# “It is currently hodgepodge”: Examining AI/ML Practitioners’ Challenges
during Co-production of Responsible AI Values

Rama Adithya Varanasi  Information ScienceCornell UniversityNew YorkNYUSA
0000-0003-4485-6663<EMAIL_ADDRESS>and  Nitesh Goyal  Google
ResearchGoogleNew YorkNYUSA<EMAIL_ADDRESS>
(2023)

###### Abstract.

Recently, the AI/ML research community has indicated an urgent need to
establish Responsible AI (RAI) values and practices as part of the AI/ML
lifecycle. Several organizations and communities are responding to this call
by sharing RAI guidelines. However, there are gaps in awareness, deliberation,
and execution of such practices for multi-disciplinary ML practitioners. This
work contributes to the discussion by unpacking co-production challenges faced
by practitioners as they align their RAI values. We interviewed 23
individuals, across 10 organizations, tasked to ship AI/ML based products
while upholding RAI norms and found that both top-down and bottom-up
institutional structures create burden for different roles preventing them
from upholding RAI values, a challenge that is further exacerbated when
executing conflicted values. We share multiple value levers used as strategies
by the practitioners to resolve their challenges. We end our paper with
recommendations for inclusive and equitable RAI value-practices, creating
supportive organizational structures and opportunities to further aid
practitioners.

Responsible AI, RAI, ethical AI, value levers, co-production, collaboration,
XAI, FAT, fairness, transparency, accountability, explainability

††journalyear: 2023††copyright: rightsretained††conference: Proceedings of the
2023 CHI Conference on Human Factors in Computing Systems; April 23–28, 2023;
Hamburg, Germany††booktitle: Proceedings of the 2023 CHI Conference on Human
Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg,
Germany††doi: 10.1145/3544548.3580903††isbn: 978-1-4503-9421-5/23/04††ccs:
Human-centered computing Empirical studies in HCI

##  1\. Introduction

In November 2021, the UN Educational, Scientific, and Cultural Organization
(UNESCO) signed a historic agreement outlining shared values needed to ensure
the development of Responsible Artificial Intelligence (RAI) (UN, 2021). RAI
is an umbrella term that comprises different human values, principles, and
actions to develop AI ethically and responsibly (Ghallab, 2019; Dignum, 2021;
Askell et al., 2019; Rakova et al., 2021). Through UNESCO’s agreement, for the
first time, 193 countries have standardized recommendations on the ethics of
AI. While unprecedented, this agreement is just one of several efforts
providing recommendations on different RAI values to be implemented within
AI/ML systems (Union, 2019; González et al., 2020; Unit, 2021).

In response, several industry organizations have begun to implement the
recommendations, creating cross-functional RAI institutional structures and
activities that enable practitioners to engage with the RAI values. For
instance, several big-tech companies are implementing common RAI values, such
as Fairness, Transparency, Accountability, and Privacy, as part of their RAI
initiatives (Birhane et al., 2022a; Microsoft, 2022; IBM, 2022; Facebook,
2021; Google, 2022). However, such RAI values have minimal overlap with the
values prescribed by UNESCO’s framework that promotes non-maleficence,
diversity, inclusion, and harmony (UNESCO, 2020). Scholars have attributed the
lack of overlap to different business and institutional contexts involved in
developing AI/ML 111hereafter, we use AI/ML systems to refer to both AI
products and ML models systems (Häußermann and Lütge, 2022; Jakesch et al.,
2022). Subsequently, it is essential to understand these contexts by engaging
with practitioners across multiple roles who come together to co-produce and
enact such RAI values. Co-production is an iterative process through which
organizations produce collective knowledge (Jasanoff, 2004a). During co-
production, individual practitioners may hold certain values (e.g., social
justice), yet their teams might prioritize other values. Rakova et al. (2021)
hints at potential challenges that can arise due to such mismatches in RAI
values. Our study builds on this critical gap by giving a detailed analysis of
those challenges and strategies (if any) devised to overcome such strains as
practitioners co-produce AI/ML systems.

We interviewed 23 practitioners across a variety of roles to understand their
RAI value practices and challenges. Our findings show that institutional
structures around RAI value co-production contributed to key challenges for
the practitioners. We also discovered multiple tensions that arose between
roles and organizations during prioritization, deliberation, and
implementation. Interestingly, we also observed development of ten different
RAI value levers (Shilton and Koepfler, 2013; Shilton, 2018). These are
creative activities meant to engage individuals in value conversations that
help reduce value tensions. In the remainder of the paper, we first discuss
related work about collective values in Responsible AI from an HCI perspective
and outline our research questions. We then present the research methodology
and results of the study. We conclude with a discussion of our contributions
in improving RAI co-production practices of AI practitioners. Overall, this
work makes several contributions. First, we describe the experiences and the
organizational environment within which AI/ML practitioners co-produce RAI
values. Second, we illustrate multiple challenges faced by AI practitioners
owing to different organizational structures, resulting in several tensions in
co-production. Third, we unpack ten RAI value levers as strategies to overcome
challenges and map them on to the RAI values. Lastly, we provide essential
strategies at the different levels (individual, and organizational) to better
facilitate and sustain RAI value co-production.

##  2\. Related Work

###  2.1. The Field of Responsible AI

In the last decade, Responsible AI (RAI) has grown into an overarching field
that aims to make AI/ML more accountable to its outcomes (Askell et al., 2019;
Dignum, 2019; Abrassart et al., 2017). One of the field’s roots lies in
Ethical AI, where critical engagement with ethical values in the otherwise
traditional AI/ML field have been encouraged (Khan, 1995; Gips, 1994; Ashurst
et al., 2022; van Wynsberghe and Robbins, 2019). Example studies include
engagement with core ethical values to provide nuance in technical AI/ML
discourse (Harris and Anthis, 2021), translating ethical values into
implementation scenarios (Gips, 1994; Hooker and Kim, 2018), and AI/ML
guidelines (Vakkuri and Abrahamsson, 2018; Harris and Anthis, 2021; IEEE,
8611; Hagendorff, 2020; Jobin et al., 2019), and seminal studies that brought
critical ethical problems to the forefront (Eckersley, 2019; Yu et al., 2018;
Mittelstadt, 2019; Awad et al., 2018).

RAI has also drawn its inspiration from AI for Social Good (AI4SG (Shi et al.,
2020)) research to study human values more broadly, going beyond “ethical”
values. AI4SG helped the RAI field to translate such values embedded in AI/ML
systems into positive community outcomes (Berendt, 2019) by eliciting specific
values (e.g., solidarity (Floridi et al., 2018)), developing methods (e.g.,
capabilities approach (Bondi et al., 2021)), and producing representations
(e.g., explanations (Okolo et al., 2021)) that strongly align with community
goals (e.g., the UN Sustainable Development Goals (Ghallab, 2019)). For
example, studies have explicitly engaged with underserved communities to
examine the impact of the embedded values within AI/ML systems in their lives
(e.g., in agriculture (Madaan et al., 2019), health (Ismail and Kumar, 2021;
Wang et al., 2021), and education (Cannanure et al., 2020) domains). More
recent studies have shed light on how certain practitioners’ (e.g., data and
crowd workers) practices, contributions, and values are often ignored while
developing the AI/ML systems (Sambasivan and Veeraraghavan, 2022; Thakkar et
al., 2022; Varanasi et al., 2022). Some have looked at different values (e.g.,
fairness) that are often ignored by the discriminatory algorithms (Hampton,
2021; Kalluri, 2020; Chouldechova et al., 2018). More recent work at the
intersection of these two by Goyal et al. (2022) has also highlighted the
impact of data workers of marginalized communities on the AI/ML algorithms to
highlight complexity when building for RAI values like equity.

Lastly, RAI has also drawn motivation from recent movements associated with
specific value(s). One such movement is around the value of explainability (or
explainable AI) that arose from the need to make AI/ML systems more
accountable and trustworthy (Doran et al., 2017; Barredo Arrieta et al., 2020;
Ehsan et al., 2021). A similar movement within the RAI’s purview focused on
FATE values (Fairness, Accountability, Transparency, and
Ethics/Explainability) (Shin and Park, 2019; de Fine Licht and de Fine Licht,
2020; Kroll et al., [n. d.]; Mitchell et al., 2021). While both movements have
challenged the notion of universal applicability of RAI values, our study
illustrates how these challenges do indeed appear in practice and the
strategies used by practitioners on the ground to resolve these challenges.

Taken together, RAI has emerged as an umbrella term that encapsulates the
above movements, encouraging critical value discourses to produce a positive
impact. At the same time, departing from previous movements that focused on
specific issues within AI/ML practices, RAI takes a broad institutional
approach that promotes disparate AI/ML practitioners to come together, share,
and enact on key values (Rakova et al., 2021). Our study expands RAI
discipline by surfacing on-ground challenges of diverse AI/ML practitioners
attempting to engage in shared RAI responsibilities, such as collaborative
value discourse and implementation. In the next section, we further unpack the
notion of values, examine their roots in HCI and their current role in RAI.

