Cut down technological colonialism.


SwissCognitive Guest Blogger: Ethan Millar – “Why should you care about Explainable AI (XAI) and Responsible AI (RAI)?”



Mitigating risks head-on is mission-critical even before we understand how AI impacts enterprises. This learning curve is related to ‘Explainable AI’ & ‘Responsible AI’ which raise concerns regarding accountability, transparency, and algorithm bias. Get on the bandwagon of these two concepts that have triggered the latest interest of business leaders. We raise the curtains on the significance of (XAI) and (RAI) which have stormed into the global scene.

We touch upon the following points related to AI and your investment in its growing influence.

Table of Contents

  • Defining the 2 models: Explainable AI & Responsible AI
  • Business benefit for continuous model evaluation
  • How does the XAI concept work?
  • Why is XAI the basis for RAI?
  • Key drivers for their rise in business
  • Mitigating technological colonialism and challenges
  • Conclusion with ethical considerations

Defining the 2 models: Explainable AI & Responsible AI


Explainable Artificial Intelligence is a series of processes to generate data for interpretation. Both decisions and predictions are understood better with this system. It is in stark contrast to the conventional learning models referred to as ‘black boxes.’ It was earlier difficult to understand how these black boxes arrived at outcomes or conclusions with emerging data.

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The explainable AI model is a step ahead. It is transparent and provides clarity or specific nature of the outcome. The results are more trustworthy and acceptable to all stakeholders involved in decision-making. Additionally, it sets the pace for tapping Responsible AI which is different compared to regular AI systems.


Responsible AI works on a bigger principle aimed at ethical methods and best practices. Primarily it addresses any bias or concerns about data security and privacy. All responsible practices are integrated to mitigate risks of technological colonialism of any kind in the organization.

Business benefit for continuous model evaluation

There is no need for blind trust with AI for insights, recommendations, or predictions unless there is a model that provides continuous benefit. The new model is more concise and produces better learning in the AI environment. It can be used by any industry with preparedness and cost-effectiveness for the following:

  • Building trust with users
  • Conforming to legal requirements
  • Ethical motives and justification
  • Provision for actionable data insights

Where do you start from? 

If your company already is familiar with the AI landscape, it is easier to adapt to XAI and RAI. For example, the best department to deploy them will be in marketing. When you see the credibility of the brand and its resonance with the consumer, the results are clear. AI models that build trust with the users and are ethical are more visible and popular.

A potential consumer is most interested in the protection of the data and that it should be used responsibly by the company. So, when personalization or optimization occurs, they are heavily guarded. AI systems are effective when XAI can reveal the real reason why a product is recommended to the consumer. Marketers can also fine-tune their campaigns and strategies to refine recommendations.

When regulatory requirements are considered, the framework for benefits is enlarged. It gives wholesome transparency even in the decision-making process. There is no damage to a company as ethical considerations are in place for marketing campaigns. There are fewer risks of penalties as the system behaves responsibly.

All benefits do not translate into profits or revenues. Responsibility works even for reputation and credibility in the global market. This is how even banks can raise their equity and serve consumers with compliance.

How does the XAI concept work?

Why should you care about Explainable AI (XAI) and Responsible AI (RAI)_2

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The fact that this model works responsibly, is a boon for organizations. It is the very basis for RAI for federated learning outcomes. It needs to fulfill 4 requirements: privacy concerns, security, interpretability, and fairness in the data sets. As black boxes, algorithms tend to identify patterns for learning the user’s intent. This deep learning process is wired and quite similar to the neuron network in the human brain.

To give an example of deep learning, take the case of financial services. They require a credit score for loan approvals. If a bad algorithm crops up and gives. A bad recommendation, there is no physical harm. In some other eventuality, the consequences can be severe.

In the same manner in the healthcare sector, deep learning is critical. When the AI diagnoses a cancer screening the doctor should be able to extract the right meaning for treatment. AI can predict anything with or without accuracy. If there is a false negative the patient may not get the right treatment to save the life. All treatments are expensive and Explicability AI is much more useful. It allows oncologists and radiologists to interpret the data for diagnosis more accurately.

