# The Managerial Effects
of Algorithmic Fairness Activism

Bo Cowgill
Columbia Business School, Columbia University
<EMAIL_ADDRESS>&Fabrizio Dell’Acqua
Columbia Business School, Columbia University
<EMAIL_ADDRESS>&Sandra Matz
Columbia Business School, Columbia University
<EMAIL_ADDRESS>
###### Abstract

How do ethical arguments affect AI adoption in business? We randomly expose
business decision-makers to arguments used in AI fairness activism. Arguments
emphasizing the inescapability of algorithmic bias lead managers to abandon AI
for manual review by humans and report greater expectations about lawsuits and
negative PR. These effects persist even when AI lowers gender and racial
disparities and when engineering investments to address AI fairness are
feasible. Emphasis on status quo comparisons yields opposite effects. We also
measure the effects of "scientific veneer" in AI ethics arguments. Scientific
veneer changes managerial behavior but does not asymmetrically benefit
favorable (versus critical) AI activism.

_Our submission is a 4-page summary to comply with the workshop page limits.
Our full paper can be viewed
athttps://www.aeaweb.org/articles?id=10.1257/pandp.20201035._

##  1 Introduction

In the last five years, the ethical and distributional consequences of
algorithmic decision-making have become a rising concern. Interest in
“algorithmic bias,” measured by Google Trends, has increased more than ten-
fold during this time. Bias in algorithms is now the topic of major regulatory
efforts, new lines of academic research, and even political debates. How has
this surge of attention to fairness considerations affected the adoption of
AI?

In this paper, we assess this question by surveying business decision-
makers.111Related research surveys ML practitioners and decision-makers, see
for example, Chowdhury (2020); Wallach (2019). While these conduct semi-
structured interviews, our studies experimentally manipulate the arguments in
favor or against AI that the practitioners read. Our surveys include
experimental manipulations of arguments about algorithmic fairness. We
randomly expose business decision-makers to arguments used in AI fairness
activism. We then measure the effects on AI adoption recommendations and
supporting beliefs. In particular, we assess two broad sets of interventions.

We first examine the effect of arguments about the origin of algorithmic bias
and its relationship to the status quo. In our experiment, subjects are
randomly assigned to read an op-ed. One op-ed emphasizes that algorithmic bias
is unavoidable. We refer to this as our “AI fatalism” condition. The second
op-ed claims that while algorithmic bias exists, algorithmic decision-making
might be preferable to human-based alternatives which could possibly contain
higher levels of bias. We refer to this as the “counterfactual” condition.

In our second study, we measure the effect of adding “scientific veneer” to
arguments about AI ethics using a 2$\times$2 design. Subjects are randomly
assigned to read arguments highlighting either the favorable or unfavorable
ethical assessments of AI systems. These arguments are presented either with
scientific veneer or not (randomly assigned). We constructed scientific veneer
treatments to attach symbols of scientific authority to arguments, but without
introducing new substantive content to the claims. Our experimental materials
can be seen in Appendix E.

Both studies measure adoption of AI in settings motivated by Cowgill (2019)
(hiring) Bartlett et al. (2019) (lending). We assess not only adoption
decisions, but also a series of potential mechanisms behind these decisions
(specifically beliefs about the probability of fairness PR or legal
allegations, and the magnitude of these problems conditional on arising). We
also measure beliefs about the “fixability” of algorithmic unfairness problems
in response to focused engineering efforts. Finally, we examine how
information about status quo processes affects choices about AI.

Main Findings: In our first study, we find that our “fatalism”’ op-ed
discouraged AI adoption, while the “counterfactual’ encourages it (compared to
a baseline of no op-ed). This effect seems to be driven by differences in the
perceived likelihood of being caught in a lawsuit or PR problem. The perceived
magnitude of such a problem – in addition to the probability of one existing
at all – is also substantially higher under the “fatalism” condition (than
under the“counterfactual’ condition) but the effect is larger on the
probability of detection.

Regarding the fixability of the algorithmic biases, our business decision-
makers believe fairness problems are more correctable under our “fatalism”
conditions. Lastly, we find that “fatalism” arguments discourage
counterfactual thinking in our subjects. When given the option, subjects are
less likely to investigate comparisons between adopting AI and the status quo,
to make decisions (mostly rejecting AI) without examining alternatives.

In our second study, we find some evidence (albeit limited) that “scientific
veneer” enhances arguments promoting the ethical properties of AI. However, we
find evidence that scientific veneer amplifies arguments _against_ the ethical
properties of the AI. This is particularly true with regards to beliefs about
the fixability of AI ethics issues.

Math-based arguments were found to have a heterogeneous effect on
counterfactual thinking. Adding mathematical veneer to positive arguments
caused subjects to be more likely to investigate the status quo. However,
adding math veneer to negative arguments _lowered_ levels of counterfactual
thinking, comparing and conditioning on the status quo.