###  2.2. Collective Values in Responsible AI: HCI perspectives

Science & Technology Studies field has long examined how values are embedded
in technology systems in various social and political contexts (Winner, 1986;
Star, 1999; Latour et al., 1992). In recent years, studies within HCI have
built on this foundation to bring a critical lens into the development of
technology. Initial studies conducted by Nissenbaum and colleagues argued
against the previously held belief that technology is “value-neutral”,
showcasing how practitioners embed specific values through their deliberate
design decisions (Flanagan et al., 2008; Friedman, 1997). Value-sensitive
Design (VSD) by Friedman et al. (2013) was another step in this direction. It
has been used as a reflective lens to explore technological affordances
(through conceptual and empirical inquiry) as well as an action lens to create
technological solutions (technological inquiry) (Friedman et al., 2021; Hendry
et al., 2021). While VSD’s core philosophy remained the same, it has been
extended, often in response to its criticisms (Le Dantec et al., 2009;
JafariNaimi et al., 2015).

A criticism relevant to this study is practitioners’ ease in applying VSD in
the industry contexts (Shilton, 2013). VSD is perceived to have a relatively
long turnaround time, often requiring specialists for implementation. To
overcome these challenges, Shilton proposed ‘value levers’, a low-cost entry
point for value-oriented conversations while building technology artifacts in
the organization (Shilton, 2018, 2013). Value levers are open-ended activities
that engage participants in value-oriented discourses to develop common
ground. With creative representations, value levers can transform slow and
cumbersome value conversations into creative and fruitful engagements
(Shilton, 2013). While previous studies have applied and shaped the notion of
value levers in a very specific set of contexts, such as showcasing how
designers employ them in their practices (Shilton, 2013), this work shows a
broader utility of value levers among a diverse set of practitioners while
navigating the complex space of RAI value discourse.

Within AI/ML research, initial exploration of values were still primarily
computational in nature, such as performance (Chaudhuri et al., 2009),
generalizability (Jain et al., 2008), and efficiency (Espeholt et al., 2018).
With the advent of HCI and critical studies focusing on discriminatory
algorithms (Birhane et al., 2022b) and responsible AI, the discussions shifted
to much broader values, such as societal and ethical values, within the AI/ML
field (Birhane et al., 2022a). These studies focused on exposing inherent
biases in the models due to absence of substantive social and ethical values.
For instance, Burrell (2016) demonstrated how complex ML models have inherent
interpretability issues stemming from a lack of transparency about how
predictions were achieved. Another set of studies by Eubanks (2017) and Noble
(2018) scrutinized several algorithms governing the digital infrastructures
employed in our daily lives to expose discriminatory behaviors within
different situations, especially in the context of fairness, against
marginalized populations. In a similar vein, several studies have explored
individual values that they felt were critical for models, such as fairness
(Mitchell et al., 2021; Chouldechova et al., 2018; Friedler et al., 2016),
explainability (Doran et al., 2017), non-malfeasance (Jobin et al., 2019;
Mohamed et al., 2020), and justice (Birhane, 2021; Hampton, 2021; Kalluri,
2020), reflecting societal norms. A common underlying factor among several of
these studies was that they focused on individual values enacted in their own
spaces. Recently, however, a few studies have adopted contrasting perspectives
which argue that values do not exist in isolation, but often occupy
overlapping and contested spaces (Yurrita et al., 2022; Friedman et al.,
2013). Our study aims to provide much-needed deeper insights within this
complex space by showing how practitioners engage with and prioritize multiple
values in a contested space.

Another value dimension explored is – “whose values should be considered while
producing AI/ML algorithms (Borning and Muller, 2012)?” Most studies have
engaged with end-users values, lending a critical lens to the deployed models
and their implications on society (Robertson and Salehi, 2020; Kizilcec and
Lee, 2020; Wong, 2021; Smith et al., 2020). These studies challenged whether
developing fair algorithms should be primarily a technical task without
considering end-users’ values (Häußermann and Lütge, 2022; Robertson et al.,
2021). Subsequently, researchers leveraged action research (e.g.,
participatory approaches (Delgado et al., 2021)) to design toolkits,
frameworks, and guidelines that accommodated end-user values in producing ML
models (Katell et al., 2020; Krafft et al., 2021; Shen et al., 2022).

A more relevant set of studies have recognized the importance of understanding
the values that different practitioner roles embedded while producing
responsible algorithms (Yildirim et al., 2022; Passi and Barocas, 2019;
Scheuerman et al., 2020). Such practitioner-focused studies are critical in
understanding “how” and “why” particular values are embedded in AI/ML models
early on in the life cycle. However, these studies have explored particular
practitioners’ values in silos, leaving much to be learned about their
collective value deliberations. A nascent group of studies has answered this
call. For example Madaio et al. (2020) focused on controlled settings, where
specific practitioners’ values could co-design a fairness checklist as one of
their RAI values. Jakesch et al. (2022) explored a broader set of values of
practitioners and compared it with end-users in an experimental setting.
Another relevant study by Rakova et al. (2021) has explored RAI decision-
making in an organizational setting, laying a roadmap to get from the current
conditions to aspirational RAI practices.

Our study contributes to this developing literature in four ways. First,
within the context of Responsible AI practices, our study goes beyond the
scenario-based, controlled settings, or experimental setups by focusing on
natural work settings (Jakesch et al., 2022; Madaio et al., 2020), which
echoes the sentiment of some of the previous open-ended qualitative studies
that were conducted in the organizations (Passi and Barocas, 2019; Yildirim et
al., 2022), but not in the context of Responsible AI practices. Second, we
focus on a diversity of stakeholder roles, who are making an explicit effort
to recognize and incorporate RAI values, unlike siloed studies previously.
Third, we leverage the lens of co-production (Jasanoff, 2004a) to study RAI
values in natural work settings. Fourth, our study extends (Rakova et al.,
2021) by explicitly unpacking the co-production challenges deeply rooted in
RAI values. To this end, we answer two research questions:
(RQ-1): What challenges do AI/ML practitioners face when co-producing and
implementing RAI values ?
(RQ-2): In response, what strategies do practitioners use to overcome
challenges as they implement RAI values ?

###  2.3. Co-production as a Lens

To answer our research questions, we employed the conceptual framework of co-
production proposed by Jasanoff (2004a). She defined co-production as a
symbiotic process in which collective knowledge and innovations produced by
knowledge societies are inseparable from the social order that governs
society. Jasanoff characterized knowledge societies broadly to include both
state actors (e.g., governments) and non-state actors (e.g., corporations,
non-profits) that have an enormous impact on the communities they serve.
Studying co-production can help scholars visualize the relationship between
knowledge and practice. Such a relationship offers new ways to not only
understand how establishments organize or express themselves but also what
they value and how they assume responsibility for their innovations.

To operationalize co-production in our study, we invoke three investigation
sites, as Jasanoff proposed. The first site of exploration is the institutions
containing different structures that empower or hinder individuals to co-
produce. The second site examines different types of discourse that occur as
part of the co-production activities. Solving technological problems often
involve discourses producing new knowledge and linking such knowledge to
practice. The last site of co-production is representations produced both
during co-production to facilitate discourses and after co-production in the
form of the end-product. The three sites of the co-production framework are
appropriate for understanding the current industry challenges around RAI
innovation for several reasons. Technological corporations developing AI/ML
innovations have a robust bi-directional relationship with their end-user
communities. Moreover, for successful RAI value implementation, practitioners
need to leverage the complex structures within their organizations that are
invisible to external communities. RAI value implementations occur through
strategic discourses and deliberations that translate knowledge to effective
execution. Lastly, in the process of RAI value deliberations, individuals co-
create representations that further the implementation efforts of RAI.

##  3\. Methods

To answer our research questions, we conducted a qualitative study consisting
of 23 interviews with active AI/ML practitioners from 10 different
organizations that engaged in RAI practices. After receiving internal ethics
approval from our organization, we conducted a three month study (April-June,
2022). In this section, we briefly talk about the recruitment methods and
participant details.

###  3.1. Participants Recruitment and Demographics

To recruit AI/ML practitioners who actively think and apply RAI values in
their day-to-day work, we partnered with a recruitment agency that had strong
ties with different types of corporate organizations working in the AI/ML
space. We provided diverse recruitment criteria to the agency based on several
factors, including gender, role in the company, organization size, sector,
type of AI/ML project, and their involvement in different kinds of RAI
activities. Using quota sampling technique, the agency advertised and
explained the purpose of our study in diverse avenues, such as social media,
newsletters, mailing lists, and internal forums of different companies. For
the participants that responded with interest, the agency arranged a phone
call to capture their AI/ML experience, as well as their experience with
different RAI values. Based on the information, we shortlisted and conducted
interviews with 23 AI/ML practitioners who fit the diverse criteria mentioned
above. The aforementioned factors were used to prioritize diverse participants
with experience working on RAI projects within their team in different
capacities. For example, while shortlisting, we excluded students working on
responsible AI projects as part of their internships and included individuals
who were running startup RAI consultancy firms.

