How MLops can help build trusted and ethical AI

We are on the verge of a new era in artificial intelligence and machine learning. It’s an exciting time for everyone, with AI assisting us in making faster decisions and enabling us to work smarter.

AI will have a significant impact on how we work, live, and interact with one another. It will reshape every aspect of society—from government to healthcare to education—and create new ways for people to make a living. 

However, the ethical and social implications of artificial intelligence can also be profound. 

As we move from AI theory into practice, there are still some big questions that need answering – How can we scale AI? And how can it help build trusted ethical AI that is fair-minded? 

MLOps has the potential to answer these questions by providing an ethical framework for all aspects of data science.

What is Artificial Intelligence (AI) and Machine Learning (ML)?

AI is an emerging technology that involves using computers to perform tasks that were previously assumed to need human intelligence.

AI can handle enormous quantities of data in a manner that humans cannot. The objective of AI is to be able to do things like spot patterns, make judgments, and execute decisions comparable to humans. To enable this, we’ll need a lot of data to train algorithms.

Machine learning (ML) is a type of Artificial Intelligence that aims to teach computers how to learn and make decisions as humans do. It’s a branch of computer science that enables us to teach computers how to learn from data without having to program them explicitly.

So, what are Machine Learning algorithms? 

Machine learning algorithms are programs that modify themselves to perform better as they get exposed to additional data. The term “learning” in the phrase “machine learning” refers to these programs’ ability to change how they deal with data over time, much as people can learn over time.

Applications of Machine learning

Applications of machine learning are becoming more and more mainstream in many fields from finance to healthcare.  Some real-world applications of machine learning include:

#1. Healthcare: 

AI is being used in healthcare in many ways such as predicting when patients will be admitted into the hospital, which patients would most benefit from seeing a physician sooner rather than later, establishing better treatment plans by analyzing the symptoms from current treatments.

 #2. Finance: 

Applications in finance include using sentiment analysis to predict stock prices based on investor sentiments such as fear or greed, for the development of new financial products and services, for maximizing returns by detecting risks or patterns to manage risks in the financial products.

#3. Retail: 

Machine learning has been used in retail applications like inventory control, targeted marketing campaigns, predicting customer behavior, product recommendations, and personalized online shopping that helps improve user experience.

#4. Utilities: 

Machine learning can be applied to tasks such as outage detection, billing forecasting, optimizing utility networks, predicting peak demand times, smart meter analytics for distribution planning, grid management, or renewable energy integration.

#5. Agriculture: 

Machine learning can be used to predict crop yield based on factors like climate, soil, and moisture, for detecting insect damage to crops, to detect early signs of infestation, using computer vision systems for weed control.

Key challenges with Machine Learning algorithms? 

While AI can help us make faster decisions and enable us to work smarter, there are key challenges during the development and implementation of AI/ML models. 

If these challenges are not addressed, they can lead to ethical and social implications for the individuals, businesses, or community to whom the model outcomes are used.

Below are the key challenges with Machine Learning algorithms:

#1. Ethical Issues:

Ethical issues can arise when using artificial intelligence and machine learning. 

One ethical issue that isn’t talked about very often is bias in the machine learning introduced by both data bias and bias in individuals who build the algorithms.

Another issue can be privacy concerns related to customer data collection by companies that employ techniques such as facial recognition or voice analysis to track customers’ habits.

There are other ethical questions around, how do we balance privacy with the greater good? how do we balance privacy with transparency? what if an algorithm picks up on something that is not known or understood by humans? Is it fair for one customer to pay more than another based on their usage patterns? who will be responsible for decisions made by machines? 

These questions and many more need attention. These ethical issues have the potential to impact companys’ reputations as well. 

If an algorithm determines which stocks will be profitable (based on data points such as macro-economic indicators or corporate announcements) but fails to take into account social responsibility factors like environmental impact due to production costs, then it could lead to unethical decisions.

#2. Blackbox:

Another major issue with AI is that there are still many black boxes and gaps in our understanding of how these algorithms work. This means not only can we not predict what will happen when an algorithm changes or updates, but we also cannot fully understand why certain decisions were made by the system. 

This lack of transparency makes it difficult to know what could go wrong and leaves vulnerable to mistakes such as discrimination against certain groups, accidental or intentional biases, and errors, thus raising the question of the trustworthiness of the AI systems.

#3. Operationalization of models:

Also, there are challenges with operationalization of models.

