# Empirical comparison of network sampling techniques

Neli Blagus<EMAIL_ADDRESS>Lovro Šubelj<EMAIL_ADDRESS>lj.si Marko Bajec<EMAIL_ADDRESS>University of Ljubljana, Faculty
of Computer and Information Science, Ljubljana, Slovenia

###### Abstract

In the past few years, the storage and analysis of large-scale and fast
evolving networks present a great challenge. Therefore, a number of different
techniques have been proposed for sampling large networks. In general, network
exploration techniques approximate the original networks more accurately than
random node and link selection. Yet, link selection with additional subgraph
induction step outperforms most other techniques. In this paper, we apply
subgraph induction also to random walk and forest-fire sampling. We analyze
different real-world networks and the changes of their properties introduced
by sampling. We compare several sampling techniques based on the match between
the original networks and their sampled variants. The results reveal that the
techniques with subgraph induction underestimate the degree and clustering
distribution, while overestimate average degree and density of the original
networks. Techniques without subgraph induction step exhibit exactly the
opposite behavior. Hence, the performance of the sampling techniques from
random selection category compared to network exploration sampling does not
differ significantly, while clear differences exist between the techniques
with subgraph induction step and the ones without it.

###### keywords:

complex networks , network sampling , comparison of sampling techniques ,
subgraph induction , sampling accuracy

††journal: Journal of Physics A: Mathematical and Theoretical

##  1 Introduction

Real-world networks are often very large and fast evolving. Therefore, not
only their storage poses a problem, but their analysis and understanding
present a great challenge. In the past few years, a number of different
techniques have been proposed for sampling large networks to allow for their
faster and more efficient analysis. However, some information about the
network is lost through sampling, thus it is of key importance to understand
the changes in the network structure introduced by sampling.

Several studies on network sampling analyze the match between the original
networks and their sampled variants [1, 2, 3], while only few of them focus on
comparing the performance of different sampling techniques [4, 5, 6]. In
general, network exploration techniques like random walk and forest-fire
sampling approximate the original networks more accurately than random node
and link selection [4]. However, Ahmed et al. [7] proposed link selection with
additional subgraph induction step, where the sampled network consists of
randomly selected links (i.e. random link selection) and any additional links
between their endpoints (i.e. subgraph induction). In this way, not only the
performance of random link selection is improved, the proposed technique also
outperforms several other sampling techniques.

In this paper, we apply subgraph induction also to random walk and forest-fire
sampling. We consider ten real-world networks and analyze the changes of their
properties introduced by sampling. We compare eight sampling techniques based
on the match between the original and sampled networks. The results reveal
that techniques with subgraph induction step tend to underestimate the degree
and clustering distribution, while other techniques tend to overestimate both
properties. Moreover, the techniques with subgraph induction overestimate the
average degree and density of the original networks, in contrast, techniques
without subgraph induction underestimate them. It appears that the performance
of the sampling techniques from random selection category compared to network
exploration sampling does not differ significantly, while clear differences
exist between the techniques with subgraph induction step and the ones without
subgraph induction.

The rest of the paper is structured as follows. In Section 2, we first present
the background on network sampling and expose the sampling techniques used in
the study. The results of the empirical analysis are reported and formally
discussed in Section 3, while Section 4 concludes the paper and suggests
directions for further research.

##  2 Network sampling

Let the network be represented by a simple undirected graph $G=(V,E)$, where
$V$ denotes the set of nodes ($n=|V|$) and $E$ is the set of links ($m=|E|$).
The goal of network sampling is to create a sampled network
$G^{\prime}=(V^{\prime},E^{\prime})$, where $V^{\prime}\subset V$,
$E^{\prime}\subset E$ and $n^{\prime}=|V^{\prime}|<<n$,
$m^{\prime}=|E^{\prime}|<<m$. The sample $G^{\prime}$ is obtained in two
steps. In the first step, nodes or links are sampled using a particular
strategy like random selection and network exploration sampling. In the second
step, the sampled nodes and links are retrieved from the original network. The
sampled network is called a subgraph of the original network, if it consists
of sampled nodes or sampled links only. Otherwise, if sampled nodes and all
their mutual links are included in the sample or the sample consists of
sampled links and any additional links among their endpoints, the sampled
network is called an induced subgraph of the original network (i.e., subgraph
induction).

However, the previous studies have shown that the size $n^{\prime}$ of the
sample affects the performance of network sampling. Expectedly, larger samples
approximate the original networks more accurately [6]. Still, the sample size
of $10-30\%$ of the original network achieves the balance between small sample
and accurate approximation [4, 8, 6].

The changes of network properties introduced by sampling depend also on the
adopted sampling technique, since different techniques are suitable for
matching different sets of network properties. Thus, sampling techniques can
be roughly divided into two categories: random selection and network
exploration techniques. In the first category, nodes or links are included in
the sample uniformly at random or proportional to some particular
characteristic like degree. In the second category, the sample is constructed
by retrieving a neighborhood of a randomly selected seed node using different
strategies like breadth-first search, random walk and forest-fire.

###  2.1 Random selection

(a) RNS

(b) RND

(c) RLS

(d) RLI

Figure 1: Random selection techniques applied to a small toy network.
Highlighted nodes and links represent the samples obtained by different
techniques. 1(a) In random node selection, the sample consists of nodes
selected uniformly at random and all their mutual links. 1(b) In random node
selection by degree, the nodes are selected to the sample with probability
proportional to their degree, while all their mutual links are included in the
sample. 1(c) In random link selection, links are selected to the sample
uniformly at random. 1(d) In random link selection with subgraph induction,
the sample consists of randomly selected links (solid lines) and also any
additional links between their endpoints (dashed lines).

For the purpose of this study, we consider four techniques from the random
selection category. We first adopt random node selection [4] (RNS), where the
sample consists of nodes selected uniformly at random and all their mutual
links (Fig. 1(a)). RNS accurately approximates the degree mixing [5] and
preserves the relationship of transitivity and density between the original
and sampled networks [6]. Moreover, it shows better performance on larger
samples than on smaller [6]. Yet, RNS overestimates the degree and betweenness
centrality exponent and fails to match the clustering coefficient [5], degree
distribution [9] and the average path length [10] of the original network.

Furthermore, we adopt random node selection by degree [4] (RND), which
improves the performance of RNS. Here, the nodes are selected randomly with
probability proportional to their degrees and all their mutual links are
included in the sample (Fig. 1(b)). RND matches in-degree and out-degree
distributions and also spectral properties of the original network better than
RNS [4]. Besides, it constructs samples with larger weakly connected component
[6]. Nevertheless, despite a fully connected original network, both RNS and
RND can construct a disconnected sampled network.

Next, we adopt random link selection [4] (RLS), where the sample consists of
links selected uniformly at random (Fig. 1(c)). RLS matches well degree mixing
[5] and the distribution of sizes of weakly connected components [4]. It
constructs sparse samples and accurately approximates the average path length
of the original network [11]. Yet, RLS fails to match most of other network
properties [4]. Like RNS, RLS overestimates the degree and betweenness
centrality exponent and underestimates the clustering coefficient [5].

We last adopt random link selection with subgraph induction [7] (RLI), which
improves the performance of RLS. Here, the sample consists of links selected
uniformly at random and any additional links between their endpoints (Fig.
1(d)). RLI outperforms several other techniques in matching the degree, path
length and clustering coefficient distribution of the original networks [7].
It selects nodes with higher degree more likely than other random selection
techniques, which increases the connectivity of the sample. Moreover, RLI is
suitable for sampling large networks that can not fit into the main memory and
can also be implemented as a technique for sampling streaming networks [11].

###  2.2 Network exploration

(a) RWS

(b) RWI

(c) FFS

(d) FFI

Figure 2: Network exploration techniques applied to a small toy network.
Highlighted nodes and links represent the samples obtained by different
techniques. 2(a) In random walk sampling, the sample consists of links,
retrieved from a simulation of a random walker on the network starting at a
randomly selected seed node. 2(b) In random walk sampling with subgraph
induction, the sample consists of links selected with random walk sampling
(solid lines) and also any additional links between their endpoints (dashed
lines). 2(c) In forest-fire sampling, the broad neighborhood of a randomly
selected seed node is retrieved using partial breadth-first search, where only
a fraction of links is included in the sample on each step. 2(d) In forest-
fire sampling with subgraph induction, the sample consists of links selected
with forest-fire sampling (solid lines) and also any additional links between
their endpoints (dashed lines).

We consider four sampling techniques from the network exploration category
(note that in the literature, this category of sampling techniques is also
called topology based sampling [7], traversal based sampling [12] or link-
trace sampling [13]). First, we adopt random walk sampling [4] (RWS), where
the random walk is simulated on the network, starting at a randomly selected
seed node (Fig. 2(a)). The sample consists of links, which are visited by a
random walker and represents a connected subgraph of the original network. RWS
outperforms random selection techniques in matching the transitivity [13],
clustering coefficient distribution and spectral properties and also shows
good performance on smaller samples [4]. Yet, RWS is biased towards selecting
nodes with high degree [14] and fails to match the degree distribution [15].

