Keeping Machine Learning Algorithms fully Transparent

Today in so many industries or big brands, from manufacturing and life sciences to financial services and retail, we largely rely on algorithms to perform machine learning analysis. They are extremely useful for problem-solving and beneficial to enhance human expertise within an organization. But they are now in the news for several reasons – and regulation is on the horizon.

Gartner projects that four of the G7 countries will establish dedicated associations by 2023 to oversee artificial intelligence and ML design. It is important that we understand the logic and decision making processes of algorithms at every step.

The algorithm needs to be fully transparent in its decisions, easily validated, and monitored by a human expert. Machine learning tools should introduce full accountability that it evolves beyond “black box” solutions, and “the algorithm eliminates the easy excuse for me to do it!”

Put Bias in Its Place

The bias can begin as the initial data upload and review steps in the machine learning Algorithm. There are hundreds of parameters to keep in mind when preparing data, so it can often be challenging to strike a balance between removing bias and maintaining useful data.

For example, gender may be a useful parameter to identify specific disease risks or health perils. But in many other situations it is completely unacceptable to use gender if it introduces prejudice and in turn discrimination. Machine learning models will use essentially any parameter – such as gender – data set to which they have access. Users must understand the steps taken for the model to arrive at a specific conclusion.

Lifting the Curtain

Removing the complexity of the data science process will help users find and address bias faster – and will be able to better understand the expected accuracy and results of deploying a particular model.

Machine learning tools with built-in explanations allow users to show the rationale behind implementing ML to deal with a specific problem and ultimately justify the result. The first step toward this explainability will be the features in the Machine learning tool to enable visual inspection of data – alerting users to potential bias during preparation with the platform – and metrics on model accuracy and health. Those will include the ability to visualize what the model is doing.

In addition, ML platforms can take transparency by starting full user visibility, tracking each step through a consistent audit trail. Such tracking records how and when the data set has been imported, prepared, and manipulated. It also helps ensure compliance with national and industry regulations – such as the EU’s GDPR “right to clarify” – and helps consumers show transparency more effectively.

Another advantage here is to allow users to quickly repeat the same preparation and deployment steps, guaranteeing the same results from the same data – particularly important for achieving time capability on repetitive tasks. We are, for example, keen on replication and visibility to users in life sciences, particularly for ML, where it becomes an essential feature in areas such as clinical trials and drug discovery.



Model Accountability

There are many different model types that it can be a challenge to select and open or deploy the best model for a task. For example, deep neural network models are probably less transparent than possible methods, which typically operate in a more “honest” and transparent manner.

Many machine learning tools fall short here: they are fully automated, providing no opportunity to review and select the most appropriate model. This fact can help users to rapidly prepare data and deploy machine learning models, but it provides no possibility of visual inspection to identify data and model issues.

An effective ML platform should be able to help identify and advise on resolving potential bias in a model during the preparation phase. Then it needs to provide assistance through creation. In construction, it can visualize what the chosen model is doing and provide an accuracy matrix. Then, in deployment, it can evaluate the model’s certainty and provide alerts when the model needs to be retried.

Testing Procedures

While building greater visibility into data preparation and model deployment, we should look towards ML platforms incorporating test facilities. The user must be able to test new data sets and obtain model performance scores. This helps identify bias and make changes in the model accordingly.

During model deployment, the most effective platforms will also extract additional features from the data, which is difficult to help the user understand whether it is happening with the data at a granular level, beyond the most obvious insights.

The goal is to put power directly into users’ hands, enabling them to actively detect, visualize, and manipulate data at each stage, rather than simply handing them over to the Machine Learning tool and eliminating bias.

Driving the Ethics Debate Forward

The introduction of interpretability and augmented governance in ML platforms is an essential step towards the deployment of ethical machine learning, but we must go further.

Researchers and solution vendors take responsibility as ML teachers to inform users of usage and misuse in machine learning. We need to encourage businesses in the region to establish dedicated education programs on machine learning, including specific modules that cover ethics and bias. Those modules must explain how users can identify and in turn avoid danger.

Raising awareness in this manner will be an important step towards establishing confidence in sensitive AI and ML deployments such as medical diagnosis, financial decision making, and criminal punishment.
Also read: 5 Best Resource Capacity Planning Tools for Teams Time break open the black boxes

AI and machine learning provide truly boundless capability by which we work, learn and deal with problems across industries. Ensuring that these operations are performed in an open and fair manner is paramount to winning and maintaining both consumer and corporate trust in these applications.

There are really humble, honest algorithms that work for us and provide us with unbiased, clear predictions and provide consistent context, interpretability and accuracy insights.

Recent research suggests that 84% of CEOs agree that AI-based decisions should be trusted. The time is ripe to embrace AI and ML solutions with baked transparency.