Why is XAI the basis for RAI?

To appreciate the role of XAI and its basis for RAI, we need to understand its principles. The National Institute of Standards in the USA (NIST) has defined 4 principles. They tackle the issues of colonization of technology as well.

XAI should be able to:

  1. Provide evidence related to support and reasoning of the output from data analysis. The user should understand the output for suitable action. As in the case of a financial loan it should give the correct recommendation to give the loan or reject an application.
  2. Accuracy for explanation marks the next important principle. The process of the AI system should explain the outcome. It eliminates bad practices and the possibility of errors.
  3. AI systems operate with limitations. If a model is designed for a specific purpose, it should not exceed that limit. There is no confidence in the results of the process.
  4. All explanations should be available and understood by the user.

The German Federal Financial Supervisory Authority (GFFSA) has asked their banks to choose a more complex model that comes with better trust. Financial watchdogs are also asking them to install machine models that can keep humans in the ‘loop’ as they share the analysis. Solicited feedback in the USA target XAI to mitigate risks.

Key drivers for their rise in business

The current boom in XAI and RAI is bound to erase the scare of black boxes. We are in an era where these two concepts have catapulted the models to perform better. As transparency and trust are vital for data, organizations are willing to try out these techniques. Banks and academic institutions are already helping with the research related to the new models. It will now ensure that lack of transparency will not be a hurdle anymore.

There are more chances of relationships being understood. Variables of data sets are likely to provide better diagnoses. Imagine how safe it would be if there was no information leakage. It matters in the case of patient records or fanatical credit scores of customers.

Another big reason for the interest in AI models is to develop more convincing business cases. It will allow other organizations to deploy them for their benefit. Credibility works wonders in all areas and scaling XAI is on the anvil for next-gen of business groups.

The successful merger of regulatory compliance, upgrades, patches, and protection makes a water-tight case to prioritize both XAI and RAI successfully.

Mitigating technological colonialism and challenges

Technological Colonialism refers to large-scale enterprises that dominate the digital terrain in specific geographic. When they use AI business models, they grab the processes of deployment, development, and advanced systems. It marginalizes the identities and disparities. This colonization results in ethical concerns related to technology, equity, and its applications.

It is now embedded in the AI systems. The desired output reflects only a particular geographic region. The interpretation and context of the data is skewed. It may even come in a language suitable to one region and not the others. Therefore, expecting accurate results is difficult. AI systems being developed now have to address them to reduce the challenges of technological colonization.

Caution and responsibility are paramount to use AI more diligently. If your organizational goals have to be reached with best practices, the implementation should involve:

  • Facilitate the explanation of variables for predictions. Giving steps to conclude is desirable.
  • Will the model behave similarly in the future? How can the organization match its strengths and weaknesses?
  • Users prefer intuitive models that give simple results. With the technical knowledge they possess, they are the target audience for these operative models.

Conclusion with ethical considerations

It would be interesting to learn about the new spectrum of models that range from decision trees and move along with deep neural networks. Some banks are already choosing AutoML solutions. They all have diverse explain ability trends. These guardrails used in the early stages push for quicker results.

There are challenges but they can be considered in the context of running an ethical business globally. As leaders adapt to the right methods and processes, they pave the way for others to use the latest AI systems.

Embracing the two new concepts is a moral compass that can be a strategic move toward trust. Both bias control and cultural imposition are challenges that need to be met head-on for better influence. There is no doubt that this field of research is valuable for the future of socio-economic development not only business. It harnesses collective intelligence offering a roadmap for the future.

About the Author:

Ethan MillarEthan Millar is a technical writer at Aegis Softtech especially for computer programming like artificial intelligence, emergency technology, Big Data, data analytics, and CRM for more than 8 years. Also, have basic knowledge of AI and technology are vast fields with numerous experts contributing to various aspects of research, development, and application.