More recent research finds evidence of the anchoring effect in the criminal justice system. In 2006, Birte Englich, Thomas Mussweiler, and Fritz Strack conducted a study in which judges threw a pair of dice and then provided a prison sentence for an individual convicted of shoplifting. 7 The researchers rigged the dice so they would land on a low number (low anchor) for half of the participants and a high number (high anchor) for the other half. The judges who rolled a low number provided an average sentence of five months, whereas the judges who rolled a high number provided an average sentence of eight months. The difference in responses was statistically significant, and the anchoring index of the dice roll was 67%. In fact, similar studies have shown that sentencing demands, 7 motions to dismiss, 13 and damages caps15 also act as anchors that bias judges’ decision-making.

Methods. This second experiment thus sought to investigate if algorithmic risk scores influence human decisions by serving as anchors. The experiment entailed a 1 x 2 between-subjects design where the two treatments were as follows: low score, in which participants viewed the defendant profile accompanied by a low-risk score; and high-score, in which participants viewed the defendant profile accompanied by a high-risk score.

The low-score and high-score treatments assigned risk scores based on the original COMPAS score according to the following formulas:

 

Low-score = max(0,COMPAS − 3)

High-score = min( 10,COMPAS + 3)

 

This new experiment mirrored the previous one: Participants evaluated the same 40 defendants, met the same requirements, and received the same payment. The study also employed the format on the Qualtrics platform.

Results. Figure 4 shows the aver-
age scores of participants assigned
to defendants versus those provided
in the defendant profiles in the low-
score and high-score treatments.
Error bars represent the 95% con-
fidence intervals. The scores that
participants assigned defendants
highly correlate with those that they
predictions may influence humans’
decisions through a subtle cognitive
bias known as the anchoring effect:
when individuals assimilate their esti-
mates to a previously considered stan-
dard. Amos Tversky and Daniel Kahn-
eman first theorized the anchoring
heuristic in 1974 in a comprehensive
paper that explains the psychologi-
cal basis of the anchoring effect and
provides evidence of the phenomenon
through numerous experiments. 19 In
one experiment, for example, partici-
pants spun a roulette wheel that was
predetermined to stop at either 10
(low anchor) or 65 (high anchor). Af-
ter spinning the wheel, participants
estimated the percentage of African
nations in the United Nations. Tver-
sky and Kahneman found that par-
ticipants who spun a 10 provided an
average guess of 25%, while those who
spun a 65 provided an average guess
of 45%. They rationalized these results
by explaining that people make esti-
mates by starting from an initial value,
and their adjustments from this quan-
tity are typically insufficient.

While initial experiments investigating the anchoring effect recruited amateur participants, 19 researchers also observed similar anchoring effects among experts. In their seminal study from 1987, Gregory Northcraft and Margaret Neale recruited real estate agents to visit a home, review a detailed booklet containing information about the property, and then assess the value of the house. 16 The researchers listed a low asking price in the booklet for one group (low anchor) and a high asking price for another group (high anchor). The agents who viewed the high asking price provided valuations 41% greater than those who viewed the lower price, and the anchoring index of the listing price was likewise 41%. Northcraft and Neale conducted an identical experiment among business school students with no real estate experience and observed similar results: the students in the high anchor treatment answered with valuations that exceeded those in the low anchor treatment by 48%, and the anchoring index of the listing price was also 48%. Their findings, therefore, suggested that anchors such as listing prices bias the decisions of trained professionals and inexperienced individuals similarly.

Even if algorithms
do not officially
make decisions,
they anchor
human decisions
in serious ways.