This article contributes to a broader literature on adoption and effects of
algorithmic decision-making in economics (Cowgill and Tucker, 2019) and
psychology (Dietvorst et al., 2015), as well as on ethical AI considerations
by firms(Chowdhury, 2020; Wallach, 2019, 2020) Our results have mixed
implications for strategies for making business decision-makers mitigate bias
in AI. The remainder of this paper proceeds as follows. In Section 2 we
describe the design of our two studies. In Section 3 we review results. In our
conclusion (4), we summarize and discuss the implications of our findings.

##  2 Experimental Design

Our survey was conducted on January 8, 2020, using Prolific
Academic.222Evaluated in Peer et al. (2017) We focused our study on subjects
likely to make AI adoption decisions for organizations. We, therefore, limited
participation to U.S. adults with management experience. Table 1 reports
descriptive statistics for our sample.

Both our studies took the following format. Each subject reads two short
business case studies. Each case puts the subject in the role of a manager
making an AI adoption decision. The first case is about using AI for hiring at
a technology company, and the second is about using AI for lending decisions
by financial institutions. Each case concludes by disclosing an imbalance in
the algorithm’s behavior along gender (Study 1) or racial (Study 2) lines. The
level of imbalances are taken from Cowgill (2019) (hiring) (Bartlett et al.,
2019) (lending). The text of both cases is in Appendix C; we describe key
features below.

After each case, subjects are exposed to information about the level of
imbalances for the status quo process; i.e., what happens if the AI is
rejected. In both of these papers, imbalances appear in both algorithmic and
status-quo processes, but are _lower_ in the algorithmic condition. A random
third of subjects are shown this status quo data. For a second third, the
status quo information is available only if the subjects are willing to click
to view it (rather than proceeding to questions without viewing it). For the
final random third, the status quo is not mentioned at all.

Subjects then proceed to answer questions about the case. Each subject answers
five adoption-related questions. The first asks how positively subjects
consider the impact of this technology. The second and third ask for a
recommendation to adopt or maintain the status quo. The fourth asks how likely
lawsuits or PR problems are if the algorithm is adopted. The fifth asks about
the magnitude of lawsuits or PR problems, conditional on them existing. After
these five questions, the subject is told to imagine that the company in the
case has dedicated six months of additional engineering effort to address the
imbalances at the end of the case. Subjects are then asked to re-answer the
five questions, incorporating beliefs about how the algorithm would change
after six months of dedicated programming effort.

All subjects go through the above procedure twice, once for hiring and once
for lending. For all subjects, the second case study (in randomized order)
features an additional randomized treatment: a _persuasive argument_ about
algorithmic fairness. We use this treatment to study how various types of
arguments change subjects’ adoption decisions (and their beliefs surrounding
these decisions). We describe these persuasive interventions below. Both
studies were allocated $\approx$500 subjects.

Study 1: “Counterfactual and Fatalistic Perspectives”: In our first study,
subjects are randomly assigned into three conditions to read op-eds in between
the first and second business case. A recent media
article333https://www.axios.com/ai-
bias-c7bf3397-a870-4152-9395-83b6bf1e6a67.html, accessed Jan 9, 2020.
described a split in perspectives around AI ethics (“A tug-of-war over biased
AI”). We chose an op-ed reflective of each perspective, and randomly assigned
one third of subjects to read it between the two cases. The final third read
no op-ed.

The first op-ed conveys the perspective that AI cannot be fully cleansed of
bias and thus should be avoided altogether. We refer to this as the
“fatalistic” perspective on AI bias. In this condition, subjects read “A.I.
Bias Isn’t the Problem. Our Society Is,” an essay published in Fortune
magazine.444https://fortune.com/2019/04/14/ai-artificial-intelligence-bias/,
accessed Jan 9, 2020. The second op-ed reflected the view that AI may exhibit
biases, but can still be useful if the biases are reduced below the levels of
counterfactual human decision-making. We refer to this as the “counterfactual”
perspective as it often emphasizes comparisons against the status quo. In this
condition, subjects read “Want Less-Biased Decisions? Use Algorithms,” an
essay published in Harvard Business Review.555https://hbr.org/2018/07/want-
less-biased-decisions-use-algorithms, accessed Jan 9, 2020.

Study 2: “Scientific Veneer”: In our second study, we examine a related set of
hypotheses about persuasion in AI. This perspective appears in many critical
outlets, but was succinctly articulated by U.S. Senator Kamala Harris, who
wrote that AI could “hide bias behind a scientific veneer of objectivity.”

