AI for industries: The importance of responsible innovation

In recent years, there has been a growing interest in artificial intelligence (AI) and its potential applications across industries. There are industries that are proactively working on Responsible AI practices, while others are still in the early stages of adoption and exploring the possibilities of what this transformative technology can do for them.

At Capgemini, we believe that Responsible AI is key to success in the adoption of AI technologies.

In this blog post, we’ll explore the importance of Responsible AI innovation in industries, and how businesses can benefit from mindful adoption of this technology.

AI and business: Transforming industries

Artificial intelligence (AI) can be a powerful tool for businesses and industries to make meaningful contributions to society. AI enables us to accelerate the transformation of existing products and services, as well as the emergence of new ones that utilize big data, machine learning, predictive analytics, robotics, and more.

By placing ethical practices at its core, artificial intelligence can be deployed responsibly in every sector of industry. With AI at full scale implementation within these organizations, greater efficiency can be achieved quickly and cost effectively.

Capgemini is excited to be a part of leading this innovative change!

What is Responsible AI (RAI)?

Responsible AI is an important concept as it helps to ensure that AI systems are ethically sound (ethical AI) and are trustworthy (trusted AI), while protecting the interests of both individuals and organizations.

This is achieved by creating clear principles, policies, and guidelines that emphasize human-centered values such as trustworthiness, fairness, privacy, transparency, and accountability.

To help ensure Responsible AI use, organizations can adopt certain best practices such as developing ethical guidelines for their AI systems; conducting rigorous testing to check for bias; making sure algorithms are understandable; ensuring safety measures are in place; monitoring performance regularly; and educating staff on how to use the technology responsibly.

At Capgemini, our Trusted AI aims to ensure that AI systems are ethical. And our Ethical AI is defined based upon Capgemini’s Code of Ethics for AI.

Our Code of Ethics for AI concerns both the intended purpose of the AI solution and the way ethical AI principles are embedded in the design and delivery of AI solutions and services.

Importance of Responsible AI

Responsible innovation is a key focus when it comes to the development and implementation of Artificial Intelligence (AI) technologies. Responsible AI must be planned and put into practice with social values in mind — like ethics and trustworthiness.

As a whole, Responsible AI needs to be designed for safety through measured risk approaches that can support responsible decision-making when utilizing data and algorithms.

Across many sectors, Responsible AI has immense potential to automate business processes, create efficiencies, drive cost savings, add value to customer interactions, and enhance the quality of products and services.

It’s now seen as a “must have” proposition by many global organizations who depend on both digital optimization and ethical processes.

Capgemini is determined to continue to develop Responsible AI solutions that result in tangible business benefits as well as social benefits such as sustainability and increased diversity efforts.

Responsible AI in action: Real-world applications across industries

At Capgemini, Responsible AI and how it is used in industry sectors is an area of increasing focus.

We are seeing AI being implemented across a range of industries today, from healthcare, to finance, to transport — all with the aim of generating performance improvements, reducing costs, and improving customer experiences.

Tackling sensible patient data:

Often, organizations are limited in their data use due to privacy and compliance risks, such as those related to patient health data. Another problem is data scarcity, which can impact the use of AI-based solutions.

Capgemini, by leveraging Sogeti’s Artificial Data Amplifier solution that takes advantage of cutting-edge deep learning techniques, uses sample set of real data to create a synthetic dataset that closely mimics the characteristics of real data.

By utilizing this solution, organizations are able to generate enough production data for testing to improve accuracy while maintaining statistical similarity and distribution in their results. This enables compliance with GDPR and other data regulations around sensitive data.

Avoid bias and discrimination:

Utilizing data for decision-making is a key component of AI, but if the data sets are biased — either due to inadequate quality or misuse — then AI will learn with that bias. Not only is AI initially as biased as humans, but it can also worsen the bias of a dataset by continually learning from that same flawed data. And discrimination in AI applications can bring about significant repercussions, even legal ramifications.

Furthermore, machine learning algorithms can introduce unintended bias through the learning process. Machine learning algorithms are typically attempting to minimize the error term between the actual and the predicted outcome and do not discriminate between training examples that have an error and those which do not. Consequently, if the erroneous examples happen to belong to a particular cohort (say age or gender), then a bias that disfavors that cohort may have been introduced.

Capgemini’s SAIA is a revolutionary discrimination-fighter tool that not only identifies biases, but also examines the sources of these tendencies and recommends necessary alterations and corrections to algorithms.

Explainable AI (XAI):

AI systems are able to make decisions that appear accurate based on the outcomes from their analysis; however, they lack an understanding of why a decision was correct and if it was made for the right reasons.

That is why it is very importance to explain the method behind an AI-generated outcome, so that we can easily understand and pinpoint why the decision went wrong. If not addressed immediately, this might lead to a major trust issue and difficulty in maintaining compliance.

In order to combat this issue, Capgemini created an explainability tool that explains the rationale behind conclusions made by AI programs, such as how fraud detection is determined through graph knowledge.

By introducing XAI into the deployment of AI models, businesses can gain a greater understanding of how their automated systems work and which decisions are being made by the AI system.

Through our Responsible AI insights and expertise, we aim to support our partners and clients in securing competitive advantages that improve operations and provide tangible benefits to businesses.

Best practices for Responsible AI implementation

Responsible AI is essential for businesses striving to further integrate artificial intelligence into their operations. To do so, employers must recognize that AI can make decisions faster than humans and take proactive steps to identify potential ethical questions or unintended consequences that may arise from using the technology.

Building on our core Values, we believe that the design and delivery of AI solutions should be guided by seven principles:

#1. Sustainable AI:

Unlock the power of artificial intelligence to realize sustainability objectives and stay ahead in a competitive landscape.

#2. Fair AI:

Take a preemptive approach to incorporating diversity and inclusion principles into every stage of artificial intelligence development for maximum impact.

#3. Transparent and explainable AI:

Leverage cutting-edge technology tools to facilitate AI transparency and accountability.

#4. Controllable AI with clear accountability:

For a truly remarkable artificial intelligence experience, prioritize the humanization of AI systems and guarantee that humans are always in control.

#5. Robust and safe AI:

Ensure technical robustness of AI from a safety, security, and accuracy standpoint.

#6. AI respectful of privacy and data protection:

Give individuals the right to choose and control their AI interactions, thus allowing them to safeguard their privacy.

#7. AI with carefully delimited impact:

Before adopting any AI systems, it is essential to clearly define their purpose and evaluate the potential impacts they may have on individuals. This assessment should be comprehensive in order to ensure that no harm comes from their implementation.

Capgemini’s Code of Ethics for AI is intended to help organizations ensure that their AI solutions are aligned with their values and objectives and are based on best practices for Responsible AI development.

Conclusion

At Capgemini, we believe Responsible AI can create tangible business value and improve the lives of consumers.

We envision a Responsible AI driven future as one where responsible innovation is applied across all industry sectors, from marketing to energy distribution — enabling responsible organizational transformation. Therefore, businesses must take up the mantle of responsible innovation and use AI to maximize efficiency and benefit society as a whole.

We stand ready to help you embrace responsible technology innovation for a better tomorrow.

Let’s take this chance to shape a future suited for all by driving Responsible AI strategies forward, and let’s work together to make it happen.

Thanks to Richard Price and Bala Natarajan for co-authoring this blog with me, and I appreciate their contributions.