We’ve learned that many of the hurdles that are faced by marginalized communities seeking services include the accessibility, readability and discoverability of the benefits programs that are available. Many of the programs and policies that are available are written in ways that are too complex for most people to understand either due to legibility, language or literacy. While some outputs from LLMs can be unreliable, language and the written word is a place where this technology excels.
Improved language support coupled with referral systems will create service delivery that empowers individuals to self identify their eligibility and lead to subsequent referrals that are more relevant.
Reduce content complexity
Simplifying current policy language would allow potential applicants to better understand eligibility requirements and allow them to self identify more opportunities.
Increase audience reach
Once content has been simplified, it increases the quality of subsequent accessibility and translations resulting in expanding the reach of services to marginalized communities.
Improve referral recommendations
As applicants gain confidence in easier to understand acceptance criteria, there is an improved likelihood that referral systems would gain more accurate understanding of their needs.
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