<prompt> Etsa mohlala oa bothata ba algorithmic toka.nan  <response>: Instruction in English: Create an example of an algorithmic fairness problem. . Response in English: One of the most common examples of an algorithmic fairness problem is the one associated with loan or credit applications. Considering that credit scores, income, employment status and history etc. are some of the factors that determine creditworthiness, some models may inadvertently discriminate against certain demographics.For instance, take the case of an algorithm that is designed to predict the chance of loan repayment. The program may incorporate factors such as credit scores, income, employment status, and loan amount to determine whether a loan application should be approved or denied. Since credit scores are given significant importance, discrepancies begin to appear. People from low-income neighborhoods or a particular ethnicity, who may have lower credit scores- not because of their inability to repay loans but because of systematic biases in the credit scoring system- may find their applications denied more often. The same holds for people with lower income, a lack of credit history, or those who have faced long periods of unemployment - factors that disproportionately and unjustly affect certain groups.This is a manifestation of human bias reflecting on AI since historical data used to train the algorithm was based on past credit decisions made by humans.To sum up, models that base their decisions on credit scores and other such metrics may end up systematically denying loans to individuals that belong to certain communities or demographics, resulting in a lack of fairness and unequal opportunities. . Response in Sesotho: ngoe ea mehlala e atileng haholo ea bothata ba algorithmic ea toka ke e amanang le likopo tsa kalimo kapa tsa mekitlane. Ha ho nahanoa hore lintlha tsa mekitlane, chelete e kenang, boemo ba mosebetsi le nalane, joalo-joalo ke tse ling tsa lintlha tse khethollang ho tšoaneleha ha mekoloto, mefuta e meng e ka khetholla batho ba bang ba sa tsebe. Mohlala, nka taba ea algorithm e etselitsoeng ho bolela esale pele monyetla oa ho khutlisa kalimo. Lenaneo le ka kenyelletsa lintlha tse kang lintlha tsa mokoloto, chelete e kenang, boemo ba mosebetsi, le palo ea kalimo ho bona hore na kopo ea kalimo e lokela ho amoheloa kapa ho haneloa. Kaha lintlha tsa mokitlane li fuoa bohlokoa bo boholo, liphapang li qala ho hlaha. Batho ba tsoang libakeng tse nang le meputso e tlaase kapa morabe o itseng, ba ka nang ba e-ba le lintlha tse tlaase tsa mokoloto-eseng ka lebaka la ho se khone ho lefa mekoloto empa ka lebaka la leeme le hlophisitsoeng tsamaisong ea lintlha tsa mekoloto- ba ka fumana likopo tsa bona li hanoa khafetsa. Ho joalo le ho batho ba nang le moputso o tlase, ba haelloang ke nalane ea mekoloto, kapa ba tobane le nako e telele ea ho hloka mosebetsi - lintlha tse amang lihlopha tse itseng ka mokhoa o sa lekanyetsoang le ka leeme. Ena ke ponahatso ea leeme la batho le bontsang AI kaha data ea nalane e sebelisitsoeng ho koetlisa algorithm e ne e ipapisitse le liqeto tse fetileng tsa mokoloto tse entsoeng ke batho. Ho akaretsa, limotlelara tse thehang liqeto tsa tsona holima lintlha tsa mekitlane le lintlha tse ling tse joalo li ka qetella li hana likalimo ho batho ba tsoang lichabeng tse itseng kapa palo ea batho, e leng se bakang khaello ea toka le menyetla e sa lekanang. 