These critics alleged that AI advocates benefit from the use of statistics and
engineering in their application and persuasion. They also allege that the
biographies of AI engineers give them scientific authority, even though
“Algorithms are opinions embedded in code,” according to one
commentator.666See _Weapons of Math Destruction_ by Cathy O’Neil. However, the
persuasion of AI critics also exhibits mathematical arguments and credentials.
Leading critics include computer science professors, statisticians,
engineering executives, and math PhDs. These features may similarly lend a
veneer of objectivity to critical arguments about AI ethics.

We study the effects of scientific veneer on both negative and positive
arguments regarding the ethics of AI. In Study 2, subjects read a report from
an expert between the two cases. The report appears after the second business
case before the subject examines the status quo (if available) and before
questions about adoption. The content of the report is randomized to present
either negative or positive arguments about the ethics or bias of the AI
application. In addition, the report was randomized to include scientific
veneer or not. The text of the “veneer” report is identical to the non-veneer
one, but also repeats the verbal statements using mathematical notation.777Our
“veneer” interventions were carefully constructed for redundancy; i.e., _not_
to include any new arguments. Examples appear in Appendix E.

Our veneer treatments also differ in the expert’s identity and the typesetting
of the report. Both experts were U.C. Berkeley PhDs employed to evaluate
algorithms. The “veneer” expert was a physics Ph.D., and the “non-veneer”
expert was a sociology Ph.D. The physicist’s report was created in LaTeX(a
typesetting program popular in mathematics, natural sciences, and
engineering). The sociologist’s report was prepared in Microsoft Word using
Arial font.

##  3 Results

We summarize results verbally below, but Appendix B provides the
specifications in our results tables (Appendix A) and some guidance on how to
interpret the coefficients. Because each subject answered all questions twice,
we employ subject fixed effects and panel methods. Note that positive
coefficients refer to favorable evaluations of the algorithm, except for the
“lawsuit” and “damages” column. All outcome variables are standardized.

We report our results from Study 1 (“Counterfactual” and “Fatalism” op-eds) in
Table 4. Reading the counterfactual op-ed gives a more positive perception of
the algorithm and encourages the adoption of AI. It increases the perceived
positive impact of the technology by 0.24 standard deviations and increases
recommendations for adoption by 0.19 standard deviations.

By contrast, reading the fatalistic op-ed leads to a more negative perception
of the algorithm (-0.16 standard deviations), and makes adoption less likely,
in particular when measured on a scale (-0.28 standard deviations). A relevant
mechanism for these results seems to be connected to PR and potential
lawsuits. Business decision-makers who read the counterfactual op-ed believe
fairness lawsuits and PR problems are less probable (-0.12 standard
deviations). At the same time, they also think that, if a problem was
assessed, it would be less damaging (-0.14). Instead, subjects who read the
fatalistic op-ed consider lawsuits more likely and at the same time more
damaging, respectively by 0.19 and 0.061 of a standard deviation.

When we examine fixability outcomes, we observe that the counterfactual op-ed
makes business decision-makers think that fairness problems have less room to
be fixed compared to the no op-ed case. This may partly be the result of
having set initial expectations relatively high in the counterfactual
condition. The effects of the fatalism op-ed about fixability are mixed; in
some specifications, they are positive but not statistically significant. Even
after accounting for potential fixability effects, the net effect of the
fatalism op-ed on recommendation is lower than if the subject read no op-ed at
all.

As mentioned above, subjects exposed to the status quo rated AI as more
positive, and consider lawsuits less likely and less damaging. In Panel B of
Table 4, we focus on subjects who are given the _option_ to evaluate the
status quo in comparison with the algorithm in the case study for a small
price (an additional click). Using an IV specification, we observe that
decision-makers who read the fatalistic op-ed were less likely to condition on
them. Some of our specifications suggest that subjects who read the fatalism
op-ed are less interested in finding out about the status quo at all. By
contrast, subjects in the counterfactual condition appeared slightly more
likely to explore the status quo and to condition choices on them.

In Table 5 we present the results of the second study of “scientific veneer.”
We find strong evidence that scientific veneer affects the decisions to adopt
AI. After reading the same opinion with the addition of math symbols and
jargon, decision-makers recommended algorithm’s adoption less by 0.21 standard
deviations if this opinion is against AI. Instead, they recommended the AI
more by 0.27 standard deviations when the opinion is in favor of AI.

Across specifications and outcome variables, the magnitude of the effects of
scientific veneer is approximately equal, irrespective of whether the veneer
is applied towards favorable or unfavorable opinions in algorithms. Panel B of
table 5 reports the results on counterfactual thinking. When decision-makers
are exposed to positive arguments with scientific veneer, they are more likely
to be interested in the status quo. They show less interest in the status quo
when shown negative arguments with scientific veneer.

