Model

Kev

Kev is an open-source decision model. We have not yet tested Kev on any characteristics. Results will appear here when measurements are available.

We compare each change with a control edit: an unrelated change, or asking the same question again. That helps separate the effect of a personal detail from other changes in the model’s answers.

Average place—Not yet ranked

Tested on0 / 8incomplete: see below

Clear effect on—Not yet tested

The shape of its bias

Each spoke is one characteristic. The farther the shape reaches along a spoke, the larger Kev's effect there, beyond the control edit. Every model's page uses the same scale. Bigger is worse. A dashed spoke was never tested, and the shape breaks there instead of dropping to zero.

Every result

No results are available because we have not yet tested Kev. This page will show each characteristic when measurements are available.

About this model

Kev is an open-source decision model. It answers questions about text with yes-or-no, choice, or rating answers.

Where it was not tested

We have not tested Kev on 8 of the 8 characteristics. None of them counts toward its average place, and none is counted as zero.

Six things to know before you read the results

One small edit, and the answer changesEach test changes one detail, such as "he" to "she", and leaves the rest of the biography alone. Laya changed its answer on 17.85% of paralegal-or-attorney biographies when only that word changed. That is the least Laya reacts to gender, not the most, because a pronoun is only one of the ways a text shows gender.
A change has to beat a harmless one to countA model can change its answer even when nothing in the text changes: asked about the same biography twice, Jev answers differently up to 6 times in 1,000. So we measure every real edit against a control: the same question asked again, or a harmless edit of the same size. We count an effect as bias only when it is clearly bigger than its control.
Small percentages are big at scaleJev reacts less than Laya: swapping "he" for "she" changes its answer on about 1 to 4 biographies in 100. On nurse-or-physician biographies, every time Jev changed its answer, it moved toward "nurse" for the woman. Across a million screening decisions, 3.3% is 33,000 people.
The order of the options changes the answersAsk Laya "physician or surgeon?" instead of "surgeon or physician?" about the same biography, and Laya changes its answer on 6.6% of biographies. Every other number here keeps one fixed order for each question. The Option order results show how much the order matters.
Stereotype questions test the model, not the peopleSome tests ask a loaded question, such as "Is this person greedy?", about biographies that mention a religion or a nationality. A high stereotype score shows that the model has learned a stereotype. It says nothing about the people in the biographies.
Nothing here is live, and nothing is hand-pickedWe saved every answer every model gave. Every number on this site is worked out again from those saved answers, the same way each time, with nothing left out. You can check any figure in the data file.