Next, we adopt forest-fire sampling [4] (FFS). Here, a broad neighborhood of a
randomly selected seed node is retrieved from partial breadth-first search
(Fig. 2(c)). The number of links sampled on each step is selected from a
geometric distribution with mean $p/(1-p)$, where $p$ is set to $0.7$ [4].
Thus, on average $2.33$ links are included in the sample on each step. FFS
matches well spectral properties [4] and together with RWS shows the best
overall performance among several techniques [4]. However, FFS fails to match
the path length and clustering coefficient of the original networks [11].

Moreover, we apply subgraph induction step to random walk and forest-fire
sampling, which we term random walk sampling with subgraph induction (RWI) and
forest-fire sampling with subgraph induction (FFI). Here, the sample consists
of links, sampled with random walk (Fig. 2(b)) or forest-fire sampling (Fig.
2(d)), while any additional links among the endpoints of sampled links are
also included in the sample. To the best of our knowledge, RWI has not been
analysed in any of the previous studies. On the other hand, FFI shows worse
performance than RLI in matching the path length, degree and clustering
distributions [11]. Still, the performance of FFI has not yet been compared to
a larger set of sampling techniques.

##  3 Analysis and discussion

In the following sections, we present the adopted framework for comparison of
sampling techniques (Section 3.1), report the results of the empirical
analysis and formally discuss the findings (Section 3.2).

###  3.1 Statistical comparison

Sampling techniques are compared through four network properties (see Section
3.2) on ten real-world networks (see Table 1). For each property, we compute
externally studentized residuals of the properties of sampled networks that
measure the consistency of each sampling technique with the rest. We expose
statistically significant inconsistencies between the techniques with two-
tailed Student $t-$test [16] and calculate the residuals as introduced below
(see also [17]).

Let $x_{ij}$ denote some property value of $j-$th network for $i-$th sampling
technique. Externally studentized residual $\hat{x}_{ij}$ is calculated as

$\hat{x}_{ij}=\frac{x_{ij}-\hat{\mu}_{ij}}{\hat{\sigma}_{ij}\sqrt{1-\frac{1}{N}}},$
(1)

where $N$ is the number of sampling techniques ($N=8$ in our case),
$\hat{\mu}_{ij}$ is the mean value and $\hat{\sigma}_{ij}$ is the standard
deviation of the considered property. The mean value and standard deviation
are computed for all sampling techniques, excluding the observed one

$\hat{\mu}_{ij}=\sum_{k\neq i}{\frac{x_{kj}}{(N-1)}},$ (2)

$\hat{\sigma}_{ij}^{2}=\sum_{k\neq
i}{\frac{(x_{kj}-\hat{\mu}_{ij})^{2}}{(N-2)}}.$ (3)

Assuming that the errors in $x$ are independent and normally distributed, the
residuals $\hat{x}$ have Student $t-$distribution with $N-2$ degrees of
freedom. Statistically significant inconsistencies between the sampling
techniques are revealed by two-tailed Student $t-$test [16] at $P-$value of
$0.05$, rejecting the null hypothesis that the values of the considered
property are consistent across the sampling techniques.

###  3.2 Empirical analysis

The empirical analysis is performed on ten social and information networks.
Their main characteristics are presented in Table 1. Due to a large number of
networks considered, the detailed description is omitted. Networks are
considered to be undirected, although some of them are directed. For each
network, we perform $100$ realizations of each sampling technique, where we
consider sample sizes of $15\%$ of the original networks as suggested in [4,
6]. For each run of the network exploration techniques, the sample was
constructed from a new randomly selected seed node.

We analyze different properties of the original and sampled networks,
including degree distribution (probability distribution of degrees of all
nodes), distribution of clustering coefficient (probability distribution of
the proportions of connected neighbors of each node [18]), average degree
(average number of neighbors of nodes over the whole network), and density
(the ratio of existing links to all possible links). We compare sampling
techniques based on the match between the original and sampled networks;
Section 3.2.1 reports the results for degree and clustering distributions,
while in Section 3.2.2 we analyze the average degree and density.

Table 1: Real-world networks considered in the study. Network | Description | Nodes | Links | Average | Clustering | Density
---|---|---|---|---|---|---
degree | coefficient
ca-hep |  High E. Phys. collaboration [19] |  $12$,$008$ |  $237$,$010$ | $39.5$ | $0.660$ | $3.3\times 10^{-3}$
ca-astro |  Astro Phys. collaboration [19] |  $18$,$772$ |  $396$,$160$ | $42.2$ | $0.318$ | $2.2\times 10^{-3}$
cit-hep |  High E. Phys. citation [20] |  $27$,$240$ |  $342$,$437$ | $25.1$ | $0.120$ | $9.2\times 10^{-4}$
brightkite |  Brightkite friendship [21] |  $58$,$228$ |  $214$,$078$ | $\phantom{4}7.4$ | $0.111$ | $1.3\times 10^{-4}$
slashdot |  Slashdot friendship [22] |  $82$,$168$ |  $948$,$464$ | $23.1$ | $0.024$ | $2.8\times 10^{-4}$
flickr |  Flickr images metadata [23] |  $105$,$938$ |  $2$,$316$,$948$ | $43.7$ | $0.402$ | $4.1\times 10^{-4}$
ca-dblp |  DBLP collaboration [24] |  $317$,$080$ |  $1$,$049$,$866$ | $\phantom{4}6.6$ | $0.306$ | $2.1\times 10^{-5}$
nd.edu |  Web graph of nd.edu [25] |  $325$,$729$ |  $1$,$497$,$134$ | $\phantom{4}9.1$ | $0.097$ | $2.8\times 10^{-5}$
youtube |  Youtube friendship [26] |  $1$,$134$,$890$ |  $2$,$987$,$624$ | $\phantom{4}5.2$ | $0.006$ | $4.5\times 10^{-6}$
road-tx |  Texas road network [22] |  $1$,$379$,$917$ |  $1$,$921$,$660$ | $\phantom{4}5.5$ | $0.060$ | $3.9\times 10^{-6}$

####  3.2.1 Degree and clustering coefficient distributions

(a) Degree distribution

(b) Degree distribution

Figure 3: Statistical comparison of the degree distribution of the original
networks and their sampled variants obtained by different sampling techniques.
We show externally studentized residuals that measure the consistency of each
sampling technique with the rest and expose statistically significant
inconsistencies between the techniques with two tailed Student $t-$test at
$P-$value of $0.05$ (see 3(a); shaded region corresponds to $95\%$ confidence
intervals). Merely for complete understanding, we present average externally
studentized residuals over all networks (see 3(b); the star marker indicate
the residuals for the original networks, while error bars display standard
deviations). Notice that techniques with subgrah induction step (full markers)
approximate the degree distribution more accurately than the techniques
without subgraph induction (empty markers).

(a) Clustering coefficient distribution

(b) Clustering coefficient distribution

Figure 4: Statistical comparison of the clustering coefficient distribution of
the original networks and their sampled variants obtained by different
sampling techniques. We show externally studentized residuals that measure the
consistency of each sampling technique with the rest and expose statistically
significant inconsistencies between the techniques with two tailed Student
$t-$test at $P-$value of $0.05$ (see 4(a); shaded region corresponds to $95\%$
confidence intervals). Merely for complete understanding, we present average
externally studentized residuals over all networks (see 4(b); the star marker
indicate the residuals for the original networks, while error bars display
standard deviations). Notice that techniques with subgrah induction step (full
markers) improve the performance of the corresponding techniques without
subgraph induction (empty markers).

We first analyze the performance of sampling techniques based on the match of
the degree and clustering coefficient distributions between the original and
sampled networks. To compare the distributions of properties, we use
Kolmogorov-Smirnov $D-$statistics, which is commonly used in similar studies
[4, 11, 6]. Kolmogorov-Smirnov test checks the null hypothesis that the
distributions of property of the original network and its sampled variant are
the same, while the $D-$statistics measures the distance between the observed
distributions. Based on the values of Kolmogorov-Smirnov $D-$statistics, we
compare the adopted sampling techniques (see Section 3.1).

The comparison of sampling techniques based on the degree distribution is
shown in Fig. 3. We observe that in most cases, the difference between the
techniques with subgraph induction step (i.e., RNS, RND, RLI, RWI, FFI) and
those without subgraph induction (i.e., RLS, RWS, FFS) is clear. The first
group of techniques approximates the degree distribution of the original
networks more accurately. In addition, the techniques with subgraph induction
step improve the performance of the corresponding techniques without subgraph
induction in the case of RLS and FFS. Since the subgraph induction increases
the degrees of the nodes in the sample [7], this clearly contributes to better
match the degree distribution between the original and sampled networks.