##  4 Conclusion

A key question for activists and policymakers is: What is the goal of AI
fairness activism? If the goal is for organizations to abandon AI – which some
critics advocate – the “fatalistic” perspective is more effective. We show
that the abandonment effect persists even in settings like (Bartlett et al.,
2019) and Cowgill (2019) where algorithms reduce racial- and gender-
disparities. The abandonment effect is also robust to the possibility of
dedicated engineering improvements.

If the goal is to encourage adoption when AI reduces disparities, and
improving algorithmic bias when it doesn’t – we find that counterfactual
arguments are more effective. This may be the best activism strategy in
settings where human biases exist, but machine biases could feasibly reduce
them. Additionally, our results can inform firms about how to incorporate AI
ethics more effectively in their operations.888Chaintreau (2020) shows simple
reminders about algorithmic bias are among the most effective ways to
encourage engineers to improve fairness in a hiring algorithm for a firm.
Other common methods within companies include AI ethics checklists, discussed
in Wallach (2020)..

Our paper highlights the perspective of firms, for whom AI adoption features
both benefits as well as costs. Our study finds that AI abandonment is
correlated with fear of lawsuits and negative PR. In our setting, this results
in decisions made by human review, which other researchers have shown to
exhibit bias. Because human judgments are harder to audit, this shift may
reduce lawsuit risk rather than reducing disparities.

## References

  * R. Bartlett, A. Morse, R. Stanton, and N. Wallace (2019) Consumer-lending discrimination in the fintech era.  Technical report National Bureau of Economic Research.  Cited by: §1, §2, §4.
  * A. Chaintreau (2020) Biased programmers? or biased data? a field experiment in operationalizing ai ethics.  Proceedings of the 21st ACM Conference on Economics and Computation,  pp. 679–681.  Cited by: footnote 8.
  * R. Chowdhury (2020) Where responsible ai meets reality: practitioner perspectives on enablers for shifting organizational practices.  arXiv preprint arXiv:2006.12358.  Cited by: §1, footnote 1.
  * B. Cowgill and C. E. Tucker (2019) Economics, fairness and algorithmic bias.  preparation for: Journal of Economic Perspectives.  Cited by: §1.
  * B. Cowgill (2019) Bias and productivity in humans and algorithms: theory and evidence from resume screening.  Columbia Business School, Columbia University 29.  Cited by: §1, §2, §4.
  * B. J. Dietvorst, J. P. Simmons, and C. Massey (2015) Algorithm aversion: people erroneously avoid algorithms after seeing them err..  Journal of Experimental Psychology: General 144 (1),  pp. 114.  Cited by: §1.
  * E. Peer, L. Brandimarte, S. Samat, and A. Acquisti (2017) Beyond the turk: alternative platforms for crowdsourcing behavioral research.  Journal of Experimental Social Psychology 70,  pp. 153–163.  Cited by: footnote 2.
  * H. Wallach (2019) Improving fairness in machine learning systems: what do industry practitioners need?.  Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems.  Cited by: §1, footnote 1.
  * H. Wallach (2020) Co-designing checklists to understand organizational challenges and opportunities around fairness in ai.  Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems.  Cited by: §1, footnote 8.