Participants |  23 |  Gender |  Men: 13 , Women: 10
---|---|---|---
Age (years) |  Min: 30-35; Max: 55-60; Avg: 35-40 |  Experience (years) |  Min: 1; Max: 15.6; Avg: 3.94
Company type |  Product: 16, Service: 7 |  Company Scale |  Small: 4, Medium: 4, Large: 15
Region |  Global North: 18, Global South: 5 |  Roles |  Engineer: 5, UX Designer/Researcher: 3, Product/Program Manager: 5, Senior Management (e.g., director): 3, Content & Support: 2, Policy & governance: 5
ML Type |  Supervised: 7, Unsupervised: 12, Reinforcement Learning:9 Deep learning: 13 |  ML application |  Health: 6 , Energy: 1, Social media: 3, Personal apps: 7, Finance: 2, Business: 5, Work: 2
Table 1. Practitioners’ Demographic Details.

Out of the 23 practitioners, 10 identified themselves as women. Participants
comprised of product-facing roles, such as UX designers, UX researchers,
program/product mangers, content & support executives, model-focused roles,
such as engineers, data scientists, and governance focused-roles, such as
policy advisors and auditors. Out of 23 practitioners, all but one participant
worked for a U.S. based organization. However, participants were
geographically based in both Global North and Global South. Participants also
worked in a wide variety of domains, including health, energy, social media,
personal apps, finance and business among other, lending diversity to the
captured experiences. Three participants worked for independent organizations
that focused exclusively on RAI initiatives and AI governance. twelve
participants had a technical background (e.g., HCI, computer-programming),
four had business background, two had law background and one each specialized
in journalism and ethics. For more details, please refer to Table 1.

###  3.2. Procedure

We conducted semi-structured interviews remotely via video calls. Before the
start of the each session, we obtained informed consent from the participants.
We also familiarized participants with the objective of the study and
explicitly mentioned the voluntary nature of the research. The interviews
lasted between 40 minutes and 2 hours (avg.= 65 mins.) and were conducted in
English. Interviews were recorded, if participants provided consent. Our
interview questions covered different co-production practices. First, in order
to understand different co-production challenges (RQ-1), we asked questions
about (1) how practitioners faced challenges when sharing RAI values across
roles (e.g., “Can you describe a situation when you encountered problems in
sharing your values?” ) and (2) how practitioners faced challenges when
collaborating with different stakeholders (e.g., “What challenges did you face
in your collaboration to arrive at shared common responsible values?”).
Second, to understand different co-production strategies (RQ-2) we asked (3)
how practitioners handled conflicts (e.g., “Can you give an example where you
resisted opposing peers’ values?”) and (4) how practitioners sought assistance
to achieve the alignment in RAI values (e.g., “What was the most common
strategy you took to resolve the conflict?”). To invoke conversations around
RAI values, we used a list of RAI values prepared by Jakesch et al. (2022) as
an anchor to our conversations. After first few rounds of interviews, we
revised the interview script to ask newer questions that provided deeper
understanding to our research questions. We stopped our interviews once we
reached a theoretical saturation within our data. We compensated participants
with a 75$ gift-voucher for participation.

###  3.3. Data collection and Analysis

Out of 23 participants, only three denied permission to record audio. We
relied on extensive notes for these users. Overall 25.5 hours of audio-
recorded interviews (transcribed verbatim) and several pages of interview
notes were captured. We validated accuracy of notes with the respective
participants. Subsequently, we engaged in thematic analysis using the NVivo
tool. We started the analysis by undertaking multiple passes of our
transcribed data to understand the breadth of the interviewee’s accounts.
During this stage, we also started creating memos. Subsequently, we conducted
open-coding on the transcribed data while avoiding any preconceived notions,
presupposed codes, or theoretical assumptions, resulting in 72 codes. We
finalized our codes through several iterations of merging the overlapping
codes and discarding the duplicate ones. To establish validity and to reduce
bias in our coding process, all the authors were involved in prolonged
engagement over multiple weeks. Important disagreements were resolved through
peer-debriefing (Creswell and Miller, 2000). The resultant codebook consisted
of 54 codes. Example codes included, ‘social factors’, ‘prior experience’,
‘enablers’, ‘RAI pushback’. As a final step, we used an abductive approach
(Timmermans and Tavory, 2012) to further map, categorize, and structure the
codes under appropriate themes. To achieve this, we used three key instruments
of co-production framework developed by (Jasanoff, 2004a), namely, making
institutions, making discourses, and making representations. Examples of the
resultant themes based on the co-production instruments included ‘value
ambiguity’, ‘exploration rigidity’, ‘value conflicts’, and ‘value lever
strategies’. Based on the instruments of co-production framework, we have
present our resultant findings in the next section.

##  4\. Findings

Our overall findings are divided based on different sites of exploration
proposed by Jasanoff (2004a). The first section answers RQ-1 by exploring
several institutional challenges that hinder the co-production of RAI values
among practitioners (Section 4.1). The second section explores subsequent
knock-on challenges that unstable institutional structures create in co-
production discourses (Section 4.2). The last section answers the RQ-2 by
presenting carefully thought out representations that overcome challenges
deliberation and execution of RAI values using the concept of value levers
(Shilton, 2013). (Section 4.3).

###  4.1. RAI Value Challenges within the Institutional Structures

Institutional structures are essential in enabling co-production of new
knowledge (Jasanoff, 2004a). It is these structures that facilitate
relationships for deliberation, standardize democratic methods, and validate
safety of new technological systems before information is disseminated into
the society. We found two key institutional structures that facilitated
deliberation around RAI values within AI/ML companies. These structures
brought about different RAI challenges.

Bottom-up: Burdened Vigilantes. The First type of structures were bottom-up.
Within these structures, RAI conversations developed through RAI value sharing
in the lower echelons of organizations, often within AI/ML practitioners’ own
teams. In our interviews, eight practitioners, namely a UX researcher,
designer, content designer, and program manager from two mid-size
organizations and two large-size organizations experienced or initiated
bottom-up practices that engaged with RAI values. One of the enablers for such
bottom-up innovation was individuals’ sense of responsibility towards
producing AI/ML models that did not contribute to any harm in society. A few
other practitioners paid close attention to ‘social climate’ (e.g., LGBTQ
month, hate speech incidents) to elicit particular RAI values. For instance,
P08, a program manager in a large-scale technology company took responsibility
for RAI practices in their team but soon started supporting team members to
come together and share RAI values:

> “We cater to projects that are very self-determined, very bottom-up aligned
> with our values and priorities within the organization …These are what I
> call responsible innovation vigilantes around the company. I also started
> that way but have grown into something more than that. You’ll see this at
> the product or research team level, where somebody will speak up and say,
> ‘Hey, I want to be responsible for these RAI values, make this my job and
> find solutions’. So you start to see individuals in different pockets of the
> company popping up to do RAI stuff.”

Figure 1. A summary of co-production activities mapped to Jasanoff (2004a)’s
co-production sites, along with the themes, RAI values invoked, and key
findings and takeaways.

A key challenge with such bottom-up structures was that the responsibility of
engaging with RAI value conversations implicitly fell on a few individual
“vigilantes”. They had to become stalwarts of particular RAI values and take
out substantial time out of their work to encourage and convince their teams
to engage with RAI values. They also actively seeked out RAI programs
available within and outside their organization. When such RAI programs were
not available, individuals took it upon themselves to create engagement
opportunities with other members within the organization. These bottom-up
structures were useful in breaking the norms of “boundary-work” that are often
set within AI and similar technical organizational work where only engineers
and high-officials in the company maintain control (Gieryn, 1983). It allowed
non-technical roles such as user experience researchers, product managers,
analysts, and content designers to a create safe space and lead the RAI
efforts. While such efforts early on in AI/ML lifecycle minimized the
potential harm of their ML models or AI products, it often happened at the
cost of their overworked jobs.

Bottom-up: Burdened with Educational Efforts. Apart from self-motivated
vigilantes, the burden of RAI value exploration also fell on a few
practitioners who were implicitly curious about RAI innovation.  Unlike the
vigilantes, these participants were pushed to become the face of their team’s
RAI initiatives since there was no-one else who would. P14, a product manager
working for two years at a medium size company within the energy sector
shared,

> “When I came in to this team, nobody really believed in it [RAI] or they
> really didn’t think it [RAI] was important. I was personally interested so I
> was reading about some of these principles …When there was an indication of
> a future compliance requirement, people didn’t want to take up this
> additional work …somebody had to do it.”