RNS and RND show comparable accuracy to network exploration techniques with
subgraph induction, even though they construct disconnected samples. The
latter indicates that the connectivity of the sample does not affect the match
of the degree distribution. On the other hand, among the techniques without
subgraph induction, RWS shows the best performance, which could be explained
by its bias towards selecting high degree nodes and exploring densely
connected parts of the network [4]. In contrast, the samples constructed by
FFS are sparse, a large fraction of the nodes in the samples has low degree,
while the number of nodes with higher degree is underestimated [7].
Accordingly, FFS is the least accurate among all techniques.

Moreover, we compare the sampling techniques based on the clustering
coefficient distribution. The results are presented in Fig. 4. The techniques
show more comparable accuracy than in the case of the degree distribution. In
general, the techniques with subgraph induction underestimates the clustering
distribution, while others overestimate it. FFS and RWS prove as the best
performing techniques. This indicates that in the case of matching the
clustering distribution of the original networks, including all the links
among sampled nodes is not the best choice. However, RNS shows the worst
performance, which could be explained by its tendency to construct samples
with a large number of low clustered nodes [7].

In summary, the sampling techniques with subgraph induction step underestimate
the degree and clustering coefficient distributions of the original networks.
On the other hand, the techniques without subgraph induction, overestimates
both properties. The best overall performance is provided by RWI, which shows
the most stable accuracy for matching both distributions on all considered
networks. However, it appears that the properties of the original networks
have relevant effect on the accuracy of the sampling techniques. For example,
the techniques with subgraph induction match the degree distribution of the
networks with larger average degree more accurately than when the average
degree is lower and the techniques without subgraph induction also perform
well. We aim to study that kind of relations in future work, since they should
be observed in an even larger set of real-world networks to obtain
statistically significant results.

####  3.2.2 Average degree and density

In the second part of the analysis, we study the performance of sampling
techniques based on the match of the average degree and density between the
original and sampled networks. We compare the properties between networks
based on the actual values of properties. Furthermore, we modify Eq. (1) and
(3) and use the true value of considered property of the original networks
instead of the mean values $\hat{\mu}_{ij}$ (see Section 3.1) to compare the
sampling techniques.

Fig. 5 shows the comparison of sampling techniques based on the average
degree. We observe that the techniques with subgraph induction tend to
overestimate the average degree, while others underestimate it. Among the
techniques with induction step, only RNS underestimate the average degree. The
samples constructed with RNS consist of a large fraction of low-degree nodes
[7] and have a low average degree particularly in the smaller samples [5].
Therefore, RNS underestimates the average degree of the networks, especially
compared to the techniques, which are biased in selecting nodes with higher
degree (i.e., RND).

Lastly, we analyze the density of the networks. Fig. 6 shows the comparison of
the sampling techniques based on the density. In general, all techniques
overestimate the density, yet the techniques without subgraph induction step
approximate the density more accurately than the techniques with subgraph
induction. Additionally, techniques without subgraph induction improve the
performance of the corresponding techniques with subgraph induction. In the
previous study [27], we proved the power-law relationship between the size and
density of real-world networks and their sampled variants. The results show
that the network density decreases with its size. However, the techniques with
subgraph induction do follow the latter relationship, while the techniques
without induction construct sparser samples than expected. Therefore, the
accuracy of the sampling techniques based on the density is relative and
depends on whether the samples should accurately match the density of the
original networks or the relationship between the size and density of the
original networks and their sampled variants should be preserved.

In addition to all above, we analyze the performance of the techniques with
partial subgraph induction, where we include a different portion of mutual
links between the sampled nodes in the sample (i.e., we randomly select
$10\%-90\%$ links from all possible links). The results are not described,
since the techniques with partial subgraph induction did not improve the
performance of the techniques with subgraph induction and they did not perform
worse than techniques without subgraph induction.

In summary, the sampling techniques with subgraph induction step tend to
overestimate the average degree and the density of the original networks. In
contrast, the techniques without subgraph induction underestimate the average
degree and approximate the density of the original networks more accurately
than other techniques. Yet, the accuracy of sampling techniques does not
depend only on the characteristics of the adopted technique, but also on the
characteristics of the original networks. However, from exploration sampling
techniques, the best performance is provided by RWI, while the best overall
performance is provided by RNS, which shows very stable accuracy in matching
considered properties.

(a) Average degree

(b) Average degree

Figure 5: Statistical comparison of the average degree of the original
networks and their sampled variants obtained by different sampling techniques.
We show externally studentized residuals that measure the consistency of each
sampling technique with the rest and expose statistically significant
inconsistencies between the techniques with two tailed Student $t-$test at
$P-$value of $0.05$ (see 5(a); shaded region corresponds to $95\%$ confidence
intervals). Merely for complete understanding, we present average externally
studentized residuals over all networks (see 5(b); the star marker indicates
the residuals for the original networks, while error bars display standard
deviations). Notice that techniques with subgrah induction step (full markers)
tend to overestimate the average degree, while the techniques without subgraph
induction (empty markers) underestimate it.

(a) Density

(b) Density

Figure 6: Statistical comparison of the density of the original networks and
their sampled variants obtained by different sampling techniques. We show
externally studentized residuals that measure the consistency of each sampling
technique with the rest and expose statistically significant inconsistencies
between the techniques with two tailed Student $t-$test at $P-$value of $0.05$
(see 6(a); shaded region corresponds to $95\%$ confidence intervals). Merely
for complete understanding, we present average externally studentized
residuals over all networks (see 6(b); the star marker indicates the residuals
for the original networks, while error bars display standard deviations).
Notice that the techniques without subgraph induction step (empty markers)
approximate the density more accurately than the corresponding techniques with
subgraph induction (full markers).

##  4 Conclusion

In this paper, we analyze different real-world networks and study the changes
of their properties introduced by network sampling. We consider eight sampling
techniques with and without subgraph induction step and compare them based on
the match of properties between the original networks and their sampled
variants.

The results reveal that the techniques with subgraph induction underestimate
the degree and clustering distribution, while they overestimate the average
degree and density of the original networks. On the other hand, the techniques
without subgraph induction step exhibit opposite behavior, since they
overestimate the degree and clustering distribution and underestimate the
average degree and density. The techniques without subgraph induction
approximate the density most accurately. However, from the random selection
category, random node selection shows the best overall performance, while from
the network exploration category, the most stable accuracy is provided by
random walk sampling with subgraph induction. Still, it appears that the
performance of the sampling techniques from random selection category compared
to network exploration sampling does not differ significantly, while clear
differences exist between the techniques with subgraph induction step and the
ones without subgraph induction. Following this, a suitable classification of
network sampling techniques would be a class of techniques with subgraph
induction and a class without it.

Some of the results suggest that the characteristics of the original networks
affect the accuracy of the network sampling. Thus, our future work will mainly
focus on analyzing relations between network properties and accuracy of the
sampling techniques on a larger set of real-world networks. Besides, a
prominent direction for further study is the analysis of the time and space
efficiency of sampling techniques, since, for example, fitting even a sampled
network in the main memory becomes challenging with significant growth of
real-world networks in the past few years.

## Acknowledgment

This work has been supported in part by the Slovenian Research Agency ARRS
within the Research Program No. P2-0359, by the Slovenian Ministry of
Education, Science and Sport Grant No. 430-168/2013/91, and by the European
Union, European Social Fund.