##  Appendix A Appendix: Tables and Figures

Table 1: Descriptive Statistics: Subjects | Op-Ed | Veneer
---|---|---
Male | 0.49 | 0.52
Female | 0.49 | 0.45
Other Gender | 0.02 | 0.02
Latinx | 0.10 | 0.07
White | 0.81 | 0.85
Black | 0.07 | 0.05
Asian | 0.05 | 0.04
Other Ethnicity | 0.06 | 0.06
AI Decisions | 0.49 | 0.40
Prepared by Educ. | 0.75 | 0.71
Knows ML | 0.46 | 0.45
Table 2: Status Quo Conditions and Demographics: Study 1 (“Counterfactual” and “Fatalism” Activism) | Positive | Rec (Scale) | Rec (Y/N) | Lawsuit | Damage Size
---|---|---|---|---|---
Algorithm Fix | .47*** | .45*** | 1.3e-14 | -.7*** | -.34***
| (.057) | (.051) | (.) | (.061) | (.044)
Status Quo Shown | .52*** | .65*** | .57*** | -.43*** | -.44***
| (.1) | (.1) | (.094) | (.095) | (.099)
Status Quo Shown (only if clicked) | .46*** | .63*** | .54*** | -.38*** | -.42***
| (.11) | (.099) | (.096) | (.092) | (.098)
Fix $\times$ Status Quo Shown | -.17** | -.26*** | -2.2e-14 | .19** | .12*
| (.072) | (.063) | (.) | (.084) | (.061)
Fix $\times$ Status Quo Shown (only if clicked) | -.19** | -.3*** | -1.9e-14 | .2** | .091
| (.076) | (.062) | (.) | (.082) | (.061)
Female | -.16** | -.11 | -.15** | .11 | .29***
| (.07) | (.072) | (.071) | (.066) | (.071)
Black | -.25* | -.22* | -.048 | .18 | .22*
| (.14) | (.13) | (.13) | (.11) | (.13)
Asian | -.17 | -.027 | -.084 | -.026 | -.16
| (.13) | (.13) | (.16) | (.14) | (.15)
Other Ethnicity | -.33*** | -.26* | -.21 | .083 | -.086
| (.12) | (.14) | (.16) | (.14) | (.15)
Political Conservatism (Standardized) | .094** | .082** | .026 | -.067** | -.079**
| (.038) | (.037) | (.036) | (.034) | (.038)
AI Decisions | .12* | .12* | .078 | .025 | .019
| (.069) | (.072) | (.075) | (.067) | (.074)
Prepared by Educ. | .13 | .14* | .099 | -.12* | -.12
| (.08) | (.083) | (.082) | (.075) | (.08)
Knows ML | .0055 | .0028 | -.056 | .062 | -.097
| (.07) | (.074) | (.074) | (.068) | (.076)
Observations | 1,992 | 1,992 | 1,992 | 1,992 | 1,992
$R^{2}$ | .1 | .12 | .12 | .12 | .097
$p$-value: Status Quo (SQ) Shown + Fix $\times$ SQ Shown=0 | .0005 | .00018 | 2.4e-09 | .024 | .0023
$p$-value: SQ Optional + Fix $\times$ SQ Opt=0 | .0075 | .0018 | 3.0e-08 | .097 | .0017
$p$-value: SQ Shown = SQ Opt | .48 | .85 | .74 | .57 | .85
$p$-value: Fix $\times$ SQ Shown = Fix $\times$ SQ Opt | .75 | .37 | 1 | .86 | .69
$p$-value: SQ Shown+Fix $\times$ SQ Shown = SQ Opt+Fix $\times$ SQ Opt | .32 | .47 | .74 | .5 | .95
Table 3: Status Quo Conditions and Demographics: Study 1 (“Scientific Veneer”) | Positive | Rec (Scale) | Rec (Y/N) | Lawsuit | Damage Size
---|---|---|---|---|---
Algorithm Fix | .34*** | .32*** | -2.9e-15 | -.51*** | -.23***
| (.055) | (.047) | (2.9e-09) | (.057) | (.042)
Status Quo Shown | .35*** | .55*** | .51*** | -.21** | -.16*
| (.086) | (.086) | (.081) | (.085) | (.086)
Status Quo Shown (only if clicked) | .28*** | .57*** | .57*** | -.27*** | -.21**
| (.088) | (.087) | (.08) | (.084) | (.092)
Fix $\times$ Status Quo Shown | -.11 | -.16** | 3.0e-15 | .087 | .03
| (.072) | (.064) | (2.3e-09) | (.079) | (.056)
Fix $\times$ Status Quo Shown (only if clicked) | -.043 | -.14** | 5.5e-15*** | .089 | .023
| (.074) | (.062) | (1.5e-15) | (.078) | (.062)
Female | -.05 | -.016 | -.034 | .0037 | .1
| (.063) | (.067) | (.067) | (.066) | (.075)
Black | -.55*** | -.42*** | -.28** | -.032 | -.048
| (.11) | (.12) | (.12) | (.17) | (.19)
Asian | -.17 | -.12 | -.2 | -.053 | .21*
| (.11) | (.12) | (.15) | (.14) | (.12)
Other Ethnicity | -.53*** | -.43** | -.19 | .2 | .14
| (.15) | (.17) | (.15) | (.15) | (.18)
Political Conservatism (Standardized) | .16*** | .13*** | .091*** | -.15*** | -.14***
| (.032) | (.033) | (.034) | (.033) | (.04)
AI Decisions | .019 | -.0022 | -.058 | -.0055 | -.028
| (.064) | (.067) | (.069) | (.068) | (.077)
Prepared by Educ. | .17*** | .08 | .062 | -.2*** | -.16**
| (.064) | (.068) | (.073) | (.067) | (.077)
Knows ML | .12* | .15** | .13* | -.027 | -.098
| (.065) | (.067) | (.068) | (.069) | (.078)
Observations | 1,984 | 1,984 | 1,984 | 1,984 | 1,984
$R^{2}$ | .11 | .11 | .086 | .094 | .063
$p$-value: Status Quo (SQ) Shown + Fix $\times$ SQ Shown=0 | .0038 | 8.7e-06 | 9.4e-10 | .19 | .16
$p$-value: SQ Optional + Fix $\times$ SQ Opt=0 | .0032 | 5.5e-07 | 3.5e-12 | .043 | .058
$p$-value: SQ Shown = SQ Opt | .41 | .81 | .4 | .45 | .55
$p$-value: Fix $\times$ SQ Shown = Fix $\times$ SQ Opt | .31 | .76 | 1 | .98 | .91
$p$-value: SQ Shown+Fix $\times$ SQ Shown = SQ Opt+Fix $\times$ SQ Opt | .99 | .63 | .4 | .48 | .53
Table 4:  Effects of Activism: Study 1 (“Counterfactual” and “Fatalism”
Activism)

