Data is a double-edged sword. We need the numbers to help government and institutional leaders develop and implement programs. On the other hand, a narrow focus on the numbers trivializes the humans they represent.
In today’s technology-driven world, human data consists of text instead of numbers, which hinders the development of a more responsive society.
AI and machine learning could improve so many aspects of our daily lives. In personal finance, mobile check deposits and fraud prevention alerts simplify banking while protecting consumers. Automated transcription for voice-to-text capabilities makes mobile phones more accessible. Even online shopping leverages AI with product recommendations based on the user’s history.
With all of AI’s capabilities, citizens could also become the sensors for a more responsive society.
Can Machine Learning Make Data More Human?
Citibeats’ machine learning technology gives a human voice to data.
First, it identifies insights and trends by collecting information—in this case, text—across multiple networks in any language. The text is all-inclusive from social media channels (Facebook, Twitter, Instagram, LinkedIn), chat forums, blogs, and other relevant platforms where citizens express opinions.
Next, proprietary machine learning algorithms interpret the data by sorting it and structuring it into specific categories. This allows decision-makers to analyze relevant information as a unified voice.
Challenges of Human Data
Using human data raises privacy concerns. Apart from that, complex challenges arise with people-based research:
- Acquiring quality data
- Being inclusive
- Avoiding individual profiling
- Sorting out fake news
- Counterbalancing online hate speech
At Citibeats, we implement practices to help offset challenges:
- Anonymizing data
- Aggregating data to provide insights on cohorts of citizens, minimizing the effects of profiling and discriminatory skews
- Using an advanced categorization system to sift through fake news and bots
- Supporting projects that use our system to identify and counteract hate speech
AI Text Analytics at Work
Through AI text analytics, the Citibeats platform has increased the speed at which data translates into action:
- 5,000 damaged infrastructure reports from the 2018 Japan floods were extracted 21 days earlier for more efficient prioritization of necessary repairs.
- 7,000 consumer complaints in Kenya were analyzed 45 days earlier to identify potential bank scams and initiate consumer protection investigations.
- 3,000,000 citizen voices were reported 60 days earlier to quantify public feedback on social issues and allocate the budget according to the priorities of citizens.
Let’s take a closer look at how this played out and made an impact.
Social Policy Change in the United Kingdom
The Milton Keynes Council—the local council of the Borough of Milton Keynes in Buckinghamshire, England—was shifting to a data-driven policy-making approach. To that end, it developed MK Insight as a singular hub to collect open data. Council members wanted to include social media in the data set, but they were discouraged by its messiness and constant change. How do you pull actual data from chaos?
This is where Citibeats came in, measuring real-world dialogue about pressing social issues: homelessness, drug abuse, and exclusion. Citizens became sensors, voicing the problems they saw around them.
In February 2018, the AI system reported a spike in dialogue about the effects of sub-zero temperatures on the sizable homeless population. The next three days of freezing weather conditions resulted in a national scandal covered by newspapers nationwide.
In this case, the citizens acted as a social early warning system, allowing MK Insight to tackle the issue with the help of grassroots organizations.
Financial Inclusion and Consumer Protection in Kenya
Kenya is a trailblazer in mobile money and digital credit. As financial services became more digital, so did consumer methods of reporting abuse and complaints. With thousands of complaints each day, manually processing and investigating concerns was nearly impossible. So how could banks and regulators use this valuable data to flag investigation-worthy issues in real time to protect consumers?
Financial Sector Deepening Kenya (FSD Kenya) teamed up with the Dignity and Debt Network to define a framework to qualify topics that warranted further investigation (scams, blacklisting, and unfair charges). From there, Citibeats’ AI was trained to recognize these topics and alert regulators when it detected red-flag patterns.
Not only did FSD Kenya report that the Citibeats alerts were 80% to 90% relevant for investigation-meriting issues, but it flagged consumer complaints 45 days earlier than before the technology’s implementation. The information was so valuable that FSD Kenya is leveraging a one-year complaint analysis to make humanized data recommendations to the country’s financial services industry.
So far, over 55 countries apply Citibeats’ technology to acquire civic feedback data for actionable insight. Over 70 million citizen voices have been represented in the decision-making process.
We’ve seen the impact in sustainable development, migration, vaccines, gender equality (public and private sectors), financial services, natural disaster response, and social policy. Our ultimate objective is to build a responsive society on a global scale, one voice at a time.