Customer Service

Police will need new ways of monitoring the community for crimes. Police agencies don’t have the manpower to deal with the move towards cities for the majority of the population.  Because of that, police agencies, data governance experts, and the majors of smart cities are increasingly using AI for physical security and public safety. While ‘smart cities’ continue to emerge globally, ‘citizen sensing’ systems will become commonplace and provide governments the much-needed technological edge to ensure the safety of their people and assets. 

By marrying the power of our flagship framework, xpresso.ai, with a decade of wholesome experience, we have ensured AI projects are delivered in a timely and robust manner, and organizations achieve a successful AI transformation journey with our systematic approach towards enterprise AI and MLOps solutions.  

Our approach to an enterprise MLOps journey begins with a robust data intake and elaborate analysis to frame the problem areas.  The second part includes applying an engineering mindset, identify enablers required for AI initiatives to be successful at enterprise scale, and preparing cognitive models. Finally, these models are deployed and managed throughout the lifecycle of the solution. Throughout the lifecycle, xpresso.ai pre-built frameworks are used to push the required transformations ahead. Coupled with a robust uptime for applications we use and near-zero chances of interruption, this means unwavering support all the way for customers. So, what would have typically taken months to deliver can be developed and deployed in weeks, and most importantly — at a fraction of the cost.

Challenges

Use of AI and its implications isn’t straightforward: A survey by the Boston Consulting Group reveals that:

• People in emerging markets tend to be more positive about government use of AI. 
• Government use of AI finds more support in emerging markets than mature economies.
• Support for government use of AI correlates moderately with trust in government. 
• Trust in institutions is essential if governments are to gain the support needed to roll out AI capabilities. 
• Less-developed economies and countries that have higher reported or perceived levels of corruption also tend to be more supportive of the use of AI. 
• Citizens may prefer AI-based decision-making over human decision-making if there is low confidence in the government machinery.

Building or rebuilding trust in government institutions can be a challenge: The survey also revealed that significant ethical issues had not yet been resolved (32%), the possibility of bias and discrimination while using AI (25%), perceived lack of transparency in decision making (31%), the accuracy of the results and analysis (25%), and the capability of the public sector to use AI (27%). 

Bias and discrimination can influence AI adoption: AI can effectively reduce cognitive and social biases that influence human decision-making — algorithms can minimize both noise and bias from decision making as algorithms can weigh all inputs exactly as instructed. However, as AI learns from primarily human activity-driven data, it includes human bias. Hence, models might be created that magnify and perpetuate pre-existing biases. 

Creating models bias-free models is a technical challenge: For black-box models, neural networks, and deep learning, it may be impossible to understand how a recommendation or decision was derived. Thus, it can also be impossible to meet traditional government requirements for explainability, transparency, and the ability to be scrutinized.

xpresso.ai – Cognitive Solutions

• With the proliferation of social media, opinions on almost any issue under the sun stream in every time. These issues have the potential to snowball and render any situation critical if not checked and investigated adequately by the government. 
• The Ministry of Communication and Information from a nation in SE Asia wanted to gain access to their citizens’ voice in quasi-real-time, understand top trending topics, bring out citizen sentiment, and send an immediate alert to concerned departments before they grew viral and sparked law and order threats.
• The Citizen Sensing solution we developed listens to the voice of the citizens in quasi-real-time, detects and understands trending topics in different sectors, uncovers the underlying thoughts, and performs public sentiment analysis. 
• By using xpresso.ai, the citizens’ voice is classified into different expression types (advocacy, suggestion, complaint, etc.) These were correlated to appropriate aspects and entities. The application tracked topics, activities, events and analyzed social conversations or statements by extracting and quantifying the sentiments, expressions, and emotions attached to them.

Solution Approach

Use Case Discovery

We actively engaged with our client to capture the business requirements while observing the problem. In the case of deriving credit scores, traditional model development methods are lengthy, tedious, and often prone to human bias, lack data accuracy in the absence of proper data collection, data models, and data cleansing. We can effectively identify the relevant datasets and formulate a use case-based approach that would solve the business problem or improve/predict actionable insights to mitigate the problem.

We can assist in setting up the required infrastructure – xpressso.ai framework provides out-of-the-box development platforms, and all functionalities can be accessed using a Jupyter Notebook — ensuring zero-delay and plug-and-play availability of high-end hardware. Development images configured based on predefined templates can be installed on-premises or in a development VM within the xpresso.ai infrastructure. This enables authentication using LDAP, seamless project setup using Bitbucket, Jenkins, and Docker (ensuring build and deployment without software compatibility issues).  The project can be started seamlessly with the relevant environments, which are subsequently created automatically. 

The framework made available by xpresso.ai leverages the latest ML and DL tools while preparing models and includes Pachyderm-based data versioning, deployment using a Kubernetes orchestration system, Kubeflow and Spark-based ML and DL build and deployment, Istio-based service mesh enabled microservice architecture, and ELK based monitoring capability; contributing to reduction in latency time.

By combining NLP and ML techniques, a sentiment analysis system for text analysis can read different texts from variegated sources, stack them against a sentiment library, and assign weighted sentiment scores to the entities, topics, themes, and categories within a sentence or phrase. We tailored our domain knowledge based on regional characteristics. Predefined categories were created to classify citizen feedback from social media.

Data Engineering

xpresso.ai’s MLOps framework has different data adapters available through a common catalog of services which simplifies interoperability and scalability concerns, enables APIs, and abstracts all the technical complexities from the service consumer. This allows establishing high-end Alluxio and Presto-based rapid, inexpensive data connectivity and data collection from diverse sources (available in structured, unstructured, and streaming formats) coming in at a high velocity and in huge volumes. 

All the data sources are funneled into the data storage layer after proper validation and cleansing. The storage landscape with different storage types and extreme flexibility is built-in to manipulate, filter, select, and co-relate different data formats.

Infrastructure and MLOps Automation

The details collected, project code, data preparation workflows, and models can be easily versioned in a repository (Bitbucket, Git, etc.), and data sets can be versioned through on-premise /cloud storage. These can be added as exploratory variables by using two excellent features of xpresso.ai: 

1. Data Connectivity Marketplace libraries
2. Data Versioning

The attributes obtained are used for categorization (employing Pachyderm-based data versioning) and then performing univariate, bi-variate, and Bag of Words analysis — for both structured and unstructured datasets through xpresso Exploratory Data Analysis (Data and Statistical Analysis).  Different datasets and their different versions can be easily controlled and stored into xpresso Data Model (XDM)-enabled data store that enabled easy retrieval and storage of datasets/ files into internal XDM. 

xpresso.ai MLOps automation allows creating pipelines, train with as much data and as accurately as possible, fastest time to inference, with the ability to rapidly retrain. The xpresso Data Pipeline Management (Rapid Model Training and Experimentation) uses Kubeflow-enabled pipelines. Thus, multiple experiments using different models and datasets can be created, tested, paused, and restarted to gain better insight.

The government could extend control over any significant temporal and/or spatial spikes in negative sentiments and emotions such as fear and anger. This resulted in multi-fold operational efficiency as efficient governance decisions could be taken quickly, and preparedness for mitigating possible law and order lapses was ensured.