We're on the verge of a new era in artificial intelligence and machine learning.
Artificial intelligence (AI) is on the rise, and with it comes great power.
AI will have a significant impact in all aspects of life, from work to healthcare to education – it can even reshape government policy.
The ethical implications of AI are profound too: who should be controlling these powerful tools? How do we ensure fairness for everyone affected?
As we move from AI theory into practice, there are still some big questions that need answering – How can we scale the technology? And how does it help build ethical artificial intelligence with a sense of fairness?
MLOps has the potential to answer these questions by providing an ethical framework for the end-to-end machine learning lifecycle covering all aspects of data science.
What are artificial intelligence and machine learning?
AI is an emerging technology that uses computers to perform tasks previously thought of as requiring human intelligence.
AI can handle enormous quantities of data in a manner that humans cannot, but it's not just about handling large datasets; sometimes, we need are machines with enough knowledge and experience so they can make decisions like humans. To enable this objective, a significant amount of data is needed to train them.
Machine learning (ML) is a powerful tool for enabling machines to learn and make decisions just like humans do. It's an offshoot of artificial intelligence, which means we can teach computers how they should behave by showing them data without having to program anything explicitly.
So, what are machine learning algorithms?
Machine learning algorithms are programs that modify themselves to perform better as they get exposed to new data, and this could be a computer program, an app, or a website feature.
The term learning in the phrase machine-learning refers not just their ability to change how it deals with new information, but also to what has happened before, much like how people learn from the past experience too.
If you are keen to dive deep into machine learning, a good starting point will be looking at the top 10 machine learning algorithms.
Applications of machine learning
The applications of machine learning are limitless and are becoming more and more mainstream in many fields, from finance to healthcare. The wide range of applications of machine learning will continue to be an important topic in the future.
Some real-world applications of machine learning include:
#1. Healthcare
AI is used in health care in many ways, such as predicting when patients need to get admitted to the hospital, which patients would most benefit from seeing a physician sooner than later, and establishing better treatment plans by analyzing the symptoms from current treatments.
#2. Finance
Applications in finance include sentiment analysis that can predict stock prices based on sentiments of the investors, such as fear or greed, development of new financial products and services, maximizing returns by detecting risks or patterns to manage risks in the financial products, and so on.
#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 help improve user experience and many more.
#4. Utilities
Machine learning solutions can be applied from outage detection and forecasting to optimizing utility networks. It can also be applied as an energy-saving solution for homes with smart meters or grid management during times when renewable resources are added into the mix - all while predicting peak demand.
#5. Agriculture
Machine learning can be used to predict crop yield based on factors such as climate, soil, and moisture. It can also detect early signs of infestation by using computer vision systems for weed control or identifying insect damage too.
And there are many more applications of AI, and it continues to expand.
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.
Some of the key challenges with machine learning algorithms include:
#1. Ethical issues
Ethical issues may arise while using artificial intelligence and machine learning.
One ethical problem that isn't discussed very often is data bias in algorithms, which can lead to more skewed outcomes; and also bias in individuals who build the algorithms.
Another issue is the way companies collect customer data, leading to privacy concerns. Companies might employ techniques such as facial recognition or voice analysis to track customers' habits.
There are also other ethical questions such as, how do we balance privacy with the greater good?
For example, transparency versus privacy - should someone's personal information always remain private or does that go against more important goals like helping the greater good by being transparent about the usage of customer data (to provide better services)?
What happens if an algorithm picks up something unknown/unclear outside human comprehension level instead of having humans involved at every stage of the decision-making process? Also, who will take responsibility if things crop up despite best efforts?
It's not just about what an algorithm determines to be profitable; it must consider social responsibility factors like environmental impact.
The more data points, the better the decision-making process is to determine which stocks will perform well or poorly in financial markets - such as macroeconomic indicators or corporate announcements. All of these could be based on past performance records without regard (or consideration) towards how those actions may negatively affect people.
These questions and many more need attention. These ethical issues can potentially impact the company’s bottom-line and reputation.
#2. Black box
Another major issue in AI is that there are still many black boxes, which makes it difficult to understand how these algorithms work and what influenced the predictions.
The lack of transparency in AI systems has created major issues for both humans and machines.
The algorithms that are black box make it difficult to predict what will happen when they change or update; we cannot understand why certain decisions were made by the system even if those actions have negative consequences, like discrimination against certain groups and mistakes. Also, accidental biases or errors may cause harm to innocent people.
#3. Operationalization of models
There are challenges with the operationalization of models.
Operationalizing a machine learning model is the process of implementing the models in production that can be executed and used for making predictions and other business decisions.
