteworthy example comes from Barabas et al.[82], who refused
to build an algorithm due to moral concerns about its function in reinforcing
structural injustices in the prison system.

##  5 Concerns of Data Scientists Regarding Ethics Statements

This section considers possible concerns and arguments arising from atomist
data scientists.

#### Lack of technical credibility

Multiple cases, including our own personal experience, exist where research is
rejected or red flagged for apparent ethical concerns. Sometimes, however, the
rejection is unwarranted as the contribution of the paper may be orthogonal or
even a mechanism to better understand AI model risks. For example, an
explainable AI paper might be rejected because the model it explains (viz. a
large language model) has fairness issues. But the model itself is not the
contribution. Undesirable behavior uncovered through explanations is not a
limitation, but rather a validation of the explanation method. A rejection
decision showcases ethics reviewers’ inability to understand the work’s
technical contribution. In larger contexts, AI tools and systems might fare
similarly if not properly assessed.

#### Lack of intellectual freedom

Rightly or wrongly, many data scientists currently see ethics requirements as
constraints, not just algorithmically but also intellectually. Many see their
purpose as discouraging work that does not adhere to certain “ethical”
standards. Such rhetoric can engender a defensive attitude towards those
demanding these standards for fear of one’s ideas being shunned and
suppressed. Fostering a defensive research culture could have a chilling
effect on data science progress, and in some cases even lead to “black market”
research being done away from ethicists’ eyes. Ideally, when practitioners
encounter ethical dilemmas, they should feel comfortable reporting and
debating them with the larger research community, not suppressing them for
fear of ridicule or punishment.

#### Limiting progress for an application/problem

In 2004, a chess program called Fruit [83] was open sourced, which had a much
lower playing strength than top human grandmasters at the time. However, it
led to a revolution where ideas encoded in this initial version were imbibed
in other programs and improved upon leading to the current generation of
programs such as Stockfish. These programs are nearly unbeatable by humans and
are now commonly used by top players for training and in the discovery of new
ideas. This was a clear case where imperfect versions can still lead to
significant progress. Refinement can happen over time. Analogously in AI, good
ideas could be killed early if too many ethical restrictions are imposed
upfront.

#### Limiting progress across applications/problems

Likewise, some ideas or tricks used to develop one system or application are
often useful for different applications. For example, transformer
architectures [84] were initially shown to be successful in natural language
processing tasks, however, recently they are now preferred even in computer
vision tasks [85]. Hence, the organic transfer of ideas can have impacts far
beyond the original application. Yet this intellectual transfer may be
severely curtailed if strict yet subjective constraints are placed on what is
ethically acceptable.

#### Subjectivity of ethics standards

Values can be highly subjective at an individual level and even at the level
of regulatory institutions. For instance, the requirements of the General Data
Protection Regulation (GDPR) [86] in Europe vary from those outlined by NIST
[87] in the United States. Values are also hard to precisely specify and
formalize. This can lead to frustration for persons in academia and industry
alike. Publishing in respected venues is already challenging enough. A PhD
student trying to meet publication requirements could get frustrated with
additional and vague requirements that resemble a moving target. Similar
ambiguities could betide engineering teams in industry offering products and
services.

#### Most technology is neutral

A car could be used to transport an ailing person to the hospital and saving
their life, or run over someone. Analogously in AI, a fairness algorithm could
be used to output unfair results by misspecifying protected attributes. The
point is that technology can be used for good or for bad and may not be
inherently harmful. Hence, putting restrictions on it might make little sense.
Rather, the application of it should be monitored and constrained.

##  6 Targeted Recipes for Fruitful Interdisciplinary AI Ethics Discussions

Given the ideological conflicts between atomists and holists, we adapt a
therapeutic technique designed to restore trust between conflicting parties.
Guided by its basic theme of accuracy and empathy, we then present four basic
recipes—broadly targeted at different disciplines—for fostering more
productive dialogue on AI ethics issues to benefit society at large (see Table
2).

