Primary Outcomes (explanation)
In all of the following, we will focus primarily on the minority-majority gap (mmg), which we define as the gap in starting, completion, or score between a minority and majority group. These groups can be defined by gender, where women and non-binary people are the minority and men are the majority, race, where underrepresented minorities are the minority and white and Asian people are the majority, or both, where either a gender or race minority category means minority and the remainder are majority. We anticipate that the mmg will favor majorities in all of our measures.
Below we outline the primary comparisons and what conclusions will be drawn from them:
1) AI-Explained vs. AI-Biased: by comparing these two groups, we will evaluate the impact that disclaiming the possible existence of bias in AI has on the mmg.
The mmg may be higher in AI-biased due to greater concern from minorities about the presence of bias in the AI system; on the other hand, the mmg may be lower if the presence of a disclaimer makes minority candidates believe that transparency indicates something positive about the AI provider or employer, such as their goal to reduce disparities.
2) AI-Biased vs. AI-Debiased: by comparing these two groups, we will evaluate the impact that providing a statement asserting the unbiasedness of the AI has in the presence of knowledge that AI can be biased has on the mmg.
3) AI-Biased vs. Human-Oversight: by comparing these two groups, we will evaluate the impact that providing a statement asserting human oversight has in the presence of knowledge that AI can be biased has on the mmg.
For both 2 and 3 above, providing this additional information may lead to a smaller mmg by assuaging minorities’ concerns about bias in the AI. However, this information may be ineffective or actually seen as a negative if it is seen as a band-aid, rather than a true intervention to curb disparities between majority and minority groups.
4) AI-Debiased vs. Human-Oversight: by comparing these two groups, we will evaluate the relative efficacy of providing statements of debiased AI vs. statements of human oversight in reducing the mmg.
Using this comparison, we will understand the relative efficacy of providing information about efforts to debias the AI and having human oversight in final decision making. These are two proposed ideas for how to assuage concerns generated by required disclaimers of the potential bias in AI. Assertions of human oversight may be more effective if people believe that humans make better final decisions or they are uncomfortable with AI making important decisions like hiring.
On the other hand, minorities may be concerned about the bias against themselves in human evaluators, leading to claims of human oversight being less effective.
5) AI-Explained vs. AI-Debiased: by comparing these two groups, we will evaluate the efficacy of providing statements of debiased AI relative to the benchmark of no explanation of bias for the mmg.
6) AI-Explained vs. Human-Oversight: by comparing these two groups, we will evaluate the efficacy of providing statements of human oversight relative to the benchmark of no explanation of bias for the mmg.
For both 5 and 6, we will be understanding whether the impact of informing applicants about the potential bias in AI can be mitigated by information about efforts to undo that bias (in AI-Debiased) or information about human oversight in the decision-making process (in Human-Oversight).