## References

## References

  * [1] C. Hübler, H. P. Kriegel, K. Borgwardt, Z. Ghahramani, Metropolis algorithms for representative subgraph sampling, in: Proceedings of the 8th International Conference on Data Mining, IEEE, 2008, pp. 283–292.
  * [2] H. Sethu, X. Chu, A new algorithm for extracting a small representative subgraph from a very large graph, e-print arXiv:1207.4825.
  * [3] N. Blagus, L. Šubelj, G. Weiss, M. Bajec, Sampling promotes community structure in social and information networks, Physica A 432 (2015) 206–215.
  * [4] J. Leskovec, C. Faloutsos, Sampling from large graphs, in: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2006, pp. 631–636.
  * [5] S. H. Lee, P. J. Kim, H. Jeong, Statistical properties of sampled networks, Phys. Rev. E 73 (1) (2006) 016102.
  * [6] N. Blagus, L. Šubelj, M. Bajec, Assessing the effectiveness of real-world network simplification, Physica A 413 (2014) 134–146.
  * [7] N. Ahmed, J. Neville, R. R. Kompella, Network sampling via edge-based node selection with graph induction, Tech. rep., Purdue University (2011).
  * [8] C. Doerr, N. Blenn, Metric convergence in social network sampling, in: Proceedings of the 5th ACM workshop on HotPlanet, ACM, 2013, pp. 45–50.
  * [9] M. P. H. Stumpf, C. Wiuf, R. M. May, Subnets of scale-free networks are not scale-free: sampling properties of networks, P. Natl. Acad. Sci. USA 102 (12) (2005) 4221–4224.
  * [10] N. K. Ahmed, J. Neville, R. Kompella, Reconsidering the foundations of network sampling, in: Proceedings of the 2nd Workshop on Information in Networks, 2010\.
  * [11] N. K. Ahmed, J. Neville, R. Kompella, Network sampling: from static to streaming graphs, e-print arXiv:11211.3412.
  * [12] P. Hu, W. C. Lau, A survey and taxonomy of graph sampling, e-print arXiv:1308.5865.
  * [13] A. S. Maiya, T. Y. Berger-Wolf, Benefits of bias: towards better characterization of network sampling, in: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2011, pp. 105–113.
  * [14] L. Lovász, Random walks on graphs: A survey, Combinatorics: Paul Erdös is eighty 2 (1) (1993) 1–46.
  * [15] M. Kurant, A. Markopoulou, P. Thiran, On the bias of BFS, in: Proceedings of the 22nd International Teletraffic Congress, IEEE, 2010, pp. 1–8.
  * [16] R. D. Cook, S. Weisberg, Residuals and influence in regression, New York: Chapman and Hall, 1982.
  * [17] L. Šubelj, D. Fiala, M. Bajec, Network-based statistical comparison of citation topology of bibliographic databases, Sci. Rep. 4 (2014) 6496.
  * [18] D. J. Watts, S. H. Strogatz, Collective dynamics of ”small-world” networks, Nature 393 (6684) (1998) 440–442.
  * [19] J. Leskovec, J. Kleinberg, C. Faloutsos, Graph evolution: Densification and shrinking diameters, ACM Trans. Knowl. Discov. Data 1 (1) (2007) 1–40.
  * [20] KDD Cup ’03, http://www.cs.cornell.edu/projects/kddcup/ (2013).
  * [21] E. Cho, S. A. Myers, J. Leskovec, Friendship and mobility: user movement in location-based social networks, in: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2011, pp. 1082–1090.
  * [22] J. Leskovec, K. J. Lang, A. Dasgupta, M. W. Mahoney, Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters, Internet Math. 6 (1) (2009) 29–123.
  * [23] J. McAuley, J. Leskovec, Learning to discover social circles in ego networks, in: Advances in Neural Information Processing Systems 25, 2012, pp. 548–556.
  * [24] J. Yang, J. Leskovec, Community-affiliation graph model for overlapping network community detection, in: Proceedings of the 12th International Conference on Data Mining, IEEE, 2012, pp. 1170–1175.
  * [25] R. Albert, H. Jeong, A.-L. Barabási, Internet: Diameter of the world-wide web, Nature 401 (6749) (1999) 130–131.
  * [26] J. Yang, J. Leskovec, Defining and evaluating network communities based on ground-truth, Knowledge and Information Systems 42 (1) (2015) 181–213.
  * [27] N. Blagus, L. Šubelj, M. Bajec, Self-similar scaling of density in complex real-world networks, Physica A 391 (8) (2012) 2794–2802.


# Engaging Engineering Teams Through Moral Imagination: A Bottom-Up Approach
for Responsible Innovation and Ethical Culture Change in Technology Companies

Benjamin Lange Joint first authors Google Research  Geoff
Keeling††footnotemark:  Google Research  Amanda McCroskery Creators of the
program and joint second authors Google Research
Ben Zevenbergen22footnotemark: 2 Google Research  Sandra Blascovich
Contributors to the program and contributing authors Google Research  Kyle
Pedersen††footnotemark:  Google Research
Alison Lentz Executive supporters and contributing authors Google Research
and Blaise Agüera y Arcas††footnotemark:  Google Research

###### Abstract

We propose a ‘Moral Imagination’ methodology to facilitate a culture of
responsible innovation for engineering and product teams in technology
companies. Our approach has been operationalized over the past two years at
Google, where we have conducted over 50 workshops with teams from across the
organization. We argue that our approach is a crucial complement to existing
formal and informal initiatives for fostering a culture of ethical awareness,
deliberation, and decision-making in technology design such as company
principles, ethics and privacy review procedures, and compliance controls. We
characterize some distinctive benefits of our methodology for the technology
sector in particular.

{benlange, benzevenbergen<EMAIL_ADDRESS>
Keywords: Ethical Culture, Responsible Innovation, AI Ethics, Ethics in
Technology, Moral Imagination, Culture Change Management, Ethical Awareness,
Ethical Deliberation, Ethical-Decision-Making, Bottom-Up Methodology,
Practitioner Perspective

##  1 Introduction

The norms and values of technology teams shape which technologies are produced
and how.111Winner (1980), Idhe (1990), Feenberg (1990), see also Weinstein et
al. (2021). But these norms and values are rarely made explicit and subjected
to critical appraisal, leading to limited ethical reflection and potentially
reinforcing biases in technological development. In response, there are
growing calls to change the culture that shapes the production of
technologies. These include calls for greater governance such as government
regulation and industry self-regulation by internal ethics review
committees222Jackman and Kanerva (2015), Blackman (2022, p.151-57),
Shneiderman (2021, p.34), Prunkl et al. (2021). and governing
principles.333Whittlestone et al. (2019). Also included are calls for engineer
education in computer science curricula and industry training modules,444Grosz
et al. (2019), Fiesler et al. (2020), Garrett et al. (2020). alongside
technical best practice developments such as technical approaches to value
alignment555Amodei et al. (2016), Kenton et al. (2021), Gabriel (2020). and
algorithmic auditing.666Brown et al. (2021), Hasan et al. (2022).

These approaches, whilst critical for promoting socially and ethically
responsible technological development, have under-addressed a central cultural
dynamic of technology production, namely, the group forums where critical
decisions about product and research direction are made day-to-day. In the
technology industry in general, these decisions are often negotiated within
engineering, product, and research teams, where largely autonomous,
entrepreneurially-driven groups decide which problems are addressed through
technology and how, before elevating recommendations to managers and
executives. To be sure, the autonomy afforded to engineering teams is a key
aspect of how technology companies drive innovation. For example, in a recent
study on the determinants of innovation in a Swedish software company, Jim
Andersén and Torbjörn Ljungkvist found that ‘[a]lthough managers play a key
role in top-down oriented innovation processes, innovation is often achieved
by smaller groups at the operational level.’777Andersén and Ljungkvist (2021).
Indeed, this observation corroborates an earlier finding by Peerasit
Patanakul, Jiyao Chen, and Gary S. Lynn that ‘[r]elative to other team
structures, autonomous teams are more effective in addressing projects with
high technology novelty or radical innovation.’888Patanakul et al. (2012). For
further empirical treatments of the role of teams and interpersonal dynamics
in software engineering see Scott and Einstein (2001), Caldwell and O’Reilly
III (2003), Karn (2008), Glynn et al. (2010), Somech and Drach-Zahavy (2013),
Robbins and O’Gorman (2015), Gerrard and Lockett (2018), Hoffmann et al.
(2022).

In this paper, we describe a “Moral Imagination” methodology to drive a
culture of responsible innovation, ethical awareness, deliberation, decision-
making, and commitment in technology organizations.999Some terminological
clarifications. First, we use the term “engineer” to cover a wide spectrum of
roles involved in tech development, including but not limited to software
engineers, data scientists, research scientists, product managers, engineering
leads, UX researchers, UX designers, or managers. We use the term ‘engineering
teams’ inclusively. Second, throughout the discussion, we refer to the concept
of “ethical culture” understood, roughly, as “the shared values, beliefs,
norms, policies, procedures, systems, and artifacts that shape the behaviors
of members of an organization and support ethical conduct.” We consider
ethical culture as the encompassing construct within which initiatives such as
responsible innovation or value-sensitive design that specifically aim at
embedding the values or interests of broader stakeholders in technology
research and development can fall. This approach aims to prompt a role
obligation shift among teams that makes the consideration of the moral
implications of their work an inherent part of their self-conception and day-
to-day decision-making. As practitioners, we have developed and tested this
capability building approach over the past two years at Google through over 50
workshops involving a range of research and product teams.

Our primary aim is to make the conceptual case for our approach, and not to
present an empirical study on the efficacy of the proposed methodology, which
we are pursuing in other work. Neither do we intend to suggest that our
approach is superior compared to other initiatives such as traditional ethics
and compliance controls, review boards, or ethics committees. Rather, we see
Moral Imagination as an important complementary effort that can serve as one
part of a portfolio approach to responsible innovation at technology
companies.

Our discussion makes three contributions to existing scholarship on
responsible innovation and technology ethics. First, it highlights a neglected
gap in the current arsenal of instruments to manage responsible innovation at
technology companies - the shaping of norms within engineering culture.
Second, it spells out a concrete method for filling this gap, in a way that
builds on existing responsible innovation frameworks, while at the same time
tailoring the methodology to the distinctive cultural and organizational
features of technology companies. Third, it aims to detail concrete real-world
cases of application by illustrating the operationalization of this approach
based on our practitioner experience.