_Panel A: Effects on Adoption Decisions_
Positive Rec (Scale) Rec (Y/N) Lawsuit Damage Size Hiring -.088 -.055 2.0e-17
-.04 -.28*** (.082) (.085) (.) (.087) (.079) Algorithm Fix .37*** .27*** .019
-.61*** -.28*** (.04) (.034) (.034) (.045) (.034) Counterfactual Op-Ed .24**
.19* .14** -.12 -.14 (.11) (.11) (.054) (.12) (.11) AI Fatalism Op-Ed -.16
-.28*** -.079 .19* .061 (.11) (.11) (.063) (.11) (.094) Fix$\times$
Counterfactual -.042 -.0064 -.28** .16* .041 (.078) (.061) (.11) (.086) (.075)
Fix$\times$ AI Fatalism -.044 .012 .16 .1 .012 (.075) (.062) (.13) (.077)
(.058) Fixed Effects Subject Subject Subject Subject Subject Observations
1,992 1,992 1,992 1,992 1,992 $R^{2}$ .68 .72 .76 .63 .73 $p$-value:
Counterfactual (CF)=Fatalism (FAT) .00061 .00003 .00079 .0057 .051 $p$-value:
Fix$\times$CF=Fix$\times$FAT .98 .82 .00079 .59 .74 $p$-value:
CF+Fix$\times$CF=FAT+Fix$\times$FAT .00016 .000021 .00079 .031 .11

_Panel B: Effect on the Use of Counterfactual Information_ :
Rec (Scale) Rec (Scale) Damage Size Damage Size Status Quo Seen (Instrumented
by Op-Ed) 1.2*** .69*** -.84*** -.35* (.28) (.2) (.32) (.21) Instrument CF Op-
Ed FAT Op-Ed CF Op-Ed FAT Op-Ed Observations 1,992 1,992 1,992 1,992 1st Stage
F-Stat 86 187 86 187

Table 5: Effects of Activism: Study 2 (“Scientific Veneer”)

_Panel A: Effects on Adoption Decisions_
Positive Rec (Scale) Rec (Y/N) Lawsuit Damage Size Hiring -.13 -.23** -1.3e-17
.0022 -.17* (.096) (.094) (.) (.091) (.093) Algorithm Fix .35*** .27*** .049
-.52*** -.24*** (.04) (.036) (.044) (.043) (.036) Scientific Veneer .036 .093
-.21*** -.17 -.036 (.16) (.15) (.079) (.15) (.13) Pro-AI Argument 1.1***
1.1*** .17** -.86*** -.48*** (.13) (.14) (.071) (.14) (.13) Scientific Veneer
$\times$ Pro AI -.019 -.072 .27** .24 -.037 (.21) (.2) (.11) (.21) (.19) Fix
$\times$ Scientific Veneer .078 -.046 .43*** .0013 -.0084 (.1) (.08) (.16)
(.1) (.07) Fix $\times$ Pro-AI -.29*** -.28*** -.34** .24** .033 (.073) (.062)
(.14) (.11) (.07) Fix $\times$ Scientific Veneer $\times$ Pro-AI -.084 .23*
-.54** .049 .1 (.14) (.12) (.23) (.16) (.11) Fixed Effects Subject Subject
Subject Subject Subject Observations 1,984 1,984 1,984 1,984 1,984 $R^{2}$ .62
.67 .63 .62 .69 $p$-value: Veneer (Ven) +Ven$\times$ProAI = 0 .9 .87 .49 .66
.58 $p$-value: Ven$\times$Fix +Ven$\times$ProAI$\times$Fix = 0 .96 .027 .49
.69 .29 $p$-value: Ven+Ven$\times$Fix
+Ven$\times$ProAI+Ven$\times$ProAI$\times$Fix = 0 .93 .11 .49 .36 .89

_Panel B: Effect on the Use of Counterfactual Information_ :
Views Status Quo Scientific Veneer -.39 (.26) Pro-AI Argument -.45* (.26)
Scientific Veneer $\times$ Pro AI .77* (.43) Observations 334 $R^{2}$ .013

##  Appendix B Regression Specifications

###  B.1 Study 1:“Counterfactual” and “Fatalism”

$\begin{split}Y_{i,t,fix}=&\beta[1(t=1)+1(fix=1)+Hiring+FE_{i}+CF_{i,t}+\\\
&FAT_{i,t}+CFxFix_{i,t,fix}+FATxFix_{i,t,fix}]+\epsilon\end{split}$ (1)

Where:

  * •

$Y_{i,t,fix}$ refers to the response to question $Y$ individual $i$ on case
$t$ regarding before/after the 6-month effort to $fix$. We run separate
regressions for each of the five questions categories.

