Do You Trust Your Own Voice? – An Experimental Approach to the Strategic Use of Audio Deepfakes

Last registered on December 03, 2024
View Trial History

Pre-Trial

Trial Information

General Information

Title
Do You Trust Your Own Voice? – An Experimental Approach to the Strategic Use of Audio Deepfakes
RCT ID
AEARCTR-0014924
Initial registration date
November 28, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
December 03, 2024, 1:35 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Primary Investigator

Name
Jannik Greif
Affiliation
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
On going
Start date
2024-10-23
End date
2025-03-31
Keywords
Behavior
Additional Keywords
Trust, Audio Deepfake
JEL code(s)
C91, D83, D91
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The provision of personal information in online interactions - the social presence - reduces social distance and therefore increases trust. This includes profile pictures, personal descriptions, video or voice messages, and other forms of direct communication that enhance the perception of human interaction. Advances in artificial intelligence have enabled the creation of convincing and realistic deepfakes, which could potentially undermine the concept of social presence. Deepfakes are artificial but hyper-realistic media content (video, audio, image) and allow the creation of artificial profile pictures, audio or video messages, which can therefore be used to manipulate the social presence. This poses a severe challenge, as the quality and therefore believability of deepfakes increased to the point that people are often unable to reliably differentiate between authentic and artificial content. Audio deepfakes may alter vocal features such as gender, emotion, and perceived trustworthiness, which potentially affect others’ behavior.
This experimental study examines the impact of audio deepfakes on trust-based decision-making. It investigates whether manipulated vocal characteristics in an artificial audio message as pre-play communication affects behavior in a trust game - for both the sender and receiver. The findings may provide insights into how artificially generated media influences trust and decision-making in online interactions.
External Link(s)

Registration Citation

Citation
Greif, Jannik. 2024. "Do You Trust Your Own Voice? – An Experimental Approach to the Strategic Use of Audio Deepfakes ." AEA RCT Registry. December 03. https://doi.org/10.1257/rct.14924-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Subjects participate in a laboratory-online experiment and are assigned to the roles Speaker (Trustee) or Listener (Trustor) and engage in a trust game. Listeners are assigned to one of the treatments, varying the type of message they receive: Control (no message), Text (transcribed message), Authentic (authentic voice message), Artificial-1 (artificial voice message, young woman), Artificial-2 (middle aged woman), Artificial-3 (young male), Artificial-4 (middle aged man).
Intervention Start Date
2024-12-09
Intervention End Date
2025-01-31

Primary Outcomes

Primary Outcomes (end points)
Trust game decisions (Amount transferred and amount transferred back)
Perceived authenticity of audio messages
Voice preference of artificial voices
Primary Outcomes (explanation)
Trust game decisions measure the trust and trustworthiness in respect to the message type. Speakers’ trust game decisions are elicited using the strategy method, which allows for a complete mapping of potential responses. Listeners’ trust game decisions are elicited using an initial decision before the exposure to the message and a decision after the exposure to capture deviations from individual baselines.
Perceived authenticity of audio messages measures the human detection rate of audio deepfakes, the believability of the used audio messages, as well as discloses potential influences on the trust game decisions.
Voice preference of artificial voices assess whether subjects base their choice of artificial voice on their perception of trustworthiness or characteristics such as gender and age.

Secondary Outcomes

Secondary Outcomes (end points)
Expectations of trust game decisions
Perceived trustworthiness ratings of artificial voices
General trust and risk preferences
Demographic factors
Secondary Outcomes (explanation)
Expectations of trust game decisions analyze the alignment between Speaker expectations and actual Listener behavior in respect to the message type.
Perceived trustworthiness ratings of artificial voices assess whether subjects base their choice of artificial voice on their perception of trustworthiness or characteristics such as gender and age.
General trust and risk preferences assess individual differences in baseline trust and risk.
Demographic factors (including age, gender, English understanding and speaking level) are examined to measure their influence on trust game decisions and the evaluation of authentic and artificial messages.

Experimental Design

Experimental Design
The experiment consists of a trust game with one-sided pre-play communication and involves two roles: Speaker (Trustee) and Listener (Trustor). The study is split in three parts:
Part 1 (Laboratory, Speaker Role): Subjects assigned to the Speaker role record an authentic audio message and select an artificial audio message. They participate in trust game decisions using the strategy method and provide ratings for artificial voices. General trust, risk preferences, and demographic information are also elicited.
Part 2 (Online, Listener Role): Subjects assigned to the Listener role are recruited online and randomly assigned to one of seven treatments: Control (no message), Text (transcribed message), Authentic (authentic voice message), Artificial-1 (artificial voice message, young woman), Artificial-2 (middle aged woman), Artificial-3 (young male), Artificial-4 (middle aged man).. They make trust game decisions before and after receiving the message and classify the message as either authentic or artificial. General trust, risk preferences, and demographic information are also elicited.
Part 3 (Laboratory, Speaker Role): Speakers review aggregated trust game decision statistics based on message type and make an additional decision on which message type to use for a new trust game interaction, elicited using the strategy method.
Experimental Design Details
Not available
Randomization Method
Individual subjects in the role of Speaker are randomly recruited from the university’s subject pool. Due to known technical difficulties in reliably generating audio deepfakes, the planned number of subjects in the role of Speaker is doubled in order to ensure having sufficient technically sound audio deepfakes. Individual subjects in the role of Listener are recruited on Prolific and randomly assigned to the treatments.
Randomization Unit
Individual subjects in the role of Speaker are randomly recruited from the university’s subject pool. Individual subjects in the role of Listener are recruited on Prolific and randomly assigned to the treatments.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
-
Sample size: planned number of observations
40 Speaker observations (5 audio messages + 1 text message each; 240 messages) 2800 Listener observations (10 observations for each message + control treatment)
Sample size (or number of clusters) by treatment arms
Sample size of 80 Speaker (in order to get 40 technically sound artificial audio messages).
Listeners and the respective treatment:
- Control: 400 subjects
- Text: 400 subjects
- Authentic: 400 subjects
- Artificial-1: 400 subjects
- Artificial-2: 400 subjects
- Artificial-3: 400 subjects
- Artificial-4: 400 subjects
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Using G*Power with alpha = 0.05, beta = 0.2 and medium effect sizes - Mann-Whitney U test: 134 subjects (67 per group) - Chi-square test of independence: 207 subjects - Pearson correlation: 82 subjects
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
2024-09-25
IRB Approval Number
o6PCXoZF
Analysis Plan

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information