The paper is structured as follows. Section 2 details the specific challenge
that we are concerned with: equipping teams with the skills to responsibly
navigate the increasing social and ethical requirements in developing their
technology and products. We argue that a comprehensive culture of responsible
innovation at tech companies requires interventions that work to adjust tech
team norms. Against this backdrop, section 3 then develops our Moral
Imagination approach. We suggest that there are three key capabilities that
should be fulfilled in order to enable robust ethical culture change among
teams within technology companies: Ethical Awareness, Ethical Deliberation and
Decision-making, and Ethical Commitment. We then show how our framework
enhances these capabilities. Section 4 concludes.

##  2 The Need for Norm Shift

###  2.1 Overview

Our argumentative strategy in this section is to show that – given a plausible
assumption about the responsibility of technology companies in general – a
crucial lever for enabling responsible innovation is currently not fully
realized, and to then argue in the subsequent section that our approach can
fill this gap. More specifically, our argument consists of the following four
key claims:

  1. 1.

Tech’s Practical Responsibility Requirement: In light of the policy vacuum in
which technology is developed, technology companies have a practical
responsibility to consider how to produce technologies that are sensitive to
the ethical and sociotechnical contexts in which those technologies will be
deployed.

  2. 2.

Importance of Shaping Norms: Shaping engineers’ team culture and prevalent
norms is a key element in responding to Tech’s Practical Responsibility
Requirement.

  3. 3.

Gap in Current Measures: Typically employed hard and soft controls are
necessary but insufficient to fully shape predominant team norms in the design
and development stages.

  4. 4.

New Opportunity for Intervention: Therefore, there is an opportunity to devise
new interventions that can meet the specific requirements distinctive
engineering team culture poses and complement existing measures.

###  2.2 Elaboration of Argument

  1. 1.

Tech’s Practical Responsibility Requirement: In light of the policy vacuum in
which technology is developed, technology companies have a practical
responsibility to consider how to produce technologies that are sensitive to
the ethical and sociotechnical contexts in which those technologies will be
deployed.

We take this assumption as given. Our claim here draws on what has been called
the “pacing problem” for technology.

Figure 1: Pacing Problem in Technology

The pace of technological innovation exceeds that of regulation.101010Garrett
et al. (2020). As a result, existing laws and policies do not necessarily
provide guidance to technical companies about how to align their work with the
needs and interests of society – what James Moor (1985) has also referred to
as a “policy vacuum”.111111Moor (1985).

The pacing problem presents two ethical gray areas. That is, ambiguities about
how to proceed ethically when developing technology given that rules, laws,
and policies do not straightforwardly provide guidance. The first gray area
concerns the absence of appropriate regulation for new technology, in the
sense that legislation is not in place to set relevant boundaries for
technological systems. The second gray area concerns the interpretation of
existing laws. This means that when regulation does exist, it has likely been
designed to be appropriately broad to address foreseeable new technology. By
necessity, this level of generality requires that engineers need to be able to
interpret it meaningfully for the relevant context of their own work or that
its interpretation may be ambiguous in the context of novel and technological
developments.

Effectively navigating these ethical gray areas requires that tech companies
understand and address the sociotechnical context in which their products will
be deployed, in particular pre-empting various risks of harm that the
technology might pose to affected stakeholders (customers, local communities,
society at large, among others).

‘Responsible Research and Innovation’ as a concept, literature, and set of
processes can be seen as a response to this practical reality. Methods such as
anticipatory governance,121212Guston (2014). technology assessment,131313Schot
and Rip (1997). upstream stakeholder engagement,141414Wilsdon and Willis
(2004). and value sensitive design,151515Friedman (1996). have arisen with the
goal of embedding stakeholder values into the design of technology
accordingly.

  1. 2.

Importance of Shaping Norms: Shaping predominant engineers’ team culture norms
is a key element in responding to Tech’s Practical Responsibility Requirement.

We think that a key element in ensuring that engineers are adequately
sensitive to the ethical and sociotechnical contexts of their deployed
technology is changing the established informal rules and beliefs that govern
the behavior of engineering teams. There are several reasons for this, which
all concern the structural and organizational features of engineering teams,
the deeply pervasive norms of engineering culture, and the agile nature of
technology development.

Firstly, large technology companies like Google are, from an organizational
standpoint, best understood as networks of autonomous cross-functional teams,
rather than as single, aggregate entities that act in accordance with unified
sets of goals.161616See, for example, Birhane et al. (2022), Andersén and
Ljungkvist (2021), and Patanakul et al. (2012). These technology companies
typically have ‘distributed’ as opposed to ‘centralized’ organizational
structures that are organized around key research, products, and services.
This means that teams typically possess a great deal of autonomy, and they
often define, frame, and develop technology solutions before elevating
recommendations to executives or senior managers. This mode of operation
applies even in moments where technology companies undertake a concerted push
toward a general goal.

Secondly, there are deeply ingrained norms that govern engineering culture
itself. Values that relate to technical systems such as simplicity,
efficiency, scalability, and elegance tend to be the focus, without explicit
reference and acknowledgement of a wider set of ethical values that are also
embedded in the work. Of course, there are good reasons for the cultural
centrality of these values, including the ability to innovate rapidly and at
scale. Engineers tend to be familiar with productive critique, optimization,
and trade-offs for these types of values; however, the same know-how for
negotiating these value tensions does not apply straightforwardly to social
and ethical values. In practice, when individuals begin to discuss a wider set
of ethically significant considerations, we find that teams are less familiar
with ways to facilitate discussion about them in a productive, meaningful, and
actionable way. This is also not to deny that individual engineers care a
great deal about the social impact of their work; in fact we experience that
engineers often consider their work as part of a larger “life project of
creating good in the world.”171717Smith (2021). However, existing norms are
often not conducive to enabling the required awareness, information-seeking,
deliberation, and follow through on complex ethical issues.

Thirdly, in the early stages of technology development, there are often
multiple paths that teams can pursue. Plans are actively in development, and
concepts for technology research and products change rapidly, moving from a
state of ambiguity into concrete formation over a period of weeks or months.
In this state, the implicit beliefs, attitudes and social norms of teams exert
preeminent influence over what and how technologies are built. Here social
norms can determine how deliberative conversations proceed and engineers’
perceptions of which activities are necessary to shape a development process.

For these reasons, the ethical gray areas underwrite the need for norm change
in responding to tech’s practical responsibility requirement. Since engineers
inevitably develop technologies in domains where the application of existing
policies and laws is ambiguous or not applicable, and because they possess a
great deal of autonomy in designing technology, they must – as teams be able
to rely on well-developed norms of recognizing ethical issues, identifying
when more information is needed, being able to reason through the different
moral considerations at stake in a situation, and then practically acting on
these in a way that translates them into robust commitments.

  1. 3.

Gap in Current Measures: Currently available hard and soft controls are
necessary but insufficient to fully shape predominant team norms in the design
and development stages.

Technology companies typically have a large arsenal of mechanisms at its
disposal to support an ethical culture and drive responsible innovation within
the unique operating context of the technology sector.

Traditionally the ethical culture of companies can be distinguished along two
dimensions: formal and informal elements.181818See Kaptein (2011). The first,
so-called “hard controls”, refer to the concrete and explicit plans, policies,
and procedures within an organization. Of these formal systems, many attempt
to influence company culture through intervention at an individual level.
Ethics training programs are a good example of this, including the code of
conduct, whistle-blowing and speak-up training, privacy and data security
training, diversity, equity, and inclusion training, as well as training for
responsible corporate citizenship among others.191919See Treviño and Weaver
(2003). n addition to these measures, at Google there exist multiple review
boards that operate at the project level, assessing research and products
against Googles’ AI principles, security, and privacy standards, for
example.202020Google . The second element of ethical culture is informal.
These so-called “soft controls” include the implicit, intangible elements,
such as the values, expectations, beliefs, myths and assumptions that prevail
in the organization that are not explicitly formalized through policies and
processes. These informal elements also greatly matter in shaping ethical
culture since the implicit norms and beliefs are key drivers of ethical
conduct.

These ethics and compliance mechanisms, education and training programmes, and
review boards are central to fostering a culture of responsible innovation
within technology companies and our aim is not to criticize them or question
their importance. However, we think that such mechanisms leave a gap with
regards to the sustained promotion of a strong ethical culture in which
technologies are consistently created with appropriate ethical foresight.
While others have highlighted some212121Schiff et al. (2020). of the reasons
for this apparent “principles to practices gap,”222222Mittelstadt (2019). in
our experience, a main reason for this gap is that existing hard controls do
not sufficiently influence the organization at a technology team level.
Engineering team norms and culture can vary somewhat widely even within a
company, and these have substantial and direct impact over ethically
significant technological design decisions.