  * •

$i$ indexes subjects (approximately 500 each for Study 1 and 2).

  * •

$t\in\\{1,2$$indexesthefirstorsecondorder.$β_1(t=1)$isafixedeffectforthefirstperiod.\par\itemize@item@$fix∈0,1$differentiatesanswerstothequestionsbefore($fix=0$)orafter($fix=1$)thesix-
monthsofdedicatedeffort.$β_1(fix=1)$isafixedeffectforafterthefix.\par\itemize@item@$Hiring$isequalto1forthehiringbusinesscase.\par\itemize@item@$FE_i$referstothesubject-
levelfixedeffect.Notethatthissubsumestheeffectsofa)theorderofthetwocases,aswellasb)whetherthestatusquodetailswereshown,hiddenoroptional.\par\itemize@item@$CF$referstothe``counterfactual^{\prime\prime}condition.Thisis1forthesubjectsreadingthecounterfactualinthesecondcase,and0foreveryoneinthefirstcase.\par\itemize@item@$FAT$referstothe``fatalism^{\prime\prime}condition.Thisis1forthesubjectsreadingthefatalisminthesecondcase,and0foreveryoneinthefirstcase.\par\itemize@item@Notethatnoop-
edistheexcludedcondition.\par\itemize@item@$CFxFix$isequalto$CF×1(fix=1)$\par\itemize@item@$FATxFix$isequalto$FAT×1(fix=1)$\par\itemize@item@$ϵ$:Standarderrorsareclusteredbysubject.\par$

###  B.2 Study 2: “Scientific Veneer”

$\begin{split}Y_{i,t,fix}=&\beta[1(t=1)+1(fix=1)+Hiring+FE_{i}+ProAi_{i,t}+SciVen_{i,t}+ProAixSciVen_{it}+\\\
&ProAixFix_{i,t}+SciVenxFix_{i,t}+ProAixSciVenxFix_{it}]+\epsilon\end{split}$
(2)

Where:

    * –

$Y_{i,t,fix}$ refers to the response to question $Y$ individual $i$ on case
$t$ regarding before/after the 6-month effort to $fix$. We run separate
regressions for each of the five questions categories.

    * –

$i$ indexes subjects (approximately 500 each for Study 1 and 2).

    * –

$t\in\\{1,2$$indexesthefirstorsecondorder.$β_1(t=1)$isafixedeffectforthefirstperiod.\par\itemize@item@$fix∈0,1$differentiatesanswerstothequestionsbefore($fix=0$)orafter($fix=1$)thesix-
monthsofdedicatedeffort.$β_1(fix=1)$isafixedeffectforafterthefix.\par\itemize@item@$Hiring$isequalto1forthehiringbusinesscase.\par\itemize@item@$FE_i$referstothesubject-
levelfixedeffect.Notethatthissubsumestheeffectsofa)theorderofthetwocases,aswellasb)whetherthestatusquodetailswereshown,hiddenoroptional.\par\itemize@item@$ProAi$referstotheconditioninwhichapositiveargumentaboutAIethicsismade.Thisis1forthesubjectsreadingthepositiveopinioninthesecondbusinesscase,and0foreveryoneinthefirstcase.\par\itemize@item@$SciVen$referstowhethertheexpertappliedscientificveneer.Thisis1forthesubjectsseeingscientificveneerinthesecondcase,and0foreveryoneinthefirstcase.\par\itemize@item@$ProAixSciVen$referstosubjectswhoseepro-
AIargumentswithscientificveneer.Thisis1forthesubjectsreadingtheseargumentsinthesecondcase,and0foreveryoneinthefirstcase.\par\itemize@item@$ProAixFix$isequalto$ProAi×1(fix=1)$\par\itemize@item@$SciVen$isequalto$SciVen×1(fix=1)$\par\itemize@item@$ProAixSciVenxFix$isequalto$ProAi×1(fix=1)$\par\itemize@item@Notethatanti-
AI,excludedistheomittedcondition.\par\itemize@item@$ϵ$:Standarderrorsareclusteredbysubject.\par$

##  Appendix C Experimental Materials: Case Studies

###  C.1 Text of Hiring-Related Business Case

The focus in this business case is on one of the world’s top technology
companies, headquartered in California (think of Facebook, Alphabet, Apple…).
The case refers to it as "Toptech Co.".

Please imagine you are an engineering executive for Toptech Co. The
organization you lead at Toptech implements software behind Toptech’s
consumer-facing products, digital infrastructure and advertising revenue.

