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How can AI Solve “Modern Slavery”?



In the past month, some may have read an article in The Atlantic, My Family’s Slave written by Alex Tizon. The article tells the story of Eudocia Tomas Pulido, a “slave” who has worked for Tizon’s family for over 56 years without him knowing about her real status (the family calls her Lola).

Lola started working for Tizon’s family since she was 18 years old. She was a “gift” that Tizon’s grandfather gave to his mother. She brought this gift along with her when the family moved to the United States. Every day, she would wake up earlier and went to bed later than everyone, working strenuously without any complaints. She may seem like a helper in the home but one difference is that she does not have a salary, she works as a slave, she works for free. The slavery era may seem to have passed, but in reality, “modern slaves” are hidden all over the world.

They come in forms of helpers with no pay like Lola and those with worst living conditions who receive extremely low wages such as labor on fishing vessels. Look around and the items we use every day like clothes, shoes, or chocolates we eat, accessories we wear may come from slave labor.

Although awareness has increased as seen in government policies, the rising demand for goods and decrease price trend pushes manufacturers to settle with limited budgets (or it may just be some selfish human). Hence, 20.9 million forced labor exists worldwide as stated in a survey in 2012 by the International Labour Organization.

Multi-national organizations are aware of the problem. They realize that this is not only about rightfulness and moral, but it also means credibility (imagine buying a football from a brand that underpays child labor). However, “detecting” slave labor is not simple.

How can AI help solve such problem?

SAP Ariba in California (previously Ariba and acquired by SAP, software manufacturer from Germany in 2012) intends to use data to solve modern slavery issues. They built a risk analysis software wherein hundreds of data points are analyzed to detect which area in the production line has a probability of using slaves. Data are collected from different sources such as the labor themselves, freelance auditors who regularly audits labor, and press reports (these reports may come from news sources like leaking information or field journalists).

These figures will be evaluated with information from NGO like Made in a Free World who work to ensure benefits and proper wage for labors all around the world. Statistics in some countries such as Bangladesh or Ghana may indicate that these countries “risk” having a higher number of slave labor. These are samples of information that will also be incorporated. Interestingly, this type of data may have been previously explored. There have been hotlines where labors can report misconducts but the problem was that the data were scattered and not integrated. Therefore, organizations (or related departments) were not able to systematically evaluate the risk or problem.

SAP Ariba will help unlock these setbacks. In the past, when affected labor reported violations they may not receive immediate responses. Now, with real-time analysis, the response will be quicker as data will be used to evaluate the risk promptly without having to wait for end quarter or year. SAP Ariba plans to open the API (Application Program Interface) to companies and other departments so they can further enhance the system. Although this may not solve the root cause and can only collects numeric data for AI analysis (which is more precise than human), numerous intangible data is being processed for easier understanding and implications. This marks hope for more intense problem-solving attempts in the future.