Responsible AI for healthcare and financial services industries

January 9, 2023 | 6 minute read
Sanjay Basu PhD
Senior Director - Gen AI/GPU Cloud Engineering
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The use of artificial intelligence (AI) in enterprise applications has grown significantly in recent years, and this trend is expected to continue. AI has the potential to improve efficiency, productivity, and decision-making in various industries, but it also raises important ethical concerns. Organizations must approach the use of AI in a responsible manner.

In the second blog of the series, we’re discussing responsible AI with respect to healthcare and financial industries. These two industries are diverse, but a few commonalities exist when it comes to develop AI and machine learning (ML) algorithm-based applications responsibly.

Responsible AI

One key aspect of responsible AI for enterprise applications is ensuring that the technology is developed and deployed in a transparent and accountable way, providing clear explanations for how AI algorithms make decisions and allowing for outside oversight and review. It also means avoiding the use of AI in ways that might be discriminatory or biased against certain individuals or groups. Another important consideration is the potential impact of AI on the workforce. As AI technology continues to advance, it can displace some jobs, requiring workers to adapt and learn new skills. Organizations must consider the potential effects of AI on their employees and develop strategies to support them through this transition.

Responsible AI for enterprise applications must prioritize the protection of personal data. AI systems often rely on large amounts of data to function, and ensuring that this data is collected and used ethically is paramount. This process includes obtaining consent from individuals before collecting their data and protecting it from unauthorized access or misuse.

The National Institute of Standards and Technology (NIST) goes even further in what responsible AI means. They recommend that orgs building AI systems look at everything from data collection, to analysis of that data, and even who’s going to consume the AI system in the end and ensure that no stone is left unturned. NIST also recommends that teams handling data and building the AI systems be as diverse as possible to bring many perspectives to help identify and mitigate biases.

The use of AI in enterprise applications has the potential to bring many benefits, but organizations must approach it in a responsible manner. This process involves considering the ethical implications of AI, being transparent and accountable in its development and deployment, and protecting personal data. By taking these steps, organizations can help ensure that AI is used in a way that benefits all stakeholders.

Responsible AI in healthcare

Artificial intelligence (AI) has the potential to revolutionize healthcare and improve the lives of patients. However, the responsible use of AI in healthcare is crucial to ensuring that people use it ethically and effectively. One of the key challenges in using AI in healthcare is ensuring its fairness and lack of bias. AI systems are only as good as the data they’re trained on, and if that data predominantly from one gender or racial group, it might not perform as well on data from other groups. This issue can lead to unequal treatment of patients and potentially even harm patients who aren’t well-represented in the training data. To address this issue, we must ensure that the data used to train AI systems in healthcare is diverse and representative of the population that the AI is used on. We can achieve this goal through initiatives such as data sharing and collaboration among healthcare providers and researchers.

Another challenge in using AI in healthcare is ensuring that it’s transparent and explainable. AI systems often make decisions based on complex algorithms that are difficult for humans to understand. So, patients and healthcare providers can have difficulty trusting the decisions made by the AI, and it can also make it difficult to identify and address any biases or errors in the system. To address this issue, developing AI systems that are transparent and explainable is important, using techniques such as explainable AI and interpretable machine learning, which aim to make the decision-making processes of AI systems more transparent and understandable.

With fairness and transparency, the responsible use of AI in healthcare also requires robust oversight and governance. AI systems must be regularly evaluated to ensure that they’re performing as intended and not causing harm to patients. This evaluation must involve both technical experts and clinicians, as well as patient representatives and ethicists. The responsible use of AI in healthcare requires a combination of technical expertise, collaboration, and ethical considerations. By addressing issues such as bias, transparency, and governance, we can ensure that AI benefits patients and improves healthcare.

Responsible AI in the financial services industry

The use of artificial intelligence in the financial services industry has the potential to bring many benefits, such as increased efficiency, improved accuracy, and faster decision-making. However, the use of AI also raises important ethical and social concerns, such as the potential for discrimination, job losses, and the concentration of power and wealth in the hands of a few large companies. To ensure that the use of AI in the financial services industry is responsible and beneficial to society, companies must adopt an ethical and transparent approach to AI development and deployment. This adoption includes ensuring that AI systems are designed and trained in a way that avoids bias and discrimination and subject to appropriate oversight and regulation.

Companies must be transparent about how they’re using AI, and they should engage with stakeholders, including customers, employees, and regulators, to ensure that the use of AI is in the best interests of all parties. This process can involve regularly disclosing information about the AI systems they’re using and providing opportunities for stakeholders to provide feedback and raise concerns. Companies must consider the potential impact of AI on employment and inequality, which can involve investing in training and reskilling programs for employees who are affected by the adoption of AI and implementing measures to ensure that the benefits of AI are shared more widely, instead of being concentrated in the hands of a few.

The responsible use of AI in the financial services industry is essential for ensuring that the technology is used in a way that’s fair, transparent, and beneficial to society. By adopting ethical and transparent practices, companies can help to build trust and confidence in the use of AI and ensure that the technology is used to improve the lives of people and communities.

Both technology providers, such as engineering organizations, vendors, and cloud service providers (CSPs), and industry-specific policy and governance organizations have important roles to play in ensuring the responsible development of AI. Ultimately both are responsible for working together to ensure that AI is developed and used in a way that’s safe and beneficial for their respective customer base and society as a whole. Engineers can use their expertise to design and build AI systems that are safe and ethical, while policymakers and government officials can create regulations and guidelines to ensure that these systems are used in a responsible manner. By working together, we can ensure that AI technology is used to benefit humanity and improve our world.

Notable mention

Anthropic has developed an essential tool for responsible AI, which allows organizations to analyze and monitor their AI systems to ensure that they’re operating according to ethical and regulatory standards. By using Anthropic’s advanced algorithms, companies can detect potential bias or unfairness within their models, which helps them create more responsible AI systems overall. Customers can also use Anthropic’s platform to help identify issues with data quality or labeling, which are often critical problems that can lead to unethical AI behavior. By identifying and addressing these kinds of problems early on, organizations can help create a more responsible approach to AI development and deployment.

How Oracle can help

Oracle Cloud Infrastructure (OCI) comes with a set of tools and services to enable any organization to move from model experimentation to production. OCI’s secure, reliable, scalable cloud services appeal to organizations as they develop models collaboratively on Oracle Data Science platform.

Try Oracle Cloud Free Trial! A 30-day trial with US$300 in free credits gives you access to Oracle Cloud Infrastructure Data Science service.

Want to learn more? See the following resources:

Sanjay Basu PhD

Senior Director - Gen AI/GPU Cloud Engineering

Sanjay focuses on the advanced services like Generative AI, Machine-Learning, GPU Engineering, Blockchain, Microservices, Industrial IoT, 5G core along with Cloud Security and Compliance. He has double masters in Computer Science and Systems Design. His PhD was in Organizational Behaviour and Applied Neuroscience. Currently, he is pursuing his second PhD in AI. His focus of research is Retentive Networks.


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