Similarly P05, a technical manager leading their team on data collection for
the development of knowledge graphs, revealed how they were considered “the
face of privacy” for the team. Therefore, P05 was expected to foster awareness
and common understanding among internal stakeholders and external partners and
ensure they strove towards similar RAI standards and appreciated data-hygiene
practices (e.g., data cleaning and de-identification). Practitioners like P14
and P05 had to assume the responsibility of figuring out the RAI space by
presenting their team’s needs and asking formative questions even when their
objectives around RAI were often not clear, such as which values to consider
(e.g., “privacy or transparency?”), what certain values mean (e.g., “what
trustworthiness as an RAI value should mean to the model and the team”), how
to operationalize specific values (e.g., “How does trustworthiness apply to
rule-based models? What kind of RAI values to invoke while collecting data?”,
and how to interpret outcomes and map them on to their team’s objectives.

Participants (n=5) shared how leading such RAI initiatives burdened their
professional lives in various ways. Multiple participants reported that the
RAI field was still in its infancy and taking up responsibilities in such
conditions meant that their efforts were not deemed a priority or sometimes
even officially recognized as AI/ML work (Madaio et al., 2020). Consequently,
the practitioners possessed limited understanding of the direction to take to
educate their team, convert their efforts into tangible outcomes, and
effectively align their educational outcomes to the team’s objectives. P13, an
RAI enthusiast and an engineer at a large-scale social media company shared
how their RAI effort seemed like an endless effort, “At this point, I probably
know more about what things (RAI values) we don’t want in it (model) than what
we do want in it …It’s like I am just learning and figuring out what’s missing
as I take every step …It is unclear which [RAI] direction will benefit the the
team.” Moreover, the burden of educating multiple team members was on the
shoulders of a very few practitioners tantamounting to substantial pressure.

Metcalf et al. (2019) in their paper around technology ethics put forward the
term ‘ethic owners’. This role shares similarity with the bottom-up vigilantes
and the educators as they both are motivated and self-aware practitioners,
invested in foregrounding human values by providing awareness and active
assistance while institutionalizing the processes. However, Metcalf’s ethic
owners’ responsibilities were clearly defined. Their tasks of working with
teams or higher management were perceived as visible, prioritized work for
which they would be credited for career growth or otherwise. While bottom-up
informal roles in our research performed similar set of tasks, their efforts
were seen as tangential, ‘administrative’, and underappreciated. It is not
just that there was an additional burden, but even the outcomes of taking on
this additional burden for bottom-up informal roles were dissimilar to the
ethic owners. Taking up informal RAI work was more problematic when the
requirements in the later stages of ML were unprompted, compelling
practitioners to focus on these efforts at expense of their own work.

In our findings, one form of the need came as academic criticism or critique
around particular values that were seen concerning a particular product (e.g.,
“what steps are you taking to ensure that your model is equitable?”). Another
form of need came from end-users’ behavior who experienced the models through
a particular product. P20, a user experience researcher working with deep
learning models in finance, shared how user feedback brought about new RAI
needs that became their responsibility:

> “Once the users use our product and we see the feedback, it makes us
> realize, ‘oh, people are sometimes using this feature in an unintended way
> that might in turn impact the way we are going about certain values, say
> transparency’ …. Initially we were like, ‘We should strive for transparency
> by adding a lot of explanations around how our model gave a particular
> output’. Later we realized too many explanations [for transparency] fostered
> inappropriate trust over the feature…UXR represents user needs so its on me
> to update the team on the issues and suggest improvements.

A few practitioners (n=2) also mentioned how the constant juggling between
their own role-based work and the unpredictability of the RAI work pushed them
to give-up the RAI responsibilities all-together.

Top-down: Rigidity in Open-discovery. While the burden of ownership and
execution of RAI values in bottom-up structures were on small group of
individuals, they had the flexibility to choose RAI values that were
contextual and mattered to their team’s ML models or projects. On the
contrary, we found that top-down institutional structures limited the teams’
engagement to “key” RAI values that impacted organization’s core business
values. For instance, P15’s company had trust as a key value baked into their
business, requiring P15 to focus on RAI values that directly reduced specific
model’s biases, thereby increasing the company’s trust among their users.
Consequently, several RAI practitioners had to skip RAI value exploration and
sharing. Instead they directly implemented predetermined RAI values by the
management just before the deployment. P06, an engineer at a large tech
company working in conversational analysis models, described this lack of
choice:

> “To be honest, I imagine lots of the conversations, around the sort of
> values that need to go into the model, happened above my pay grade. By the
> time the project landed on my desk to execute, the ethics of it was cleared
> and we had specific values that we were implementing.”

Public-oriented legal issues and ethical failures, especially when launching
innovative models (e.g., transformer networks), also determined the RAI values
that were prioritized and the subsequent formal RAI structures that were
established by the organizations. P19, a policy director at a RAI consultation
firm facilitating such structures, shared how such impromptu structures were
quite common in response to ever-changing laws around AI governance:

> “Even if you’re conservative, the current climate is such that it’s going to
> be a year or two max from now, where you will start to have an established,
> robust regulatory regime for several of these (RAI) issues. So a good way to
> be prepared is to create the [RAI] programs in whatever capacity that
> enables companies to comply with the new regulations, even if they are
> changing because if you have companies doing Responsible AI programs, it
> eventually gets compliance and executive buy-in. ”

Instead of investing in RAI structures to comply with different regulations in
Tech Ethics such as codes of ethics, statements of principle, checklists, and
ethics training as meaningful, organizations perceive them as instruments of
risk that they have to mitigate (Metcalf et al., 2019). In line with the
previous literature (Green, 2021; Bietti, 2020), our findings indicate that
practitioners often find false solace in such structures as they run the risk
of being superficial and relatively ineffective in making structures and
practices accountable and effective in their organizations. However, adding
nuance to this argument in the case of RAI practices, we found that
practitioners more broadly devoted time and energy to follow established and
prioritized values (e.g., trust or privacy) due to directed and concerted
focus. It allowed for organization-wide impact since the “buy-in” already
existed (Madaio et al., 2020).

Top-down: Under-developed Centralized Support. However, in the case of less
clearly defined values (e.g., non-maleficence or safety) we observed a limited
scope for nuance and despite best efforts, the centralized concerted direction
does not always pan out as intended. Further, while laws continue to evolve in
this space, participants felt that pre-mediated RAI values might not
longitudinally satisfy the growing complexity of the ML models being
implemented (e.g., multimodal models). Hence, while it might seem that setting
up a centralized top-down approach might be efficient, the current execution
leaves much to be desired. In fact, based on data from over half the
participants, we found that five top-down structured companies integrated
lesser known RAI values into their workflows in multiple ways without
establishing a centralized workflow. Those who did establish centralize
workflows created consulting teams to advise on RAI Practices (similar to
ethic owners Metcalf et al. (2019)).

However, these top-down centralized RAI consulting teams were not always set
up to succeed. As is the nature of consulting, people did not always know the
point-of-contact or when and how to reach out. The consulting teams needed to
also consider opportunities to advertise about themselves and engagement
mechanisms, which was difficult due to the lack of context and nuance around
the teams’ projects. Consequently, it was difficult for such teams to generate
organic interest, unless the teams were already aware of their RAI
requirements and knew a point of contact. P10, a manager who facilitated one
such top-down RAI program in a large-scale technology company for AI/ML teams,
described lack of fixed ways in which teams engaged with them on RAI values,
making it a difficult engagement:

> “We have a bunch of internal Web pages that point you in all different types
> of directions. We don’t have a singular voice that the individuals can speak
> with …. Its currently hodgepodge. Some teams come to us willingly. They had
> already thought about some harms that could occur. They say, ‘Here’s our
> list of harms, here’s some ideas on what we want to do’ They’d already done
> pre-work and are looking for some feedback. Other teams come to us because
> they’ve been told to. …They haven’t thought much about RAI and need longer
> conversations …Other teams were told to go track down an individual or team
> because they are doing ML stuff that will require RAI assistance, but they
> don’t know about us”

###  4.2. Challenges within RAI value Discourses

Fruitful co-production requires well-established institutional structures that
can empower stakeholders to engage in stable democratic discourses with an aim
of knowledge production (Jasanoff, 2004a). In the previous section, we
uncovered different structural challenges at the institutional level that
contributed to knock-on effects, creating further challenges for practitioners
during the co-production and implementation of RAI values.

Discourse: Insufficient RAI Knowledge. A key challenge that many practitioners
experienced in co-producing RAI values in team was the difficulty in engaging
deeply with new and unfamiliar RAI values deemed important by the team
members. P07, a policy advisor in a large technology company, who regularly
interacted with those who implemented RAI values, described the “superficial
engagement” with values as an act of “ineffective moralizing”, wherein
practitioners struggled to develop deeper interpretations of the team’s shared
values and contextualize them in relation to the ML models they were
developing.

