In January 2026, Senator Sharon Shewmake (42nd District) submitted Senate Bill 6315: 

 "AN ACT Relating to collecting and publishing disaggregated data to inform state-funded programs, services, and activities; and adding a new chapter to Title 43 RCW." It was then co-sponsored by Senator T'wina Nobles. The bill was sent to the State Government, Tribal Affairs and Elections Committee, chaired by Senator Javier Valdez.

​

In its findings, SB 6315 spells out the reasons why data disaggregation is necessary to assure equity among state-funded activities. Though a long read, the findings clearly describe why data disaggregation for sex and race are vital.  (Full text of SB6315 can be found here:      https://lawfilesext.leg.wa.gov/biennium/2025-26/Pdf/Bills/Senate%20Bills/6315.pdf

​

The FINDINGS:

​

(1) Data bias, meaning the unfair, inaccurate, or systematically skewing of data, affects women across all racial groups every day. Data bias can show up in many areas related to data, including its collection, storage, analysis, or reporting, or in how it is used for automated decision making.

​

(2) When data is biased, it erases or distorts real experiences, especially for groups that have historically been undercounted or misclassified. This is not done with bad intention or on purpose; itis because society as a whole tends to associate the term "human" with "man." As a result, the voices of women, including those who are black, indigenous, or women of color, are silenced when their opinions and concerns are ignored through the collection, analysis, and reporting of data. Omitting women's perspectives often leads to unintentional male bias, even when labeled as "gender neutral." Data is collected in many ways and if there are no requirements for counting certain groups, they often are not counted.

​

(3) Communities most impacted by inequities receive less visibility, fewer resources, and less tailored support. When such data is utilized in policymaking and funding allocation, it may lead to unexpected and potentially costly adverse outcomes. Biased data leads directly to biased policy and budget decisions. Data bias can create direct harm when it leads to inaccurate categorization, false assumptions, or discriminatory outcomes. When people see that data about them is wrong, incomplete, or used against them, they lose trust in government systems. Biased data systems can also create risk for agencies and states.

​

(4) Such adverse outcomes are evidenced in research including the design of automotive safety, personal protective equipment, and medical and pharmaceutical studies. Research findings point to the limitations of using the "adult male" default model that set the standard for automobile crash testing. Additionally, the average physical differences in persons assigned male and female at birth can affect working conditions in a manner that warrants individual or unique attention. Research also highlights the need to go beyond the "average male" in the realm of personal protective equipment. The impacts and benefits of fitting protective equipment for those people who will actually perform the work is critical. Designing protective equipment, gear, and tools without incorporating the different characteristics of diverse populations leaves women and smaller and larger framed individuals at an unacceptable level of risk.

​

(5) Further, research addressing women's health and pharmaceutical issues also points to adverse outcomes when women are not part of the process. Negative outcomes are also apparent in urban planning, park planning, the workplace, public life, and finance. When policymakers fail to account for gender, public spaces become male spaces by default.

 

(6) Even in the present, leading economic indicators do not measure unpaid labor that was historically undertaken by women, such as caregiving, domestic labor, child rearing, elder care, and household management, so they are treated as less important. This means paid labor is affected heavily by the burden of unpaid labor carried by individuals.

​

(7) The following agencies and programs are likely to have particular impacts due to the better publication of data disaggregated particularly on the basis of sex and gender:(a) The department of social and health services as related to caseloads, family support, domestic violence services, long-term care, and behavioral health;(b) The department of health as related to public health monitoring, maternal and child health, injury prevention, sexual and reproductive health, and public health interventions;(c) The department of children, youth, and families as related to childcare subsidies, family care supports, and prevention programs;(d) The office of the superintendent of public instruction as related to school health services, after-school programs, and supports that respond to gendered participation and needs; (e) The department of transportation as related to transit safety, lighting, route design, and policing resource allocation to respond to when data shows gendered patterns of mobility and harassment; and (f) The department of commerce as related to shelters, tenant protections, and housing placements when sex-disaggregated homelessness or domestic violence data drives targeted capacity.

​

(8) As artificial intelligence sees increased adoption, it is essential that the elements used by decision makers contain more nuanced data. Progress cannot be made without measurement, and bad data leads to bad resource allocation

​

(9) Requiring agencies to collect and publish data using the same guidelines and standards will increase consistency between agencies, increase representation in agency data and allow agencies to better measure the impact of agency actions on indicators of community wellbeing.

​

(10) While the federal government has issued general statistical standards for disaggregating race and ethnicity data, there exist no general federal statistical standards for sex or gender identity.

​

(11) The legislature's intent is to ensure that the voices of women and black, indigenous, and persons of color are incorporated into policymaking processes; ensure that the state makes wise financial decisions using the data it collects; and to strengthen and streamline data collection practices across agencies and when allocating funds.

​

​