For example, while review boards, committees, and ethical commitments provide
vital guardrails and checks on a product or research against critical ethical
risks of harm, they mostly come into effect at later stages of development,
and thus fail to encourage explicit reflection on the ethical costs and
benefits of different design strategies at earlier stages of a product
development, including product ideation. Often, these stages require
foundational help in developing a mindset and vocabulary for ethical analysis
that enables teams to become aware of the concrete moral implications of their
work, how design choices relate to trade-offs between important values and can
have ethical implications for key stakeholders, how the team can then
deliberate through these in an ethically sound manner, and take concrete
action for the further development of their product. To do so would require
alignment with a collaborative and bottom-up approach where teams build
crucial ethical capabilities that are tailored to their specific issues and
requirements at a fine-grained level.

Ethics trainings that are offered at an individual level encounter a different
set of limitations for influencing ethically relevant design decisions amongst
technology teams. While individual education can influence individuals’
beliefs, this doesn’t guarantee that the individual can successfully convince
others of similar beliefs or to influence technology direction by a team.
These require team solutioning and commitment. Individuals whose moral
intuition has directed them to broach the topic of the ethical dimensions of
their work, are confronted with fears of analysis paralysis, lack of shared
understanding of vocabulary and concepts, lack of confidence in moving through
difficult conversations productively, and lack of understanding about how to
integrate the moral dimensions into concrete technical design decisions.
Indeed, often entrenched norms “keep opinions and behaviors in place even if
individuals no longer privately support them, a phenomenon known as
pluralistic ignorance.”232323Prentice and Paluck (2020).

  1. 4.

New Opportunity for Intervention: Therefore, there is an opportunity to devise
new interventions that complement existing measures and can meet the specific
requirements that distinctive engineering team culture poses.

What is consequently lacking is to influence which and how technologies are
built, and to complement existing initiatives, are measures that directly
address the culture and norms about how technology teams produce their work in
the context of their work. Formats must be flexible and able to adapt to the
nature of a team’s work, the various stages of their projects, and the
idiosyncrasies of particular team cultures given embedded personalities and
existing power dynamics. Addressing team norms directly in discussion about
their work yields the opportunity to weaken existing norms and replace them
with a new social contract that explicitly incorporates follow through on team
responsibilities in light of agreed-upon ethical commitments.

##  3 Moral Imagination

In this section we propose a ‘Moral Imagination’ methodology that aims to
promote a role-obligation shift among engineers by influencing the norms of
behavior, rules, best practices, and beliefs of engineering culture at a team
level. The methodology builds upon the Moral Imagination literature in
business ethics, alongside ideas from the philosophy of technology and the
responsible innovation literature.242424For some key frameworks in the
responsible innovation literature, see Owen et al. (2012), Stilgoe et al.
(2013), and Van Oudheusden (2014). See Werhane (2008) and Werhane (1999) on
moral imagination. The Fisher et al. (2006) “Midstream Modulation” approach is
similar in spirit to our developed method here. We first articulate what a
Moral Imagination approach amounts to and its function in the context of
technology companies (3.1). We then outline a framework that specifies three
key ethical capabilities around which our approach is structured (3.2). Last,
we propose a method for strengthening those capabilities based on our
practitioner-experience of conducting more than 40 workshops with teams at
Google (3.3).

###  3.1 Moral Imagination for Engineers

We define Moral Imagination as:

Moral Imagination: The ability to i) register that one’s perspective on a
decision-making situation, including the available options and the normative
factors relevant to adjudicating those options is limited; and to ii)
creatively imagine alternative perspectives that reveal new approaches to that
situation or new considerations that bear on the competing approaches.

Crucial to this is “becoming aware of one’s context, understanding the
conceptual scheme or “script” dominating that context, and envisioning
possible moral conflicts or dilemmas that might arise in that context or as
outcomes of the dominating scheme.”252525Werhane (2008, p.3) What developing
Moral Imagination allows engineers to do is recognize the limitations of their
pre-theoretic mental models about how their technology impacts the world, what
the costs and benefits of that technology are, and what their role is in
ensuring responsible technological development.

Thus a central aim of our approach is to facilitate a role obligation
shiftamong engineers. It aims to shift teams’ self-conception away from a
mindset where ethical considerations are removed from perceived
responsibilities – something that “falls outside of the job description” –
toward a mindset where the consideration of the moral implications is an
inherent part of the research and development process. It aims to prompt teams
to realize what they do not yet understand about how their technologies impact
users, and more broadly the sociotechnical dynamics and value tensions of
their technologies. It further aims to empower teams to create a map for
information gathering about the issues and topics the team didn’t consider
before.262626For a detailed exploration and description of the various
elements and content of the Moral Imagination workshop see Lange et al.
(2023).

###  3.2 Three Key Ethical Capabilities for Moral Imagination

Our approach focuses on three ethical capabilities that we consider central to
realizing Moral Imagination and to enhance these capabilities amongst teams to
foster meaningful and productive norm change.

What undergirds our focus on these capabilities is a conception of teams as
moral group agents who have the ability to reach informed moral judgements
through awareness and reasoning, act with intent, and to be held accountable
for their own actions.272727See Rest (1986). These focal points also relate to
our prior discussion insofar as the reality of autonomous technology teams
operating in ethical gray areas requires an enablement approach that builds
teams’ ability to navigate complex ethical challenges and translate this into
concrete actions and change along their product or research lifecycle.

Ethical Awareness: Ability to recognize normatively significant factors and
implications (e.g. moral values, ethical risks of harms, constraints and
rights violations) in situations, decisions, and other relevant choice
scenarios.

A precondition for robust ethical deliberation, decision-making, and
commitment is to expand the team’s perceptual paradigm beyond that of
established engineering norms, while also sensitizing the team to moral
discourse.282828Clarkeburn (2002). Developing an understanding of moral
values, their normative force, action-guidingness, appropriate definitions of
ethical terms for work-contexts, and how these relate to the technology and
products that a team is developing are all crucial elements of this
capability. In addition to shaping participants’ understanding of moral
values, ethical awareness also pertains to risks of harm to various
stakeholder groups, especially in a sociotechnical and not just technical
context.

Ethical Deliberation and Decision-Making: Ability to engage in reasoning and
deliberation in relevant choice scenarios, including tensions between value
and other moral commitments, conflicts, moral dilemmas, and trade-offs.

Once the team has a better understanding of the ethical dimensions of their
work, alongside a grasp of key ethical vocabulary, teams can be introduced to
conceptual tools which allow them to understand and negotiate situations in
which the competing normative considerations come into conflict. This may
encompass covering conceptual distinctions concerning ‘pro-tanto’ and ‘all-
things-considered’, the gradeability of normative concepts and values,
including different degrees to which conflicts can occur, the notion of
weighing different moral factors that may relate to a choice situation for a
team in a way that is ethically rigorous and robust, and the idea that which
moral factors are apparent or significant may vary based on perspective. This
point is relevant because the status quo of engineering culture is often
primarily consequentialist and can accordingly be broadened by being
introduced to different moral considerations besides outcome-oriented utility
calculations.

Ethical Commitment: Ability to derive and set concrete plans to guide further
product development and/or research.

Increased ethical awareness and decision-making capacities enables teams to
navigate complex ethical challenges as part of their work. But building these
capabilities will miss their mark if there is no commitment and accountability
with respect to translating these insights into practical change. To that end,
teams need to address what it means to act ethically and with integrity in
their product or research context. This may mean deviating from widely
accepted norms about the content, sequence, and pace of design, development,
and release activities. What it means to operationalize ethical commitment
varies depending on organizational structures. At Google, we co-develop with
teams a set of actionable responsibility objectives that can inform Product
Requirement Documents (PRD) and individual or team Objectives and Key Results
(OKRs).

###  3.3 Methodology

In the previous subsection we discussed the key ethical capabilities that the
Moral Imagination approach intends to influence to facilitate an ethical role
obligation shift among engineering teams. In this subsection, we detail a
practical four-step workshop method to strengthen these capabilities based on
our experience of conducting Moral Imagination workshops with teams at Google.

Our method expands upon existing responsible innovation frameworks such as
those developed by Owen et al. (2012) and Stilgoe et al. (2013), as well as
Fisher et al. (2006), by providing a tailored methodology for facilitating
responsible innovation for product and research teams that engage in software
development for at-scale technologies including artificial
intelligence.292929Here our aim is to sketch conceptually how the workshop
format fosters the relevant capabilities. We elaborate on the practical
details of the workshop in a separate practice-based piece of work.

For instance, while these approaches provide frameworks for facilitating
responsible innovation in broad strokes, our approach is specifically tailored
to the day-to-day realities of engineering teams in technology companies on
the ground. Similarly, we adapt Werhane’s (1999, 2008) notion of moral
imagination within this overall approach in a way that is tailored to the
specific needs of engineering teams.

Our approach is operationalized through a series of workshops that are
facilitated by a multidisciplinary team with academic backgrounds in ethics
and/or practical experience with ethics in the technology industry. The
workshop provides a structured engagement forum to assist teams typically at
early stages of their work, for example, during the ideation, experimentation,
prototyping, piloting, or re-imagining phases.303030The benefits of engagement
with innovation teams at an early to mid stage of their work has been outlined
in Fisher et al. (2006). Workshops are designed to draw attention toward the
salient dimensions of engineers’ work, and model and support how they can work
through them together while building a shared capacity for ethical awareness,
productive debate, solution finding, and planning. The workshops are
specifically adapted and tailored to a team’s progress and work: they are
modular and involve content that is customized for relevance to the dilemmas
teams face – though it is always centrally focused on the key ethical
capabilities of Awareness, Deliberation and Decision-Making, and Commitment
through Moral Imagination.