P07 mentioned several key critical thinking questions that AI/ML practitioners
did not deliberate within their teams, such as “Is this RAI value applicable
to our product?”, “how does this value translate in diverse use cases?”, or
“should this value be enabled through the product?” The need for deeper
engagement becomes particularly important in a high-stakes situation, such as
healthcare, where certain conditions have an unequal impact on particular
demographics. P12 experienced this complexity while overseeing the development
of a ML model focused on health recommendations:

> “So a lot of models are geared towards ensuring that we have we are
> predictive about a health event and that almost always depends on different
> clinical conditions. For example, certain ethnic groups can have more
> proclivity to certain health risks. So, if your model is learning correctly,
> it should make positive outcomes for this group more than the other groups.
> Now, if you blindly apply RAI values without thinking deeply about the
> context, it might seem that the model is biased against this group when in
> reality these group of people are just more likely to have this condition,
> which is a correct conclusion, not a biased one.”

Such deeper analysis requires hands-on practice and contextual training in the
field and formal RAI education. In our findings, top-down structures were only
effective to fill the gap for key values that aligned with the company’s
vision, leaving a much needed requirement for contextual, high-quality RAI
education for more emergent RAI values that could be modularized for specific
teams. P02, a content designer for a large health technology company shared
how this gap looked like for their team that was designing content for a
machine translation team,

> “One thing that would have been beneficial is if I or my team could somehow
> get more insights on how to think about trustworthiness in the context of
> the content produced by our machine translation model and probably evaluate
> it …Often time, I just go to someone who is known to have done some work in
> this [RAI] and say, ‘Hey, we want to design and publish the content for the
> model like in a month from now, what is bare minimum we could do from [RAI]
> principles point of view?’…Sometimes it’s never-ending because they say I
> have not thought about this at all and that it is going to take a month or
> maybe much more longer to get these principles implemented.”

Participants like P02 had no alternative but to reach out to their bottom-up
structures to seek assistance, discuss, and reduce gaps in their RAI
knowledge. On occasions, such avenues of discussion were non-conclusive. Prior
literature in AI/ML and organization studies have shown how such unequal
dependence on bottom-up structures over top-down in deliberation can
contribute to tensions, and in turn propose an “open, federated system”
linking different actors, resources, and institutions to provide a community
based support (Rakova et al., 2021; Scott and Davis, 2016).

Discourse: Deprioritized Unfamiliar & Abstract Values. Naturally,
practitioners tried to solve the superficial engagement problem by de-
prioritizing values that they found unfamiliar. In our study, most
practitioners (n=18) said that they were familiar and comfortable talking
about RAI values like privacy and security as they were already “established”
and had “matured over time”. They sharply contrasted this perceived
familiarity with other RAI values like explainability and robustness. The
familiar values were well backed with stable top-down structures and dedicated
teams, such as compliance departments and dedicate RAI personnel , making it
easy for practitioners to develop mental models of deeper engagement. P20
shared their experience in this regard in their organization:

> “The ideal situation would be like, ‘Oh, I have certain RAI principles that
> I want to make sure our product has or addresses’. In reality not all the
> principles are thought out the same way and applied in the first go. It
> usually happens in layers. First and foremost, people will look at privacy
> because that’s super established, which means everyone knows about it, they
> already have done probably some work around it, so its easy to implement.
> And then after that, they’re like, ‘Okay, now let’s look at fairness or
> explainability’ …We usually have to be quick with turnaround like one or two
> months. Its nice to bring up values that are new but naturally they also
> require familiarizing and implementation effort within the team and people
> see that”

Other practitioners (n=3) also followed a similar de-prioritization process
for RAI values that they felt were abstract and did not have a measurement
baseline (benchmarks) as opposed RAI values that could be easily measured
quantitatively against a baseline. An example observed in this category was
the contrast between RAI values like interpretability, which had concrete
implementation techniques and measurements (e.g., LIME) and non-maleficence,
which did not have a clear implementation technique or measurements.
Similarly, practitioners (n=2) who went out of their way to understand and
suggest new interpretability techniques for model debugging techniques (e.g.,
Integrated Gradients, SHAP) found it disempowering when their team members
often negotiated for easier and computationally cheaper values like accuracy
(e.g., P/E ratio) for implementation.

Discourse: Value Interpretation Tensions. Even in situations, when different
practitioners took a similar balanced approach to prioritization, tensions
emerged as different roles interpreted and contextualized the RAI values
differently during the value deliberations. We found these tensions occurring
within practitioners when different practitioners defined RAI values (e.g.,
equity) and mapped them to RAI features and metric (e.g., skin tone)
differently. P18, a senior data scientist leading an AI team in a non-profit
institute, shared one such similar tension among their team members working on
the same project,

> “Probably half of my colleagues do believe that there is a cultural, and
> historical set of RAI values that can be applied to all the products
> organization wide. Other half are vehemently opposed to that concept and say
> that [RAI] values are always model and project dependent. So if you are
> talking about our long-term goal to establish a set of RAI principles, whose
> perspectives should be considered?…This is an uneasy space that needs
> careful navigation.”

While deliberations might occur between team-members, they might occur within
a practitioner, or between the team and the end-consumers of the
product/service. Latter two usually surfaced with user-facing roles, e.g.,
Product Managers or UX Researchers. These roles have the responsibility to
understand, internalize, and embody end-user values in addition to their own
values. Overall, we found that practitioners in these roles had to invest more
effort to tease out their own values from that of the end-users. P04 was a
user experience researcher working on interfacing a large language model for
natural conversations with users. While P04 was interested in eliciting better
insights from the model’s behavior issues (i.e. interpretability (Doran et
al., 2017)), end-users were interested in a simplified understanding of the
model’s opaque behavior (i.e. comprehensibility (Doran et al., 2017)). A UX
Researcher is, however, expected to be the voice of the user in the process.
Consequently, they had the constant burden to elicit both sets of values
appropriately.

Another set of tensions also occurred between practitioners and end-users.
P22, an analyst in a financial firm, described how ML practitioners perceived
RAI values to be mutable and negotiable, allowing them to implement a
particular RAI value in stages instead of all at once. Such a process allowed
P22 (and three other participants who reported similar narratives) to build
the required experience and embed the value in the ML model or AI product.
However, end-users expected these embedded RAI values as absolute and non-
negotiable and not on a “sliding spectrum” because they are “they are often
the list of ignored rights”, leading to practitioner-user RAI tensions.

Our findings show that tensions that arose from non-uniform RAI value
knowledge and subsequent disparate value interpretations were unproductive and
a significant obstacle in the overall co-production process of RAI values.
This can be attributed to a nascent RAI field that has given rise to new forms
of values (e.g., explainability, interpretability) whose definitions and
contexts which keep changing. This is in contrast with prior value studies in
HCI studies where the tensions and conflicts around relatively more
established values (e.g., privacy) do not occur until the implementation stage
(Spiekermann and Winkler, 2020; Eriksson et al., 2022; Friedman and Hendry,
2019). Our findings show that the majority of value tensions occur much
earlier in the value interpretation stage, often contributing to the
abandonment of the value discussions altogether.

Implementation: RAI Values and Conflicts within. Implementation of RAI values
was also not a straight forward process as implementing certain RAI values
created conflict with other RAI values. For instance, P1, an engineer working
on classification models in VR environments shared how their decision to
improve accuracy by excluding instances of objects with sensitive cultural
meanings (e.g., objects with LGBTQ references) also had direct repercussions
on the diversity and fairness of the model. Implementing RAI values also
created cascading dependencies on the inclusion of other RAI values. For
instance, P16, a program manager working as an RAI facilitator for a big tech
company, shared the issues team members experienced around cascading RAI
values:

> “One common issue I see is with teams that are dealing with model Fairness
> issues. Most often the solution for them is to improve their datasets or
> sometimes even collect new forms of demographic data to retrain their model
> and that opens up another rabbit hole around privacy that the team now has
> to navigate through and ensure that their data adhere to our privacy
> standards. More often than not, teams don’t even realize they are creating a
> new issue while trying to solve their existing problem. ”

Implementation challenges also occurred when organization’ business values
were in tension with those of external clients. In such cases, the team’s
commitment to engage with RAI was at odds with clients’ business priorities.
P02, a technical program manager for a service company that developed ML
models for clients in the energy sector, had a similar issue when their team
was building a model for street light automation. After P02’s team received
the data and started developing the model, they pushed for the value of
safety. However, it was at odds with the company’s value of efficiency,

> “We should prioritize model optimization in those areas where there are
> higher crime rates …we don’t want blackouts, right? …Their argument was if
> there was a very high crime rate, such areas will also have high rate of
> purposefully damaging the lighting infrastructure. Prioritizing service to
> such areas will only create high amounts of backlogs as people will just
> vandalize it again. …So they just have different priorities. After that, our
> team just stopped following it up as it went into the backlog. ”

P02’s team gave up RAI value deliberation and implementation altogether after
their clients either deprioritized their multiple attempts to make them RAI
ready or took an extremely long time to approve their requests.

