ACSIncome#

Introduction#

The ACSIncome dataset is one of five datasets created by Ding et al. [1] as an improved alternative to the popular UCI Adult dataset. [2] Briefly, the UCI Adult dataset is commonly used as a benchmark dataset when comparing different algorithmic fairness interventions. ACSIncome offers a few improvements, such as providing more datapoints (1,664,500 vs. 48,842) and more recent data (2018 vs. 1994). Further, the binary labels in the UCI Adult dataset indicate whether an individual earned more than $50k US dollars in that year. Ding et al. show that the choice of threshold impacts the amount of disparity in proportion of positives, so they allow users to define any threshold rather than fixing it at $50k.

Ding et al. compiled data from the American Community Survey (ACS) Public Use Microdata Sample (PUMS). Note that this is a different source than the Annual Social and Economic Supplement (ASEC) of the Current Population Survey (CPS) used to construct the original UCI Adult dataset. Ding et al. filtered the data such that ACSIncome only includes individuals above 16 years old who worked at least 1 hour per week in the past year and had an income of at least $100.

Dataset Description#

Ding et al. provide data from 2014-2018 for all 50 states and Puerto Rico. Note that Puerto Rico is the only US territory included in this dataset. We uploaded the 2018 data to OpenML. The dataset contains 1,664,500 rows. Each row describes a person and contains 10 features, which we describe below:

Column name	Description
AGEP	Age as an integer from 0 to 99
COW	Class of worker: Employee of a private for-profit company or business, or of an individual, for wages, salary, or commissions Employee of a private not-for-profit, tax-exempt, or charitable organization Local government employee (city, county, etc.) State government employee Federal government employee Self-employed in own not incorporated business, professional practice, or farm Self-employed in own incorporated business, professional practice or farm Working without pay in family business or farm Unemployed and last worked 5 years ago or earlier or never worked
SCHL	Educational attainment: No schooling completed Nursery school or preschool Kindergarten Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Grade 6 Grade 7 Grade 8 Grade 9 Grade 10 Grade 11 Grade 12 (no diploma) Regular high school diploma GED or alternative credential Some college but less than 1 year 1 or more years of college credit but no degree Associate’s degree Bachelor’s degree Master’s degree Professional degree beyond a bachelor’s degree Doctorate degree
MAR	Marital status: Married Widowed Divorced Separated Never married or under 15 years old
OCCP	Occupation. There are over 500 categories. Please see data dictionary at ACS PUMS documentation [3] for the full list of occupation codes.
POBP	Place of birth. There are over 200 categories, including the 50 US states and several countries. Please see the data dictionary at ACS PUMS documentation [3] for the full list.
RELP	Relationship to householder: Reference person Husband or wife Biological son or daughter Adopted son or daughter Stepson or stepdaughter Brother or sister Father or mother Grandchild Parent-in-law Son-in-law or daughter-in-law Other relative Roomer or boarder Housemate or roommate Unmarried partner Foster child Other nonrelative Institutionalized group quarters population. Includes correctional facilities, nursing homes, and mental hospitals. [4] Noninstitutionalized group quarters population. Includes college dormitories, military barracks, group homes, missions, and shelters. [4]
WKHP	Usual hours worked per week in the past 12 months. Values are an integer from 1 to 99. Any hours above 99 are rounded down to 99
SEX	Sex code: Male Female
RAC1P	Race code White alone Black or African American alone American Indian alone Alaska Native alone American Indian and Alaska native tribes specified; or American Indian or Alaska Native, not specified and no other races Asian alone Native Hawaiian and Other Pacific Islander alone Some Other Race alone Two or More races

The target label is given by PINCP. For generalizability, the integer value is provided. A threshold can be applied to PINCP to frame this as a binary classification task.

Column name	Description
PINCP	Total annual income per person, denoted as an integer ranging from 104 to 1,423,000.

Using the dataset#

The dataset can be loaded via the fairlearn.datasets.fetch_acs_income() function. By default, the dataset is returned as a pandas.DataFrame.

References: