Preference for Explainable AI Experiment

Individuals make a decision to determine how a private lender allocate a $10000-loan. Participants are randomized into a neutral treatment where payoffs are not tied to actual loan repayment outcome, or a "lender-aligned" treatment where they have with direct stakes tied to loan repayment.

The actual date of the experiment will be pre-registered here before launch.

2025-06-01

2025-12-06

(1) Binary decision of whether the participant want to see an explanation of how the AI made the prediction of the default risk of the borrowers before making a loan allocation decision
** The full set of outcome variables that we intend to analyze is listed in the stata do file attached to this experiment before the experiment itself; besides those tables, we plan to present the word cloud formed by the responses to the question “You chose to see an explanation of how the AI Algorithm made the default risk predictions BEFORE making the loan decision, what were you hoping to learn from the explanation?”, among the participants who was presented and taken up the option to review an explanation where the chance to see the role race and gender play in the default risk calculation by the AI is made salient

(2) Binary decision of whether the participant want to see the predicted default risk of the borrowers before making a loan allocation decision

Both study information-avoidance decisions.

(3) Whether the participant want to allocate 90% of the loan to the low-risk borrower and 10% to the high-risk borrower, or 50% to each
(4) How much, to reduce the second participant’s bonus at a personal cost of $0.01 per $1.00 reduction, up to the full $10 (3 scenarios -informed, not informed, chose not to be informed about explanation) - within subject

This gets at whether the information changes the actual loan allocation decision (i.e. to see if the information influences decision)

I experimentally test how individual's preference for information regarding an AI is influenced by certain factors.

(Full design attached and randomization flow attached in this pre-registration as pdf/docs before the experiment launch)

Not available

Randomization done using Qualtrics' in-built function

individual

No

See below

2,500

Evenly for non-lender-aligned and for lender-aligned for main arm. 1250 each.

4/5 will be in the arms where they choose whether or not to see the explanation: Same shares for a variant of the main arm where race-gender descriptors for borrowers are replaced by odd/even number birth-day/month. (total 2000 across these arms)

1/5 as much as above, but within this even between non-lender-aligned and lender-aligned for an arm similar to main arm except that participants have a choice to see the predicted default risk generated by AI or not. These participants will be randomized to see an explanation. (total 500)

2500 in total.