Implementation: Unexpected Late-stage Value Changes. Another challenge
practitioners faced was encountering new RAI values during late stages of
implementation. These values were not initially shortlisted. Instead, they
were brought out later and sometimes championed by a very vocal practitioner,
who felt deeply about it. Such late-stage RAI values also became a part of the
discussion when practitioners in the team uncovered last-moment issues (e.g.,
bias) during implementation that significantly impacted the model. Several
participants (n=3) shared how such late-stage RAI values decreased the
productivity of their overall RAI discourse and implementation efforts,
leading to a negative experience. While such last-minute changes are not
welcomed, P12, an engineer shared how it also gives an opportunity to the
developers to ship a better product before any harm might have been done. This
tension between potentially better outcomes and slower implementation was
visible in how the implementation efforts and timelines were impacted (Madaio
et al., 2020).

Such values also disrupted a planned implementation by taking the spotlight
and pushing the team in navigating the company’s non-standardized approvals,
thereby significantly altering the project timeline. For example, P23, an ML
engineer shared how when they received issues around fairness from other
stakeholders, it meant “substantial changes to the model from the ground-up,
because most of the time, issues with fairness stem from the data”. It meant
revisiting the data and redoing data collection or further debugging to remove
the issues. Moreover, when new and untested RAI values assumed prominence
(e.g., interpretability), more time and effort was required from the
practitioners during implementation. RAI facilitators are essential in easing
the tension in such situations by engaging in back-and-forth conversations
with the teams to reduce the effort, streamline the process, and help
practitioners appreciate the eventual consequences of implementing the RAI
values.

Implementation: Perceived Misuse of RAI values. Lastly, we also observed
tensions between individual efforts in implementing RAI values and their
organization’s use of such efforts for the overall business purposes. For
instance, P15, research director of a large-scale technology company
overseeing research in large language models, shared how he was actively
supporting a few teams in his company to co-produce and embed explainability
into their models. However, he also expressed his concern about how companies
could misrepresent such embedded RAI values,

> “I worry that explainable AI is largely an exercise in persuasion. ‘This is
> why you should trust our software’ rather than ‘This is why our software is
> trustworthy’ …I’m not saying everybody who does explainable AI is doing that
> kind of propaganda work, but it’s a risk. Why do we want our AI to be
> explainable? Well, we’d like people to accept it and use it …Explainability
> part is ethically complicated …even for explainability for the practitioners
> the company wants it to be explainable, transparent, reliable, all those
> things as a means to an end. And the end is ‘please like our model, please
> buy our software’”

We found two more practitioners who raised similar concerns with other RAI
values, such as privacy and trust. They were concerned that making their
product “completely responsible” could enable companies to market their
products as nearly perfect, leading to overtrust and over-reliance. These
findings align with the ethics-washing phenomenon within the tech ethics
literature which argues that companies sometimes invest in ethics teams and
infrastructure, adopting the language of ethics to minimize external
controversies and superficially engage with the proposed regulations (Green,
2021; Wagner, 2019). Practitioners who expressed these sentiments were quite
dissatisfied with their RAI implementation work as they felt their actions
were merely a “band-aid” solution for the organization, instead of
meaningfully altering organization’s culture and practices.

###  4.3. Representational Strategies to Mitigate RAI Challenges

In response to the challenges mentioned in the aforementioned sections, we saw
several strategies used by the practitioners to overcome the limitations in
RAI value co-production. To present the strategies, we use a form of
representations called values levers (Shilton, 2013), a set of activities that
facilitate opportunities to share and collaborate around values. We show how
different practitioners use value levers to build upon current RAI
institutional structures and make their RAI co-production manageable. In an
ideal situation, value levers can also be employed in any situation of RAI co-
production. For example, organizations created several formal RAI structures
for practitioners to facilitate sharing and deliberation of values. These
included top-down standardized guidelines, such as guidebooks (e.g., PAIR
(PAIR, book), HAX (Microsoft, lkit)) around established RAI values, bringing
in experts to share their experiences around co-production (lectures), and
enabling shared spaces for co-production. However, in this section, we will be
looking at value levers specifically developed in response to the challenges
experienced in RAI value co-production.

Institutional Value Levers: External Expertise and Certifications to reduce
Ambivalence. One of the ways in which organizations brought stability to their
inconsistent top-down RAI institutional structures was by taking assistance of
independent agencies or professionals who specialized in establishing values
levers that helped streamline their existing structures. One such value lever
was ‘Responsible AI certifications’ that were designed to bring different
recognized and informal RAI co-production activities under one-roof. These
programs act as facilitators between the non-technical and technical workforce
by enabling co-production around RAI values to make them compliant with
upcoming regulations. Participants reported that different activities were
packaged into the RAI certification program, such as getting buy-in for
particular RAI values, leveraging trusted partners for running impact
assessments, engaging key actors in value discovery and prioritization, and
implementing appropriate RAI methods. P19, policy director of one such RAI
certification organization, shared how these certifications are effective in
sectors, such as energy, mining, and human resources that often have a limited
technology workforce. They described effort of facilitating RAI value
conversations within their client teams as a key part of the certification
process:

> “It is important to have everybody on board for those [RAI] value
> conversations. So we try really hard to have all the different teams like
> internal or external audit, legal, business, data and AI team come together,
> brainstorm, discuss different [RAI] issues in specific contexts and
> shortlist [the RAI values], even if we just get a little bit of their time
> …everyone needs to be brought in early because we conduct a lot of
> activities likes audit analysis, bias testing…it saves time, addresses
> several concerns, and establish streamlined [RAI] processes. …For
> simplicity, we just package all of the different activities we do under RAI
> certification. …Some times few activities are already being executed by the
> organization, we just do the job of aligning them in a way that works for
> the organization.”

Such external expertise and certifications can provide an opportunity for open
discovery, bolster existing centralized support, and identify RAI values that
might otherwise be discovered at the last stages.

Institutional Value Levers: Activities to Distribute RAI burden. We also found
several nascent but more focused value levers in bottom-up institutions
focused on distributing the burden experienced by a few roles more widely
within the team. These value levers provided opportunities for increased
participation from stakeholders, especially in the starting stages by enabling
them to bring-in complementary RAI values into the team. Most commonly used
levers in this context included scenario-based narratives and role plays, and
open-ended activities that engaged practitioners in opinion formation and
sharing. Other value levers included conducting a literature review of
specific RAI values and applicable cutting-edge methods, definitions, and
guidelines around them to share and invoke feedback from the team. We also
observed more experimental value levers that were geared towards bringing-in
complementary RAI values of external stakeholders (e.g., end-users) into the
team.

For example, P18, a data scientist working in a startup, hosted a panel to
capture complementary perspectives around AI explainability. Visibility into
how explainability was perceived differently by different community members,
such as NGOs and government, contributed to a better understanding and
alignment within the team to develop explainable models. In a similar example,
P09, an engineer working on a reinforcement learning model in the context of
healthcare for low resources in India, facilitated field visits to the end-
user communities. Such exposure helped roles that were passive in sharing
their values as well as roles that were thinking about new values, such as
social justice, in the RAI discourse. Overall, these value levers (narratives,
role-plays, literature reviews, panels, and field visits) focused on primarily
bottom-up structures, which helped reduce pressure on specific roles and limit
superficial value engagements.

Discourse Value Levers: Facilitating Disagreements. Moving our focus to RAI
value co-production, we saw user-facing practitioners create explicit
opportunities for disagreements and healthy conflicts to tackle the problem of
superficial value engagement and improve the quality of their teams’
deliberations. Disagreements in co-production phase allowed practitioners like
UX researchers and product managers to think inclusively, capture diverse
perspectives and expert knowledge, and more importantly predict future value
conflicts. For example, P04, a UX researcher, created bottom-up adversarial
prioritization framework. In the starting phases of this framework, the UX
researcher pushed team members to go broad and co-produce values by wearing
other practitioner’s hats and invoking their RAI values. This practice allowed
them to bring forward interesting disagreements between different roles that
were then resolved and prioritized to achieve a small set of meaningful RAI
values. P04 recalled two of the values that received maximum disagreements
were diversity and inclusion. Wearing complementary user hats enabled
practitioners to familiarize with these values that were otherwise unfamiliar
in their own roles. Other top-down RAI programs also facilitated similar
structures, explicitly providing narratives that brought out disagreements,
e.g.,:

> “Usually I will write something in the prompts that I think that the team
> absolutely needs to hear about but is controversial and opposing. But what I
> do is I put it in the voice of their own team so that it is not coming from
> us. It is not us scrutinizing them. That promotes interpersonal negotiation
> that pushes individuals to really defend their values with appropriate
> reasoning.”

