Na prihajajoči konferenci Big Data: A Tool for Inclusion or Exclusion? (FTC, Washington, US) se bodo lotili implikacij kategoriziranja potrošnikov in napovedovanja njihovega vedenja z analitiko velikega podatkovja za družbeno enakost. Proučili bodo potencialno pozitivne in negativne učinke velikega podatkovja na prebivalstvo z nižjimi dohodki.
“
Companies
such as financial institutions, online and brick and mortar retailers, lead
generators, and service providers may use big data in the following ways:
– To
reward loyal customers with better customer service or shorter wait times.
reward loyal customers with better customer service or shorter wait times.
– To
offer different prices or discounts to different consumers. For example, a
financial institution may offer a consumer a discounted mortgage rate if that
consumer has a checking, savings, credit card, and retirement account with a
competitor.
offer different prices or discounts to different consumers. For example, a
financial institution may offer a consumer a discounted mortgage rate if that
consumer has a checking, savings, credit card, and retirement account with a
competitor.
– To
tailor advertising for financial products. For example, high-income consumers may
receive offers for “gold level” credit cards and low-income consumers may
receive offers for subprime credit cards.
tailor advertising for financial products. For example, high-income consumers may
receive offers for “gold level” credit cards and low-income consumers may
receive offers for subprime credit cards.
– To
assess credit risks of particular populations. For example, some commentators
have highlighted the use of unregulated “aggregate scoring models” that assess
credit risks, not based on the credit characteristics of individual consumers,
but on the aggregate credit characteristics of groups of consumers who shop at
certain stores.
assess credit risks of particular populations. For example, some commentators
have highlighted the use of unregulated “aggregate scoring models” that assess
credit risks, not based on the credit characteristics of individual consumers,
but on the aggregate credit characteristics of groups of consumers who shop at
certain stores.
“