###  6.1 A therapeutic model for interdisciplinary accuracy and empathy

Carl Rogers in 1951 proposed a _person-centered approach_ to psychotherapy
useful for resolving interpersonal conflict [88]. Importantly, dialogue
participants are more satisfied and less resentful regardless of outcome. A
similar dialogical process may be useful in improving the quality of ethics
discussions in general. The core idea is the following:

Accuracy: Alice first expresses her worry. Bob then repeats it as accurately
as possible. This repetition should not be an interpretation of what Bob
thinks Alice meant, but as accurate a reproduction as possible. Afterwards,
Bob asks Alice if he missed something, and if so, tries again until Alice is
satisfied.

Empathy: Once Alice is satisfied with Bob’s reformulation, Bob empathizes with
her concern. He imagines Alice’s perspective and follows her line of
reasoning, although he may not agree with it. Then Bob makes his point, which
now Alice must accurately repeat. The process continues until both parties are
satisfied. A fictional conversation might proceed as follows:

  * •

Alice: I spent the last few weeks testing Schmoogle’s newest large language
model for biases. I am now convinced it might be sentient.

  * •

Bob: You said you “spent the last few weeks testing Schmoogle’s newest large
language model for biases and now you are convinced it is sentient.” Is that
right?

  * •

Alice: Yes, that is right. We might want to ask for consent before we continue
running A/B tests on it.

  * •

Bob: I understand why you would think such a complex language model might be
sentient. If I spent as much time as you testing it, I would also be
concerned. But the model is just finding statistical correlations in massive
amounts of text from all over the internet.

  * •

Alice: You said, “The model is just finding statistical correlations in
massive amounts of text data from all over the internet” Is that right?

  * •

Bob: $\cdots$

Table 2: Examples of data science ethics reviewer backgrounds and training and their position in the atomist-holist taxonomy. We distinguish the degree to which ideological assumptions are implicit or explicit, largely cutting across disciplinary divides.  | Atomist | Holist
---|---|---
Implicit |  | Engineering, Physics,
---
Computer Science, Statistics
Technology Activism
Explicit |  | Economics,
---
Quantitative Social Science
| Humanities, Law,
---
Qualitative Social Science

###  6.2 Moral education for data scientists

Moral education is about the socialization and development of virtuous
persons: persons with ethically admirable character traits and dispositions
who empathize with and care for particular others in their community [16].
Today, however, most data scientists receive a highly technical education.
Skills, not virtues—excellences that contribute to a flourishing human life
and community—are the focus. Understandably, data science graduates may
implicitly apply their disciplinary standards of objectivity to ethical
issues, realize it cannot easily be done, and conclude that anything goes, or
that ethical judgments are meaningless. Yet neglecting the moral education of
data scientists can leave open a moral vacuum liable to be filled by
tribalism, dogmatic identity politics, relativism, or nihilism. Data science
educators would do well to emphasize the history and arguments behind the
rejection of the fact-value distinction [52], as these points seem to have
gone unnoticed in AI-feeder disciplines.

Greater debate about the ideal character and virtues of data scientists can
also pave the way for professionalization (see [80]). Example debate-starters
might be: under what sort of conditions is corporate whistleblowing
appropriate? What is the difference between ethics consulting and fairwashing?
To what extent should data scientists be expected to reasonably foresee the
harmful use of AI-based technologies by others? While we cannot speak for the
community, at the very least the moral education of future data scientists
should aim at cultivating an ability to empathize, tolerate, and negotiate in
good faith with those whose views differ from their own, without resorting to
name-calling and threats of cancellation.

###  6.3 Incentives for economists

To encourage atomists to cooperate with holists to tackle the complex ethical
issues posed by AI-based technologies, one suggestion draws on the idea that
commerce mutually reinforces morality [89]. By providing economic incentives
towards more civil engagements on AI ethics issues, we can also respect
atomists’ desire for autonomy and personal integrity.

At the institutional level, academic data science departments might
incentivize faculty to explore co-teaching data science ethics courses with
philosophers and lawyers. Senior researchers and practitioners could also be
incentivized to mentor junior data scientists, focusing on character
development. Industry data scientists could be rewarded for convening “ethics
roundtable” discussions or ethics reading groups. Conferences and journals
with ethics reviews and impact statements could also offer prizes or community
recognition to authors whose statements are particularly cogent or creative
[60]. Similarly, positive examples of civil debate—whether on social media or
offline—could be rewarded through community praise. The actions of certain
“moral exemplars” can become teaching material for data science ethics
courses. In general, organizations can do more to frame ethics issues as new
and exciting research challenges, rather than as autonomy-restricting
handcuffs.