According to P19, having such RAI system in place early also allows companies
to judge its ML models benchmarks when compared to their competition.
Leveraging the adversarial prioritization framework appropriately in both top-
down and bottom-up structures can enable open-discovery, and surface the
values and related conflicts for resolution.

Discourse Value Levers: Model Cards & Visual Tools to Reduce Abstractness from
Values. We found that practitioners also created succinct representations and
simplified documentation to bring much needed clarity to various RAI values
and simply associated models. For instance, engineers shared documentation of
model and data cards, making it easier for non-engineering and engineering
roles to grasp the information. P23, a senior engineer at an AI startup
looking into sustainability, shared the process:

> ‘Even we have introduced this concept of a model card, wherein if a model is
> developed, the model card has to be filled out. So what is a model card? A
> model card is a series of questions that captures the basic facts about a
> model at a model level at an individual model level. What did you use to
> build a model? What was the population that was used? What is the scoring
> population? It is like, having all of that in a centralized standard format.
> Goes a long way to roll it up because the product can be very complex as
> well, right? With multiple players and whatnot. But having that information
> collected in this way benefits other roles that own the product to think
> about different values that are missing’

UI/UX designers, UX researchers, and analysts also used similar documentation
tools to initiate discussions and receive feedback from other practitioners in
the team. P20, a UX researcher, used presentation slides containing model
features to facilitate brainstorming sessions and receive feedback from other
roles. They also repurposed tools and methods used in their own work to give
shape to their peers’ abstract values. For example, P20 reused online jam-
boards containing key RAI values and user findings for affinity diagramming,
enabling the team to “categorize the findings and map them to specific RAI
values”. Other RAI levers in this category included designing and sharing
infographics and regular RAI standups where practitioners took it upon
themselves to be stewards of RAI principles for the team to give updates,
receive feedback and learn about team’s perspectives on specific RAI values.

Implementation Value Levers: Office Hours, User stories, Safe Spaces to Reduce
Tensions.

A few value levers that were part of top-down RAI programs were also effective
in reducing various value tensions that occurred between different
practitioners (n=2). One such program was RAI office hours that was available
for elicitation and production, but were also extremely effective for tension-
resolution. A typical office hour was 30 minutes in which practitioners
engaged with a relevant expert and an experienced facilitator. One key way
experts solved the tensions in these sessions was by collecting and providing
concrete case-study examples. For example, P21, an RAI office hour
facilitator, shared an example about the use of his office hours. The
practitioners were at odds with each other in implementing explainability and
trustworthy features. During the office hours, P21 responded by sharing an
edge case scenario where even good explanations might backfire, such as, “If a
pregnant woman had a miscarriage, showing even good end-user explanations
around why they are seeing infant-related content can be very problematic.
Explainability should be carefully teased out based on the context in which it
is applied.”

Another set of value levers used especially by the roles facing end-users were
user stories and scenarios to influence and persuade users to change their
value priorities and align with rest of the team. These levers were also used
by the roles to converge on key values after engaging in healthy conflicts
within the divergence phase. For example, P04, exposed different pain points
and key user journeys by “highlighting the clip of a user that is really,
really amazing story that is either very painful or poignant”. Interestingly,
P04 was aware how such value levers had to be evoked carefully,

> “If that story is not representative, I’m manipulating the system. If it is
> representative, I’m influencing the system…I will have to be careful not
> operate on the side of manipulation and try to be very squarely on the side
> of influence. So, I do like regular checks for myself to make sure that I am
> operating on influence, not manipulation, in terms of the stories that I am
> allowing people to amplify.”

Lastly, in order to tackle several types of value conflicts in the co-
production of RAI values, we found different RAI value levers that focused on
improving value alignment. One key alignment strategy was to create structures
and activities that aligned the team’s RAI values pretty early in the process.
One such activity that we saw in both practitioner’s and RAI programs was
providing a safe space to encourage open discussions among individuals to
empathize with other members. P09 shared,

> “One alignment strategy was open discussion with the safe space, where team
> members could fail, be called out and to learn from each other as we were
> developing values. So say someone finds the value of democratization really
> important, they are made to articulate what they mean by it.…. It is easy if
> there are different buckets in which they can categorize and explain because
> then people can easily surface all the different ways they think and
> prioritize values and that helps with alignment”

##  5\. Discussion and Future Work

Overall, our findings show that co-production of RAI values in practice is
complicated by institutional structures that either support top-down decision-
making by leadership or are inhabited by bottom-up practitioners exercising
voluntary agency (section 4.1). In other case, multiple challenges exist when
practitioners have to reconcile within their internally held values and RAI
values expected from their roles; and between themselves and their team-
members. Our findings also show that discourse around alignment and
prioritization of RAI values can sometimes be unproductive, non-conclusive,
and disempowering when practitioners have to implement said RAI values
(section 4.2). We observed a lack of transparency, and unequal participation
within organizations; and between organizations and end-users of their
products/services (section 4.2). Despite the relatively complicated lay of the
land, practitioners have been pushing ahead and discovering multiple
strategies on how to make progress (section 4.3). In the subsections below we
will unpack these challenges, strategies and potential future work across the
three sites of co-production: institutions, discourse, and representations.

Sn.No |  Challenges |  Strategies
---|---|---
1. |  Complexity of RAI values, unstable centralized structures |  RAI value certification to streamline ambivalent structures
2. |  Burden on a few vigilantes who have taken on responsibility |  Scenario-based narratives, adversarial role, panels, and field visits to distribute RAI burden
|  Stress on a few curious practitioners to educate the team |
3. |  Top-down RAI in response to external pressures. Lesser RAI Autonomy |  RAI value certification to streamline ambivalent structures
4. |  Lack of RAI knowledge, superficial engagement |  Model cards & visual tools to provide clarity and initiate discussions
|  RAI values without business case, benchmarks deprioritized |
5. |  Certain RAI values in conflict with other values |
|  Organization values in conflict with client’s business needs |  Prioritization framework for facilitating disagreements
|  Late stage value changes, substantial development effort |  Office hours for conflict resolution, user-stories, & safe spaces to resolve challenges
|  Practitioner’s RAI values in conflict with organization’s business values |
Table 2. Summary of challenges mapped against strategies.

###  5.1. Envisioning balanced Institutions: Middle-out RAI Structures

Inequity in Burden. According to Jasanoff (2001), strong institutions provide
a stable environment for effective knowledge co-production. They can also act
as safe spaces for nurturing and transforming contested ideas to effective
practices leading to long-lasting impact on the immediate ecosystem. Recent
scholarship by Rakova et al. (2021) has put faith in an aspirational future
where organizations would have deployed strong institutional frameworks for
RAI issues. Our findings show that as of today, top-down structures are
underdeveloped. Organizations have deployed structures that range from being
reactive to external forces (e.g., compliance, public outcry) by tracking
teams that implement RAI to proactively establishing structures that make
teams RAI-ready (e.g., office hours). Furthermore, stable workflows have been
established for a limited number of values or use-cases, restricting the
number of teams that could leverage such workflows.

Being in the midst of restrictive structures, particular practitioner roles
embraced the persona of bottom-up vigilantes and self-delegated themselves to
be champions of lesser known RAI values (e.g., non-maleficence and
trustworthiness). They initiate open-ended exploration for value discourses
and subsequent value implementation. However, such bottom-up structures also
put burden and occupational stress on selective roles, risking the
implementation success of such RAI projects. In particular, we have found that
roles like UX researchers, designers, product managers, project managers,
ethicists have been taking the brunt of this work. These findings build on the
previous work (Rakova et al., 2021; Madaio et al., 2020), highlighting
existing inequity and subsequent individual activism being performed by some -
either by volition or due to lack of alternatives.

Enabling Equal Participation. Going forward, there is a need for a holistic
middle-out approach that seeks a balanced synergy between top-down and bottom-
up structures while balancing for the challenges that each of the structures
provide. For instance, organizations can work with RAI value stalwarts and
champions to formalize and streamline bottom-up workflows, making it a
standard practice for all teams to engage in open-ended exploration of RAI
values. Such a process can enable teams to look beyond loosely applicable
organization-recommended RAI values and shortlist those values that actually
matter and apply to their team. To standardize the structure, organizations
can leverage independent (flat) team/roles that can guide the target team
through the team while giving enough room for exploration.