However, this process is not without its flaws: some models require a large number of datasets for training, which may not always be feasible; sometimes more than one model may need to be trained to improve accuracy; and finally, there are also problems with overfitting.
Also, most of the models in the experimentation phase do not go into production due to complexities in the deployment of models.
Some of the other key factors 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 the development teams and the teams that implement models into production.
MLOps has the potential to address all these challenges encountered when building and implementing the machine learning models.
How can MLOps help?
Before looking into how machine learning operations (MLOps) can help address the issues highlighted above, let’s first understand what it is.
What is MLOps?
MLOps includes integrating people, processes, practices, and technologies that automate the deployment, monitoring, and management of machine learning models into production that is scalable, fully governed, and provides measurable business value.
MLOps is a term that combines machine learning in one case and data engineering in the other, along with the operationalization of projects.
Now let’s look at how MLOps helps in creating a trusted and ethical AI system:
#1. Risk mitigation
MLOps plays an important role and is crucial for any team with one or more models in production, as we need continuous performance monitoring and adjustments to ensure models are performing as expected.
MLOps becomes very important for risk assessment and mitigation of risks. It enables organizations to identify and minimize the risks associated with deploying ML models by providing secure and reliable operations.
Risk assessment
When it comes to machine learning algorithms, the level of risk can vary considerably.
For example, risks can be very low when the recommendation engine is used to recommend movies compared to a model used for predicting the repayment capability of an individual, determining whether to approve or reject a loan.
So, it is important that the models are monitored regularly and adjusted based on the adverse impact on business.
We need to take a risk-based approach when using the models in production.
Machine learning models need to be assessed for any risks in production, such as the unavailability of a model for a certain period, giving an incorrect prediction for a given data, decreasing accuracy over time, or the inability to maintain the models due to lack of skills/talent.
When you go from one or a handful of models in production to tens, hundreds, and even thousands that have a positive business impact, it is important to have an MLOps discipline.
MLOps discipline helps in:
- Auto-scaling and enabling models to run in production with high-scale data to train a large number of models
- Keeping track of code and data versioning
- Comparing the models that are tuned to the models in production
- Ensuring that the model performance is 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 using them with their data for better business outcomes.
As organizations grow, they also start to use more and different types of models. As the number of these models increases, it might become too much for one person or team to manage them all.
A robust MLOps discipline is needed so that we have a mature process. Otherwise, we may end up with tens, hundreds, and thousands of models running amok because no one knows how the model was built and tested, what model would be best suited for a given problem and how it will impact the outcomes and also the social and ethical implication of the decisions.
MLOps helps to ensure that the models are performing as they are meant to.
#3. Responsible AI (RAI)
Responsible AI means designing and building systems that act in ways that are responsible toward human beings. It includes systems that are responsible, trustworthy, reliable, robust, accountable, and transparent.
MLOps provides organizations with responsible machine learning practices to ensure that they are not blindly implementing these technologies into their business models without knowing their consequences, so it benefits everyone involved.
The two guiding principles of Responsible AI are: ethical and explainable AI.
From an ethical perspective, AI should be fair and inclusive, accountable for its decisions, and not discriminate or hinder different races, disabilities, and backgrounds.
Explainability is about ensuring that the AI systems can justify their decisions. It helps data scientists and business decision-makers to build AI systems and explain the outcomes of their decisions and the impact. It also ensures that company policies, industry standards, and government regulations are adhered to.
Responsible AI is not only about ensuring end-to-end model management but also protecting against biases and other risks associated with machine learning models.
Two recent examples where the organizations were negatively impacted due to lack of responsible AI include:
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Revenue NSW unlawfully collected debts using an automated system
NSW Ombudsman has raised concerns about the Revenue NSW’s use of Robodebt-style artificial intelligence to garnishee the bank accounts of vulnerable people with overdue fines, which were deemed unlawful. This resulted in $11.5 million in fines unlawfully taken from 1 million Aussie bank accounts.
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UK privacy watchdog wants to fine Clearview AI $31m
Britain's independent Information Commissioner's Office (ICO) wants to fine Clearview AI in excess of £17 million (A$31.7 million) for unlawfully scraping users' data from the internet for biometric facial recognition purposes.
Responsible AI (RAI) practices are very important for organizations to ensure AI systems are responsible, ethical, and legal.
Conclusion
MLOps is the new discipline of machine learning that will make the machine learning models more ethical, scalable, and explainable. It also provides well-defined frameworks for end-to-end model management, from data collection to operationalizing an end product with oversight in place.
It is the next evolution of machine learning and will play a critical role in creating a trusted and ethical AI system as they go from theory to practice.
