<translate> To evaluate bias, Hutiris team is creating a toolbox with guidelines for conducting context-specific evaluation data sets, and developing a Python library to audit bias during the development of voice biometrics. We realized that we cannot create a data set thats going to cover every possible scenario, but instead, direct how evaluation should happen. We can then use insights from developing the evaluation guideline to propose reporting standards for how technology performance should be communicated to consumers, Hutiri explains. <swh>Ili kutathmini upendeleo, timu ya Hutiri inaunda zana yenye miongozo ya kufanya utathmini wenye muktadha mahsusi wa seti za data, na kuunda maktaba ya Python ili kukagua upendeleo wakati wa uundaji wa bayometriki ya sauti. Tuligundua kuwa hatuwezi kuunda seti ya data ambayo itashughulikia kila hali inayowezekana, lakini badala yake, tuelekeze jinsi tathmini inapaswa kufanywa. Kisha tunaweza kutumia maarifa kutoka kwa utengenezaji wa mwongozo wa tathmini ili kupendekeza viwango vya kuripoti jinsi utendaji wa teknolojia unapaswa kuwasilishwa kwa watumiaji, Hutiri anafafanua. 