As part of your job, you need to recruit and hire workers. Hiring qualified
technology workers is difficult and high-stakes. Hiring quality people can
make an enormous difference in product quality, market leadership and
profitability. Many technology companies have reported shortages of qualified
workers.

Toptech gets high volumes of applications from aspiring software engineers
from around the world. Because of this high volume, it is cost-prohibitive to
hire recruiters to read each job application with great care. As a result, a
team at Toptech was assigned to work with your team to develop software to
read the text of job applications and score them.

The technology looks at Toptech’s historical hiring and job performance
records. This data contains information about what job candidates and workers
perform well on the job, who doesn’t, and what each candidates’ personal
characteristics, experiences and qualifications were on the job application

Using this data, a team of statistical and machine learning experts has built
a sophisticated model of what candidates are is likely to succeed using this
data. The model is truly sophisticated; it allows for many different “paths”
to being labeled a likely success. In data from the past five years, the model
is very accurate at predicting which candidates will succeed or fail, even as
circumstances have changed in the tech industry and at Toptech.

Advocates of the program claim that it improves the objectivity and
consistency of evaluation. However, critics are concerned that the algorithm
may propagate biases in the historical data. For example, critics inside the
company point to the fact that the software’s recommendations for who to hire
in technical roles are approximately 71% men, and 29% women.

Your questions today are about whether your organization should adopt this
technology.

###  C.2 Text of Lending-Related Business Case

For this scenario, imagine you are a finance executive at one of the largest
banks in the country (think of Bank of America, Wells Fargo, Citigroup…). The
case will refer to it as "Financial Co." The organization you lead at
Financial oversees consumer lending products and services.

As part of your job, you need to oversee a process for making decisions about
small business lending. This is a critical part of Financial’s strategy.

Financial gets high volumes of applications from small businesses. Because of
this high volume, it is costly to hire loan officers to read each loan
application with great care. As a result, a team at Financial was assigned to
work with your team to develop software to read the content of loan
applications and score them.

The technology looks at Financial’s historical data about loan performance.
This contains data about what borrowers historically paid back their loans,
who didn’t and what all applicants’ loan characteristics are.

Using this data, a team of statistical and machine learning experts has built
a sophisticated model of what borrowers are likely to pay back using this
data. The model is truly sophisticated; it allows for many different “paths”
to being labeled a likely success. In data from the past five years, the model
is very accurate at predicting which borrowers will pay back or not, even as
circumstances have changed in the economy.

Advocates of the program claim that it improves the objectivity and
consistency of loan evaluation. However, critics are concerned that the
algorithm may propagate biases in the historical data. For example, critics
inside the company point to the fact that the software’s recommendations
charge higher and more expensive interest rates – about 3.2 higher basis
points – to Latinx and African-American borrowers, compared to white
borrowers.

Your questions today are about whether your organization should adopt this
technology.

##  Appendix D Survey Questions

      * *

How negative or positive is the impact of this technology? (very negative -
very positive)

      * *

What is the probability that a fairness issue is alleged and becomes a problem
(through lawsuits or PR)? (very low - very high)

      * *

If a problem is alleged, how damaging would that be? (not at all damaging -
very damaging)

      * *

To what extent do you recommend using this algorithm rather than the status
quo processes? (Do not recommend - Strongly recommend)

      * *

If you were forced to make a decision on the future use of the algorithm now,
what would you do? (Use the status quo methods (don’t switch to the algorithm)
OR Switch to adopt this algorithm).

[pagebreak]

Now suppose that the team agrees to spend the next six months making an effort
to address fairness issues. Please re-evaluate the proposal based on how you
expect the algorithm to perform after six additional months of your team
attempting to address fairness issues.

##  Appendix E Experimental Materials: “Scientific Veneer” Interventions

###  E.1 Expert Introductions

For our veneer treatment, the following introduction was provided to the
expert:

_To help your team make a decision, your company has hired an expert to offer
an assessment of the technology._

_The expert, Taylor, has a Ph.D. in Physics from UC Berkeley. Taylor has been
employed as a third-party evaluator of digital technology for the past two
years, working on questions like those you’re facing._

_On the next page you will read Taylor’s summary and analysis of the
technology and decision you are facing._

For our non-veneer treatment, the following introduction was provided to the
expert:

_To help your team make a decision, your company has hired an expert to offer
an assessment of the technology._

_The expert, Taylor, has a PhD. in Sociology from UC Berkeley. Taylor has been
employed as a third-party evaluator of digital technology for the past two
years, working on questions like those you’re facing._

_On the next page you will read Taylor’s summary and analysis of the
technology and decision you are facing._

###  E.2 Pro-AI Argument Without Scientific Veneer

###  E.3 Pro-AI Argument With Scientific Veneer

###  E.4 Anti-AI Argument Without Scientific Veneer

###  E.5 Anti-AI Argument With Scientific Veneer

## References