Responsible AI: Inclusive, Unbiased, Accessible, and Secure

FICO’s State of Responsible Artificial Intelligence (AI) in Financial Services report asked 100 banking and financial C-level AI leaders, how they are ensuring AI is used ethically, transparently, securely, and in customers’ best interests. 71% of those organizations said that AI Ethics and Responsible AI are not yet a core part of their operational strategies.

With the rise in demand for AI solutions in these highly regulated industries the need for responsible AI strategies is more important than ever. Inaccurate or biased algorithms can be problematic in unregulated industries but introducing unintended biases without proper AI safeguards in Financial Institutions could lead to much more serious issues.

While AI holds incredible promise, it also carries the potential for bias, inaccessibility, and security concerns. To unlock the complete capabilities of AI while minimizing its adverse effects, organizations and individuals must adopt a responsible AI strategy that prioritizes unbiased, accessible, and secure AI solutions.

Responsible AI explained

Responsible AI, also referred to as ethical AI, is the practice of creating and deploying AI systems in a fair, accountable, ethical, and transparent manner. The primary objective of responsible AI is to minimize potential biases and harm to individuals and communities. 

With responsible AI, institutions can identify and remove sources of discrimination impacting groups or individuals. Instead of focusing on the organization, responsible AI addresses the needs of all current and future stakeholders in an ethical manner. 

In this blog post, we'll explore how to be inclusive with responsible AI by addressing these critical aspects, because when it comes to the future of AI, responsible choices pave the way to a brighter and more equitable world for all.

While ModuleQ prides itself on being a Responsible AI solution, just a note that there are some areas listed below that are outside the scope of our AI but areas that we think you should be thinking about. If you have any questions feel free to reach out and we can help guide you through this space. 

1. Model Risk Management

According to Databricks, "the aim of model risk management (MRM) is to employ techniques and practices that will identify, measure, and mitigate the possibility of model error or wrongful model usage. Financial institutions primarily make money by taking risks -  they maximize models to evaluate risks, understand customer behavior, assess capital adequacy for compliance, make investment decisions, and manage data analytics. Implementing an effective model risk management framework is a requisite for organizations that are heavily reliant on quantitative models for operations and decision-making."

Machine learning models can inadvertently perpetuate societal biases, leading to unfair or discriminatory outcomes. Consider some of the following MRM best practices:

1. Diverse Data Collection: Start with diverse and representative datasets. Ensure that data is collected from a wide range of sources and populations to avoid under-representation or over-representation of certain groups. This will help AI models to learn from a more inclusive and balanced set of examples.

2. Fair Data Preprocessing: Implement fairness-aware preprocessing techniques to identify and correct biases in the data. This involves techniques like re-weighting or re-sampling the data to ensure that every group is treated fairly. Tools and libraries like AI Fairness 360 can be invaluable for this purpose.

3. Transparent Model Development: Build transparency into your AI development process. Document and share the methods, data sources, and algorithms used in your models. This transparency allows for scrutiny and accountability, helping to ensure that models are developed without hidden biases.

4. Continuous Monitoring and Feedback: Implement a system for continuous monitoring and feedback. Regularly assess your AI system's performance for bias and correct any issues that arise. Solicit feedback from users and stakeholders, especially from underrepresented groups, to make improvements over time.

2. Accessibility

Making AI accessible to everyone, including individuals with disabilities and those in underserved communities, is an important aspect of responsible AI. Here are some ways how you can achieve this:

1. Inclusive Design: Adopt an inclusive design approach when developing AI applications. Consider the needs of individuals with disabilities from the outset. This may involve furnishing alternative text for images, enabling keyboard navigation, and ensuring compatibility with screen readers.

2. User-Centric Testing: Conduct user testing with individuals from diverse backgrounds and abilities. Listen to their feedback and make adjustments to improve usability and accessibility. This iterative approach helps ensure that your AI solutions are truly inclusive.

3. Compliance with Accessibility Standards: Get acquainted with established accessibility criteria like the Web Content Accessibility Guidelines (WCAG) and confirm your AI applications adhere to these guidelines. Compliance with established standards is a key step in achieving accessibility. Check out the guidelines here.

4. Compliance with Americans with Disabilities Act (ADA) Standards: According to the ADA Act, inaccessible web content means that people with disabilities are denied equal access to information. An inaccessible website or application can exclude people just as much as steps at an entrance to a physical location. Ensuring accessibility for people with disabilities should be a priority for all AI applications.

5. Partnerships with Inclusive Organizations: Collaborate with organizations and groups that specialize in accessibility and inclusivity. Drawing upon their experience and knowledge can prove to be priceless in guaranteeing that your AI solutions are crafted and conceived with the needs of a broad audience in mind. ModuleQ has partnered with Microsoft to leverage their accessibility commitments leveraging Microsoft Teams at our UI.

3. Security and Governance 

Ensuring the security of AI systems is crucial to prevent misuse and protect sensitive data. Here are some strategies to implement secure AI:

• Robust Data Handling: Implement strong data security practices. This includes encrypting data, establishing access controls, and monitoring data usage. Protecting the data used to train and run AI models is essential to prevent breaches and unauthorized access.

• Threat Detection and Prevention: Employ robust threat detection mechanisms. Continuously monitor AI systems for potential threats or anomalies. Utilize AI-based security solutions that can identify and respond to security risks in realtime.

• Ethical Use Policies: Establish ethical use policies for your AI systems. Define guidelines for data usage, model deployment, and user interactions. Clearly communicate these policies to all stakeholders and ensure compliance.

• Regular Security Audits: Regularly perform security assessments on your AI infrastructure. Engage with cybersecurity experts to identify vulnerabilities and weaknesses. Address any security issues promptly to maintain a secure AI environment.

Shaping Our AI-Powered Future: Your Role in Responsible AI Adoption

In today's world, responsible AI is not an option; it is an imperative. Unbiased, accessible, and secure AI can empower individuals, organizations, and societies to harness the transformative potential of artificial intelligence while minimizing the negative consequences. By adhering to the strategies outlined in this blog post, you can take significant steps toward creating AI systems that benefit everyone, regardless of their background, abilities, or security concerns.

As responsible AI adoption continues to grow, the commitment to these principles will become increasingly vital. Organizations and individuals who prioritize unbiased, accessible, and secure AI are not only acting in their best interests but also contributing to a more inclusive and equitable AI-powered future. The time for responsible AI is now, and it starts with you.

Governance of AI systems and accountability in financial services

As a new technology that’s quickly being applied across domains, it’s important to have a structure in place for the governance of AI systems. Those who develop and deploy it should be aware of the unintended pitfalls of the tools.

The President's Supporting Workers initiative aims to mitigate the risks of AI to workers, including job displacement, workplace surveillance, bias, and increased inequality. The initiative includes developing Responsible AI guardrails to mitigate the harms and maximize the benefits of AI for workers. This order will require organizations using AI to produce a report on AI's potential labor-market impacts, and study and identify options for strengthening federal support for workers facing labor disruptions, including from AI. This report will help to inform the government's response to the potential challenges and opportunities posed by AI for workers.

There should be accountability and a continuous process of learning and iterating to make sure that the system treats everyone fairly, transparently, and ethically.