Organizations can also use a middle-out approach to reduce the burden and
occupational stress on specific roles through several top-down activities. One
such way is to facilitate structures that can lower the barrier for diverse
internal stakeholders to engage in RAI value co-production, regardless of
their proximity to AI products or ML models. For instance, data-workers and
teams/roles that do internal testing of the models (dogfooding) can contribute
to the RAI value co-production. Same structures can also enable engagement
with external stakeholders, such as end-user communities, policy, experts, and
governance agencies in the initial stages of value co-production.
Consequently, practitioners’ chances to foresee or anticipate changing
requirements could improve, especially in the later stages of AI/ML lifecycle.
Better yet, this could potentially improve not just the “user experience” of
value discourse, but also the efficiency of implementation - a goal valued by
private companies. This could be a win-win situation for multiple stakeholders
by helping the top-down RAI structures align with business goals. While, our
research uncovered only top-down and bottom-up structures that were mutually
exclusive, other structures might exist. For example, while we envisage
middle-out structures to be advantageous, future research is needed to
operationalize and simulate such structures; and discover existing
implementations. There might be some challenges uniquely inherent in those
structures. We encourage future researchers to continue this line of enquiry

###  5.2. Envisioning better Discourses: Enabling Critical RAI Value
Deliberations

Negativity in Deliberations. The ultimate aim of co-production discourse is to
engage in competing epistemological questions. Jasanoff calls it interactional
co-production (Jasanoff, 2004b, ch 8) because it deals with explicitly
acknowledging and bringing conflicts between two competing orders: scientific
order brought about by technological innovations and social order brought
about by prevalent sociocultural practices within the community. In our
findings, several underlying conflicts surfaced between scientific and social
order (section 4.2). In one instance, practitioners had to choose between
socially impactful but lesser explored RAI values (social order) and lesser
applicable but established values with measurable scientific benchmark
(scientific order). In another instance of tension an RAI value occupied
competing social spaces (e.g., equity). The underlying issue was not the
conflicts itself but lack of systematic structures that enabled positive
conflict resolution around the conflict. Such implicit conflicts often met
with deprioritization and conversations ended unresolved. There is an urgent
need to transform such implicit conflicts to explicit positive deliberations.
Organizations aiming for successful RAI co-production need to be more
reflexive (Jasanoff, 2004b, ch 9)(Wynne, 1996), mobilize resources to create
safe spaces and encourage explicit disagreements among practitioners
positively, enable them to constantly question RAI values or co-production
procedures that help embed them. While we saw some instances of these explicit
practices in the form of value lever strategies, such instances were sporadic
and localized to very few teams.

Acknowledging Differences Safely. Our findings around challenges within RAI
value discourse also showcase the politically charged space that RAI values
occupy in an organization. In their critical piece around implementation of
values in organization, Borning and Muller (2012) bring out a few value
characteristics that are applicable to our study. First, values are not
universal. Values are recognized, prioritized, and embedded differently based
on a bunch of factors, such as practitioner’s role, organizational priorities
and business motivations which make RAI as a complex space. In our findings,
roles prioritized those RAI values that were incentivized by the organizations
and the AI/ML community through computational benchmarks focused on the RAI
value outcomes. This is problematic as the RAI values that have computational
benchmarks and have implicit organizational incentives might not map onto the
RAI issues that are pertinent in the community. One way to solve this mismatch
is by rethinking the definition of benchmarks from value outcome to value
processes taken up by different roles (or teams) (Friedman, 1997). For
example, organizations can encourage teams to document their co-production
journeys around lesser known RAI values that can act as RAI benchmarks.

The second issue that Borning and Muller (2012) bring out is whose value
interpretations should be prioritized and considered? In our findings,
tensions emerged as same values were interpreted and prioritized differently
by different stakeholders. End-users viewed RAI values as immutable and
uncompromisable, whereas practitioners viewed them as flexible and iterable.
Similar tensions were also observed between the internal stakeholders in
contextualizing the same values. While we saw a few strategic value levers,
such as office hours, they were mainly targeted at the stakeholders that were
within the same team. Extending this line of value levers, we propose
participatory approaches that take a balanced approach for both internal and
external stakeholders. In particular, we take inspiration from participatory
design fictions, a participatory approach using speculative design scenarios
(Liao and Muller, 2019; Dourish and Bell, 2014), to find alignment on
polyvocal speculations around embedded values in the emergent technologies.
Deliberations around the fiction narratives can be used to arrive at a common
ground RAI value implementation that are both contextual and implementable.

Shaping Institutional Structures to Improve Discourse. Employing co-production
as a lens has also allowed us to reveal a very delicate, symbiotic
relationship between the practitioners’ discourses and the institutional
structures. Several of the discourse challenges observed in our findings, such
as issues pertaining to the lack of RAI knowledge and deprioritization of
lesser known values, stemmed not only from the lack of mature top-down
structures but also dependency on a singular institutional structure. The
limitations of top-down structures pushed several practitioners in-turn to
create informal bottom-up structures, shifting the pressure from one structure
to the other. We argue that RAI-focused organizations need to seek a balance
between stability (top-down structures) and flexibility (bottom-up) (Weick,
2006). In addition to new middle-out internal-facing institutional structures,
RAI discourses can benefit from external-facing institutional structures that
can help such discourses be impactful. This can be achieved by bringing in
diverse external collaborators, such as NGOs, social enterprises, governmental
institutes, and advocacy groups. It will also help avoid the issue of co-
optation(Green, 2019) for true meaningful impact of such structures on the
practices.

###  5.3. Envisioning better Representations: Value Levers

Value Levers Enabling Progress. As the third site of co-production,
representations are essential manifestations of knowledge deliberations
(Jasanoff, 2004b, ch 3). They provide surrogate markers for progress on value
alignments or even responses to tensions between stakeholders. In our study,
we saw several value levers that were deployed within organizations at
different stages to tackle co-production challenges. These value levers
enabled progress during knowledge deliberations (section 4.3). For instance,
during deliberation, practitioners used role-specific value levers (e.g.,
visual tools by UX researchers & designers) as a window into their thought
process around specific RAI values. Similarly, enabling safe spaces provided
opportunities of RAI value alignment among different roles. As
representations, these value levers improved the transitions between internal
stages and acted as vital markers to improve alignment. We call them internal
representations. Prioritization Framework employed to resolve value related
conflicts across individuals was another example of internal representation.
In contrast, we also found a few externally represented value levers, such as
RAI certifications, that enabled organizations to reduce ambivalence in their
structures while showing their RAI readiness and compliance to the outside
community. Interestingly, we uncovered limited evidence of external
representations that engaged with end-user communities directly. We posit that
the lack of external representations can be attributed to the deprioritized
perception of the end-users’ role in RAI value conversations. External
representations have the potential to act as stable participatory structures
that enable participation of external stakeholders, making RAI value co-
production successful. This might also be due to lack of sufficient incentives
to enable such participatory structures. We hope that recent progress made by
UNESCO’s historic agreement (UNESCO, 2020) might provide the much needed push
for organizations to share and learn together.

Value Levers Enabling Practitioners. As we end this discussion, some readers
might assume that we now have strategies to manage the multiple challenges
coming our way as we deliberate and implement RAI values in products. Our
research with 23 practitioners, points to the opposite - we are far from it.
As one practitioner said, “It’s currently hodgepodge”. Multiple individuals
and organizations are trying to surmount this incredibly complex challenge at
institutional and individual level. While value levers discussed in this work
were successful in helping practitioners make progress, the discovery of these
value levers has at best been serendipitous. Sufficient support structures,
skills training, and knowledge dissemination will be needed to enable
practitioners to overcome these and unknown challenges yet. One can liken
these value levers as a tool-belt for practitioners, as summarized in Table 2.

There is a subsequent need to design systems that can provide access to these
tools in a systematic way. This would require centering research amongst
practitioners who are making products. We hope that future educators,
researchers, and designers will pursue this opportunity to uncover further
challenges, learn from existing strategies, develop better strategies as they
iterate, and create design systems to support practitioners. Further, we need
to find more tools, and we need to share the tool-belt with other
practitioners.

##  6\. Limitations & Conclusions

Based on qualitative research with 23 AI/ML practitioners, we discovered
several challenges in RAI value co-production. Due to their roles and
individual value system, practitioners were overburdened with upholding RAI
values leading to inequitable workload. Further, we found that implementing
RAI values on-the-ground is challenging as sometimes these values conflict
within, and with those of team-members. Owing to the nascent stage of RAI
values, current institutional structures are learning to adapt. Practitioners
are also adapting by discovering strategies serendipitously to overcome the
challenges. However, more support is needed by educators to educate RAI/RAI
values, researchers to unpack further challenges/strategies, and for community
to focus on aiding AI/ML practitioners as we collectively find a common-
ground. Our work also has several limitations. First, our overall study is
shaped by our prior HCI research experience in responsible AI. While all the
participants had rich experience working with ethics in the context of social
good in both global north and global south contexts, we acknowledge that our
perspectives around responsible AI and ethical values in AI might be
constrained. Second, our insights are also limited by the overall
methodological limitations of qualitative enquiry, such as sample size of 23
participants across 10 organizations. Future work is required to generalize
our insights across different organizational sectors and ML models using
mixed-methods approach.

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