The workshops employ a non-didactic approach to ethics, in the sense that the
aim is not to lecture participants about key moral principles and
considerations. Nor is it to impose a particular ethical framework. Rather,
our approach is to construct exercises that enable engineers to reframe their
work through an ethical lens, and then re-envision their work and its
corresponding responsible development process. In doing so, our goal is to
align with the technology industry’s culture of autonomy and entrepreneurship,
while building momentum from many engineers’ expressed desire to drive their
innovations toward socially beneficial ends.

At a high level, the Moral Imagination workshops involve the following four-
step structure.

  1. 1.

(Reflection) Externalization of a team’s current moral intuitions, beliefs,
and convictions about their work.

Norms and beliefs about a team’s work have to be made explicit to be
challenged and altered. This first step therefore aims to surface and
understand the particular ethical paradigm with which a team is operating in
their day-to-day work by enabling teams to reflect on, articulate and clarify
the values they feel are currently motivating or inherent in their work. Semi-
structured discussions are used to surface the values that the team brings to
bear in their work, including personal motivations, beliefs about the
technological benefits, and envisioned characteristics of a world where the
technology has been successfully deployed and is ubiquitous, aiming to
formulate a positive vision for their technology. Building upon these
discussions, facilitators introduce the concept of values in ethics, groups
negotiate the most important values, and work to clarify and interpret them in
the particular context of their technology. Teams also reflect on whether and
in what respects current plans instantiate or fail to instantiate the stated
values, and surface tensions between values that require tradeoffs.

  1. 2.

(Expansion) Challenging a team’s perspective for the purpose of reflecting on
their moral intuitions. Envisioning possibilities for the acquisition of
ethically relevant information to inform a team’s approach, and for the work
itself.

Once teams’ moral intuitions and beliefs have been made more explicit among
the group, the next step is to challenge those intuitions and facilitate the
internalization of ethical considerations beyond those that were initially
surfaced. As part of building ethical awareness, the focus at this stage is to
challenge the teams’ paradigm from an ethical point of view to help the group
consider the key ethical implications that their work contains and also
surface relevant knowledge gaps.

The centerpiece of this section is a bespoke technomoral scenario that extends
the underlying logic of each team’s technology 5 or 10 years into the future.
The scenario complicates the interplay of technology and society, ends on a
cliffhanger, and emphasizes the importance of gaining different points of view
as a means to anticipate ethical considerations. Participants role-play in
small groups and argue the case against each other, putting to practice their
ability to interpret values, argue for or against them in technological
design, and build comfort with critical evaluation of their work. Throughout
this section, further value tensions are solicited, documented, and described.
An inclusion-focused exercise then aids participants in understanding the
needs and interests of multiple stakeholder groups and how to include their
voices to improve decision making. Participants are invited to a ‘veil of
ignorance’ scenario where they are encouraged to envision, articulate and
elaborate on the issues that might arise for the stakeholder
ecosystem.313131See Weidinger et al. (2023) for a recent discussion of using
Rawls’ veil of ignorance to align AI systems with principles of justice. This
exercise alerts participants to the possibility their team’s perspective is
limited, enumerates an initial set of perspectives from which the work would
benefit, and emphasizes diverse perspectives collected equitably must be a
high priority. Other exercises include anticipation of sociotechnical harm, in
which teams are exposed to a taxonomy of harm and brainstorm a number of
concrete adverse impacts their work could potentially have, alongside
alternative paths for the work in light of those possible impacts.

  1. 3.

(Evaluation) Reasoning through a number of ethical perspectives about the
team’s work.

Reflection and Expansion aim to build the ethical awareness of teams,
specifically with an eye towards enabling a better grasp of ethically relevant
factors including risks of harm. Once these have been surfaced and
internalized in the context of the teams’ own technologies, the next step
focuses on helping the team learn to deliberate and reason through concrete
ethical choice scenarios that are relevant to them. So, after a team’s ethical
paradigm has been made explicit and challenged by the team itself, ethical
reasoning tools are successively introduced to enable the team to learn to
reason through trade-offs and choice scenarios that they have identified as
arising in the context of their work.

During Moral Imagination workshops at Google, moral theories are introduced
schematically, and presented as a set of reasoning tools that enable
participants to approach a problem from different angles. Key notions such as
the “weighing” of competing moral values, trade-offs, and gradability of moral
commitments are introduced to teams. Exercises then involve participants
responding to arguments, formulating responses from multiple perspectives, and
discussing amongst each other to reach a consensus on how best to resolve a
particular value tension. The elements described aim to provide teams with a
shared foundation to have ethical conversations in a pluralistic manner that
goes beyond entirely deontological or consequentialist paradigms.

  1. 4.

(Action) Translation of insights and learnings into concrete team practices.

This last step focuses on supporting teams in taking actions based on their
learnings. Participants reflect on prior discussions and articulate ethical
focus areas that can inform the technology concept and design in future work.
Moderators work with participants during and after the workshop to shape these
focus areas into responsibility objectives, which serve as actionable
statements that can shape OKRs or be included in PRDs. The workshop’s focus on
discussion, clarification, and negotiation ensures that the responsibility
objectives are broadly supported and considered as legitimate North Stars for
the team.

Moral Imagination workshops enable participants to challenge beliefs and begin
to reshape the norms that guide decision-making and planning in their team.
Importantly, the norm change at issue here is shared and co-constructed.
Furthermore, in our experience, workshops render participants more aware of
the value of seeking accurate information about how their work will function
as a sociotechnical artifact, and also empower participants to interpret this
information in the context of research and development processes. To that end,
participants are able to proactively identify and mitigate ethical risks,
which supports them in making better use of other ethics controls such as
review boards, and in particular empowers teams with a degree of moral
autonomy when engaging with these other ethical controls. This holistic
approach, on which the Moral Imagination methodology complements more
traditional hard controls, ultimately enables engineering teams to develop
technologies in a way that is morally informed and which better meets the
challenges for technology companies that we articulated in Section 2.

##  4 Conclusion

In this paper, we introduced the Moral Imagination approach as a method for
driving ethical culture change within technology companies. The approach is a
“soft control” method that emphasizes externalization and multiperspectival
evaluation of the norms and values that precipitate innovation within teams
through semi-structured deliberation and negotiation, alongside co-development
of action-oriented ethical commitments (for example, through OKRs and PRDs).
The Moral Imagination approach has been executed over 40 times at Google, and
is positioned alongside “hard controls” such as ethics and privacy reviews
that together make up Google’s portfolio approach to fostering a culture of
responsible innovation in line with Google’s AI Principles.323232Pichai
(2018). We have argued that Moral Imagination is uniquely well-positioned to
complement and address the limitations of more traditional “hard controls” in
the context of technology companies, where team norms and values exhibit
substantial influence over research and product decisions given the bottom-up
and highly autonomous engineering culture that drives innovation within these
companies. Our hope is that the Moral Imagination approach can serve as a
template to foster a culture of responsible innovation across the industry.

While we are encouraged by the early results of the Moral Imagination
approach, we continue to refine the approach and develop new tools and
resources to scale the program within Google. This includes a dedicated
empirical research track focused on measuring the efficacy of the approach as
a method for ethical culture change, alongside the development of new workshop
modules that aim to further upskill teams on topics such as ethical reasoning,
critical reflection on metrics, and many other topics. Furthermore, we aim to
contribute to and enrich the social conversation around ethical culture change
within technology companies by publishing case studies alongside the findings
of our empirical research, and also by externalizing the methodology to
solicit participation and critical input from a broad range of stakeholders.