Operationalizing a machine learning model is the process of turning it into an algorithm that can be executed and used to make predictions. 

However, this process is not without its flaws: some models require significant datasets for training, which may not always be available; sometimes more than one model needs to be trained to improve accuracy; and finally, there are problems with overfitting. 

Also, it is estimated that more than 80% of the models in the experimentation phase are not going into production due to complexities in the deployment of models. 

Some of the other key issues that prevent operationalizing machine learning models or failures in production include infrastructure requirements, constant data changes, rigorous testing requirements, continuous monitoring needs, and lack of collaboration between development teams and teams that implement models into production.

MLOps is able to address the challenges that have been highlighted in Machine Learning models.

How can MLOps help?

Before looking into how MLOps can help address the issues highlighted above, let’s first understand what is MLOps.

What is MLOps?

MLOps includes the integration of people, processes, practices, and technologies that automate the deployment, monitoring, and management of machine learning models into production, that are scalable, fully governed and provide measurable business value.

MLOps is a term that refers to combining a field of study (machine learning in one case, data engineering in the other) along with the operationalization of projects.

Now let’s look at how MLOps is helping in creating trusted and ethical AI systems:

#1. Risk Mitigation:

The need for MLOps is crucial to any team that has one or more models in production, as continuous performance monitoring and adjustments are required to ensure models are performing as per the expectation.

MLOps becomes key in risk assessment and mitigation of risks. 

MLOps enables organizations to minimize the risks associated with deploying ML models by providing secure and reliable operations.

Risk Assessment

When it comes to machine learning algorithms, the risk levels can vary considerably.

For example, risks are very low when the recommendation engine is used to recommend movies as compared to a model that is used for approving or rejecting a loan for a customer. 

So, it is important that the models are monitored regularly and adjusted based on the adverse impact to the business. A risk-based approach needs to be taken when using the models in production.

Machine learning models need to be assessed for any risks in production such as model not being available for a certain period, model giving an incorrect prediction for a given data, model accuracy decreasing over time, or lack of skills to maintain the models.

Going from one or a handful of models in production to tens, hundreds, or thousands that have a positive business and ethical impact requires MLOps discipline.

MLOps discipline helps in:

  • Auto-Scaling and enables to run models in production with high-scale data and to train a large number of models.
  • Keeping track of code and data versioning, 
  • Comparing the models that are tuned to that of the models in production,
  • Ensuring model performance are not degrading in production over time

#2. Enterprise Scaling:

As more and more algorithms are developed to solve business problems, organizations have started building those models for use with their data for better business outcomes. 

As organizations grow, they start to use more and different models. As the number of these models increase it can become too much for one person or team in charge of managing them all.

A robust MLOps discipline across the company helps to have a mature process so we don’t end up with tens, hundreds and thousands of models running amok because no one knows how the model was built, what model would be best suited for a given problem and what impact the model has on the outcomes. MLOps helps to ensure that the models are performing as they are meant to.

#3. Responsible AI:

Another most important aspect of MLOps is, it helps with the responsible use of machine learning, referred to as Responsible AI.

Responsible AI means designing and building systems that act in ways that are responsible for human beings. It includes systems that are responsible, trustworthy, reliable, robust, accountable, and transparent.

Responsible AI is guided by two perspectives: ethical and explainable AI.

From an ethical perspective, AI should be fair and inclusive, be accountable for its decisions, and not discriminate or hinder different races, disabilities, or backgrounds.

Explainability aids data scientists and business decision-makers in ensuring that AI systems can justify their decisions and how they come to their conclusions.

This also ensures that the company’s policies, industry standards, and government regulations are followed.

Responsible AI is about ensuring that the end-to-end model management protects against biases, lack of transparency, and other risks associated with machine learning models.

MLOps is a discipline that provides well-defined frameworks, standards, processes, practices, and technologies for the end-to-end model management, from data collection down to operationalization and oversight for the responsible use of AI.


MLOps helps make machine learning models more ethical, scalable, and explainable. It is a discipline that provides well-defined frameworks, standards, processes, practices, and technologies for the end-to-end model management from data collection down to operationalization and oversight of ML models for responsible use of AI. It enables a mature process, so machine learning algorithms don’t run amok with little explanation of how they work or what model would be best suited for a given problem. It ensures that machine learning models are performing as intended and in a responsible way. 

It is the next evolution of machine learning, and it will be critical for creating a trusted and ethical AI system as they go from theory to practice. 


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