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Introduction to the 4 Principles of the Responsible AI for Business Leaders

An overview on how to achieve the development and implement Responsible AI

Artificial Intelligence (AI) technology facilitates decisions that have far-reaching consequences for everyone, and that’s why we need a solid and shared standard that ensures AI is safe, trustworthy, and unbiased, as well as that AI and Machine Learning (ML) models are robust, explainable, ethical, and efficient.

The fight for social justice is front and center in the media, where there are many spotlights on possible causes of prejudice and discrimination situations that AI can cause.

Whoever develops and uses AI should do everything possible to eliminate prejudice (bias), explain how their technology makes decisions, and accept responsibility if AI becomes dishonest.

Business Leaders are called to understand better what responsible AI is or the framework to ensure that organizations understand and apply models that adhere to corporate governance principles like fairness, transparency, empathy, and, finally, robustness.

These concepts represent the 4 pillars of Responsible AI. Let’s explore them now:

Fairness

AI algorithms make decisions based on the available data we provide it. That’s why it is fundamental to build an AI solution that can avoid unfairness, because even with the best of intentions, seemingly innocuous data can be correlated with protected variables as gender and age, introducing problems.

Companies must also regulate AI training data and assess the impact of their strategies as they are implemented in the real world to detect bias that may have been introduced unintentionally earlier in the process.

This is especially true as teams incorporate machine learning more and more in their processes. These algorithms adjust themselves quickly, which can further obscure the issue.

To ensure that AI is fair to all, business leaders must be proactive and vigilant in policing it.

This includes the development of genuinely unbiased AI data models, monitoring them regularly, and analyzing the results, regularly putting the question: Is our AI considering all genders, ethnicities, age groups, postal codes, salary ranges, religious groups, similarly?

Responsible AI is usually not prioritized until a company makes a mistake, at which point the consequences can be disastrous. In terms of bias, AI must be “better” than the society in which we live, in any case.

Decisions based on unbalanced data are inherently biased and can perpetuate unfair stereotypes.

Transparency

It is a very challenging task today to convince consumers that AI is being developed and used responsibly.

Business leaders must be proactive in certifying their algorithms, clearly communicating their discriminatory policies, and clearly explaining why decisions were made when there is a problem.

For regulated/higher-risk use-cases such as credit risk, they should also consider using transparent and explainable algorithms.

They must make it as simple as possible for frontline employees to explain customers’ reasoning while protecting proprietary information.

Developers and Product owners must be able to demonstrate how AI arrived at a particular decision, particularly in highly regulated industries such as financial services and insurance, by certifying their algorithms, clearly communicating their (learning) bias policies, and delivering a more precise explanation of why decisions were made, mainly when there is a problem.

For regulated or high-risk use cases, such as credit approvals, it is always recommended to use transparent and explainable algorithms to make it easier for end-users to understand and explain customers’ decisions.

Empathy

It is not a secret that business leaders and their development teams struggle to define and implement guardrails to establish the boundaries of “OK” and what may be harmful to an AI solution’s audience.

This is especially important in the case of AI. It must comprehend what is relevant to the audience and what is appropriate for that audience in that context. The AI development team is responsible for defining those rules and providing guardrails for the AI as it learns.

At the same time, business leaders must demonstrate empathy for their customers, showcasing that their actions are consistent, helpful and prioritize customers’ needs regarding AI.

It involves examining each customer contexts to determine precisely what they need at the time. Good business leaders know when to sell, but they also know when to serve, learn, or remain silent.

Empathy can be very beneficial to the business. According to a recent Forrester total economic impact study, using AI to develop a better one-on-one engagement program can generate significant revenue growth — nearly $ 700 million over three years — while reducing customer turnover losses by over $ 500 million.

Robustness

A Twitter bot got off track a few years ago when people were encouraged to interact with him. Within 24 hours, the model had become misogynistic and racist due to the data he was ingesting from Twitter conversations.

For some, it became a big joke, but it was a time of change (inflection) for the market, particularly for those organizations that may have acted too quickly.

We need a more robust AI, or one with built-in safeguards so that it could not be easily influenced. We also needed to put more effort into developing AI rules and safeguards to govern ‘appropriate’ actions in specific situations.

Most of us do not consider algorithms until we make a mistake. Still, businesses have the ownership of preventing discrimination by policing themselves and making decisions based on what is best for the customer.

Conclusion

These four primary pillars of Responsible AI are required to unite business leaders and policymakers around a set of fundamental customer-respecting principles that bring a sustainable (and likely profitable) vision for long-term success that benefits everyone.

It is not only the right thing to do, but it will also protect and strengthen the relationship with customers, brands, and financial results, regardless of the next crisis.

If we want to live up to our commitment to be customer-centric — and not just use a buzzword — we must commit to developing AI responsibly; in addition to being the right thing to do, it will also protect and strengthen relationships with customers, brands, and financial results, regardless of the next crisis.

References

1. A quick Introduction to Responsible AI or rAI | by Jair …. https://towardsdatascience.com/a-quick-introduction-to-responsible-ai-or-rai-ae75fad526dc
2. Can we explain AI? An Introduction to Explainable Artificial Intelligence.|https://towardsdatascience.com/an-introduction-to-explainable-artificial-intelligence-or-xai-f7ec21db825b
3. The Importance of responsible AI in our fast-paced world …. https://www.crn.in/columns/the-importance-of-responsible-ai-in-our-fast-paced-world/
4. The 4 Foundations of Responsible AI — CMSWire.com. https://www.cmswire.com/information-management/the-4-foundations-of-responsible-ai/
5. Ethical consideration definition and meaning | Collins …. https://www.collinsdictionary.com/us/dictionary/english/ethical-consideration
6. How to Beat the Social Media Algorithms. https://www.socialreport.com/insights/article/115002118666-How-to-Beat-the-Social-Media-Algorithms