## References

  * Amodei et al. [2016] Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané.  Concrete problems in ai safety.  _arXiv preprint arXiv:1606.06565_ , 2016.
  * Andersén and Ljungkvist [2021] Jim Andersén and Torbjörn Ljungkvist.  Resource orchestration for team-based innovation: a case study of the interplay between teams, customers, and top management.  _R &D Management_, 51(1):147–160, 2021.
  * Birhane et al. [2022] Abeba Birhane, Pratyusha Kalluri, Dallas Card, William Agnew, Ravit Dotan, and Michelle Bao.  The values encoded in machine learning research.  In _2022 ACM Conference on Fairness, Accountability, and Transparency_ , pages 173–184, 2022.
  * Blackman [2022] Reid Blackman.  _Ethical Machines_.  Harvard Business Review Press, 2022.
  * Brown et al. [2021] Shea Brown, Jovana Davidovic, and Ali Hasan.  The algorithm audit: Scoring the algorithms that score us.  _Big Data & Society_, 8(1):2053951720983865, 2021.
  * Caldwell and O’Reilly III [2003] David F Caldwell and Charles A O’Reilly III.  The determinants of team-based innovation in organizations: The role of social influence.  _Small group research_ , 34(4):497–517, 2003\.
  * Clarkeburn [2002] Henriikka Clarkeburn.  A test for ethical sensitivity in science.  _Journal of Moral education_ , 31(4):439–453, 2002.
  * Feenberg [1990] Andrew Feenberg.  The critical theory of technology.  _Capitalism Nature Socialism_ , 1(5):17–45, 1990\.
  * Fiesler et al. [2020] Casey Fiesler, Natalie Garrett, and Nathan Beard.  What do we teach when we teach tech ethics? a syllabi analysis.  In _Proceedings of the 51st ACM technical symposium on computer science education_ , pages 289–295, 2020.
  * Fisher et al. [2006] Erik Fisher, Roop L Mahajan, and Carl Mitcham.  Midstream modulation of technology: governance from within.  _Bulletin of Science, Technology & Society_, 26(6):485–496, 2006.
  * Friedman [1996] Batya Friedman.  Value-sensitive design.  _interactions_ , 3(6):16–23, 1996.
  * Gabriel [2020] Iason Gabriel.  Artificial intelligence, values, and alignment.  _Minds and machines_ , 30(3):411–437, 2020.
  * Garrett et al. [2020] Natalie Garrett, Nathan Beard, and Casey Fiesler.  More than” if time allows” the role of ethics in ai education.  In _Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society_ , pages 272–278, 2020.
  * Gerrard and Lockett [2018] Bill Gerrard and Andy Lockett.  Team-specific human capital and performance.  _British Journal of Management_ , 29(1):10–25, 2018.
  * Glynn et al. [2010] Mary Ann Glynn, Robert Kazanjian, and Robert Drazin.  Fostering innovation in complex product development settings: The role of team member identity and interteam interdependence.  _Journal of Product Innovation Management_ , 27(7):1082–1095, 2010.
  * [16] Google.  2022 ai principles progress update.
  * Grosz et al. [2019] Barbara J Grosz, David Gray Grant, Kate Vredenburgh, Jeff Behrends, Lily Hu, Alison Simmons, and Jim Waldo.  Embedded ethics: integrating ethics across cs education.  _Communications of the ACM_ , 62(8):54–61, 2019\.
  * Guston [2014] David H Guston.  Understanding ‘anticipatory governance’.  _Social studies of science_ , 44(2):218–242, 2014\.
  * Hasan et al. [2022] Ali Hasan, Shea Brown, Jovana Davidovic, Benjamin Lange, and Mitt Regan.  Algorithmic bias and risk assessments: Lessons from practice.  _Digital Society_ , 1(2):14, 2022.
  * Hoffmann et al. [2022] Marco Hoffmann, Daniel Mendez, Fabian Fagerholm, and Anton Luckhardt.  The human side of software engineering teams: an investigation of contemporary challenges.  _IEEE Transactions on Software Engineering_ , 49(1):211–225, 2022.
  * Idhe [1990] Don Idhe.  _Technology And The Lifeworld: From Garden To Earth_.  Indiana University Press, 1990.
  * Jackman and Kanerva [2015] Molly Jackman and Lauri Kanerva.  Evolving the irb: building robust review for industry research.  _Wash. & Lee L. Rev. Online_, 72:442, 2015.
  * Kaptein [2011] Muel Kaptein.  Understanding unethical behavior by unraveling ethical culture.  _Human relations_ , 64(6):843–869, 2011.
  * Karn [2008] John Karn.  An ethnographic study of conflict in software engineering teams.  _Journal of Information, Information Technology & Organizations_, 3, 2008.
  * Kenton et al. [2021] Zachary Kenton, Tom Everitt, Laura Weidinger, Iason Gabriel, Vladimir Mikulik, and Geoffrey Irving.  Alignment of language agents.  _arXiv preprint arXiv:2103.14659_ , 2021.
  * Lange et al. [2023] Ben Lange, Geoff Keeling, Amanda McCroskery, Ben Zevenbergen, and Sandra Blascovich.  Moral imagination workshop methodology explained.  _Unpublished Manuscript_ , 2023.
  * Mittelstadt [2019] Brent Mittelstadt.  Principles alone cannot guarantee ethical ai.  _Nature machine intelligence_ , 1(11):501–507, 2019.
  * Moor [1985] James H Moor.  What is computer ethics?  _Metaphilosophy_ , 16(4):266–275, 1985.
  * Owen et al. [2012] Richard Owen, Phil Macnaghten, and Jack Stilgoe.  Responsible research and innovation: From science in society to science for society, with society.  _Science and public policy_ , 39(6):751–760, 2012\.
  * Patanakul et al. [2012] Peerasit Patanakul, Jiyao Chen, and Gary S Lynn.  Autonomous teams and new product development.  _Journal of Product Innovation Management_ , 29(5):734–750, 2012.
  * Pichai [2018] Sundar Pichai.  Ai at google: our principles.  _The Keyword_ , 7:1–3, 2018.
  * Prentice and Paluck [2020] Deborah Prentice and Elizabeth Levy Paluck.  Engineering social change using social norms: Lessons from the study of collective action.  _Current Opinion in Psychology_ , 35:138–142, 2020.
  * Prunkl et al. [2021] Carina EA Prunkl, Carolyn Ashurst, Markus Anderljung, Helena Webb, Jan Leike, and Allan Dafoe.  Institutionalizing ethics in ai through broader impact requirements.  _Nature Machine Intelligence_ , 3(2):104–110, 2021.
  * Rest [1986] James R Rest.  Moral development: Advances in research and theory.  1986\.
  * Robbins and O’Gorman [2015] Peter Robbins and Colm O’Gorman.  Innovating the innovation process: an organisational experiment in global pharma pursuing radical innovation.  _R &d Management_, 45(1):76–93, 2015.
  * Schiff et al. [2020] Daniel Schiff, Bogdana Rakova, Aladdin Ayesh, Anat Fanti, and Michael Lennon.  Principles to practices for responsible ai: Closing the gap.  _arXiv preprint arXiv:2006.04707_ , 2020.
  * Schot and Rip [1997] Johan Schot and Arie Rip.  The past and future of constructive technology assessment.  _Technological forecasting and social change_ , 54(2-3):251–268, 1997.
  * Scott and Einstein [2001] Susanne G Scott and Walter O Einstein.  Strategic performance appraisal in team-based organizations: One size does not fit all.  _Academy of Management Perspectives_ , 15(2):107–116, 2001.
  * Shneiderman [2021] Ben Shneiderman.  Responsible ai: bridging from ethics to practice.  _Communications of the ACM_ , 64(8):32–35, 2021\.
  * Smith [2021] Jessica M Smith.  _Extracting accountability: Engineers and corporate social responsibility_.  Mit Press, 2021.
  * Somech and Drach-Zahavy [2013] Anit Somech and Anat Drach-Zahavy.  Translating team creativity to innovation implementation: The role of team composition and climate for innovation.  _Journal of management_ , 39(3):684–708, 2013\.
  * Stilgoe et al. [2013] Jack Stilgoe, Richard Owen, and Phil Macnaghten.  Developing a framework for responsible innovation.  _Research policy_ , 42(9):1568–1580, 2013.
  * Treviño and Weaver [2003] Linda Klebe Treviño and Gary Richard Weaver.  _Managing ethics in business organizations: Social scientific perspectives_.  Stanford University Press, 2003.
  * Van Oudheusden [2014] Michiel Van Oudheusden.  Where are the politics in responsible innovation? european governance, technology assessments, and beyond.  _Journal of Responsible Innovation_ , 1(1):67–86, 2014.
  * Weidinger et al. [2023] Laura Weidinger, Kevin R McKee, Richard Everett, Saffron Huang, Tina O Zhu, Martin J Chadwick, Christopher Summerfield, and Iason Gabriel.  Using the veil of ignorance to align ai systems with principles of justice.  _Proceedings of the National Academy of Sciences_ , 120(18):e2213709120, 2023.
  * Weinstein et al. [2021] Jeremy Weinstein, Rob Reich, and Mehran Sahami.  _System error: Where big tech went wrong and how we can reboot_.  Hachette UK, 2021.
  * Werhane [2008] Patricia H Werhane.  A note on moral imagination.  2008\.
  * Werhane [1999] Patricia Hogue Werhane.  _Moral imagination and management decision-making_.  Oxford University Press, USA, 1999.
  * Whittlestone et al. [2019] Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave.  The role and limits of principles in ai ethics: towards a focus on tensions.  In _Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society_ , pages 195–200, 2019.
  * Wilsdon and Willis [2004] James Wilsdon and Rebecca Willis.  _See-through science: Why public engagement needs to move upstream_.  Demos, 2004.
  * Winner [1980] Langdon Winner.  Do artifacts have politics?  _Daedalus_ , pages 121–136, 1980.