###  6.4 Philosophical and legal training for technological activists

While typically trained in the natural sciences, technological activists seek
to draw attention to social justice issues related to AI and data science,
such as racial and gender discrimination in STEM fields and industry hiring
practices. Technology activists author popular books [90, 91] and have
received major media attention in news articles [92, 70] and films (i.e., _The
Social Dilemma_). Although we sympathize with activists’ intentions, we also
worry that threatening the cancellation[93] of those who hold opposing values
can stoke fears of tribalism and dogmatic group-think, sparking further
disciplinary polarization. Indeed, given activists’ concern with the unjust
use of power, threats of mob-based cancellation may appear hypocritical.

We encourage philosophical training to hone activists’ persuasive and
reflective abilities and create new allies and collaborators in the
humanities, social sciences, and law. With broad support from the often-
fragmented “two cultures” of the natural and human sciences, technological
activists can arguably better achieve their ethical goals. Philosophy provides
an array of concepts to express ethical concerns (e.g., rights, duties,
utility, virtue, power, care, etc.) and can aid in identifying _ad hominem_
attacks and logical fallacies. Familiarity with various areas of law (e.g.,
corporate law, public law, torts, etc.) can also help technology activists
frame AI harms in terms of pre-existing legal frameworks and concepts, such as
discrimination or human rights law [94].

###  6.5 Technical training for humanities scholars

For those in the humanities (e.g., philosophy, law, media and communication
studies, etc.) and “soft” social sciences (e.g., anthropology, sociology,
education, etc.,) who might participate in ethics reviews, we believe it is
fair to ask whether they have “appropriate” technical expertise so as to
accurately evaluate data science products, systems, and/or papers. For
instance, there is a growing literature that draws on postmodern philosophical
sources to explain how AI can entrench existing racial and gender inequities
[95] and reproduce the dynamics of colonial exploitation and power
differentials [4, 96]. While we welcome critical voices into the debate on the
ethics of AI, we must clearly distinguish _features_ of the technology in
itself from _uses_ of the technology by organizations embedded in larger
social, political, and economic systems.

One suggestion is to establish an accreditation process for ethics reviewers,
such as a minimum-duration internship in industry or an AI-related academic
department (e.g., computer science or statistics). The knowledge and skills
obtained could improve both the accuracy and empathy of cross-disciplinary
dialogue. Real-world experience would help ethics reviewers to develop their
_practical_ moral knowledge and increase their ability to empathize with data
scientists. Accreditation could be done in collaboration with a number of
interdisciplinary research groups and industry research labs.

##  7 Conclusion

The data science community appears split—sometimes violently so—along an
ideological divide. We explicated key beliefs and assumptions of atomist and
holist ideologies and described their relevance to the fact-value debate.
Atomists see themselves as toolmakers and hold that facts are and should be
kept separate from values, while holists see themselves as social stewards and
believe facts and values mutually reinforce one another. In the interests of
the data science community and society, we advocate for a balance between the
two camps’ views and offer several recipes for more empathetic and productive
interdisciplinary dialogue on AI ethics. We hope this work can serve as a
microcosm of such dialogue, as we ourselves occupy various points along the
atomist-holist spectrum.

But our diagnosis and proposal has limitations. First, some recipes may not
translate well to digital and social media contexts, where the most-heated AI
ethics arguments tend to occur. Second, the atomist-holist taxonomy obscures
heterogeneity among data scientists who may hold less traditional views.
Lastly, because many advances in AI happen inside technology corporations,
corporations arguably should assume the burdens of legal and moral
responsibility for the harms and risks imposed by their innovations. More
systemic solutions—at the level of corporate law and governance [97]—are
likely needed to ensure that AI-based technology is developed and deployed
with the broader interests of society in mind, not simply shareholders.

We hope our diagnosis and vision draws attention to the importance of greater
empathy, openness, and humility for all members of the data science community,
a community whose boundaries continue to evolve and expand. We look forward to
a time when community ethics discussions are dominated by the best reasons,
rather than the loudest voices.

## Acknowledgements

We thank Finale Doshi-Velez for helpful comments on an earlier draft, and also
David Martens and the Applied Data Mining Research Group at the University of
Antwerp for the many stimulating conversations and valuable feedback.

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