Responsibility is a learned behavior. Over time we connect the dots, understanding the need to meet societal expectations, comply with rules and laws, and to respect the rights of others. We see the link between responsibility, accountability and subsequent rewards. When we act responsibly, the rewards are positive; when we don’t, we can face negative consequences including fines, loss of trust or status, and even confinement. Adherence to responsible artificial intelligence (AI) standards follows similar tenants.

Gartner predicts that the market for artificial intelligence (AI) software will reach almost $134.8 billion by 2025.

Achieving Responsible AI

As AI and building and scaling models becomes more business critical for your organization, achieving Responsible AI (RAI) should be considered a highly relevant topic. There is a growing need to proactively drive fair, responsible, ethical decisions and comply with current laws and regulations.

Manage risk and reputation

No organization wants to be in the news for the wrong reasons, and recently there have been a lot of stories in the press regarding issues of unfair, unexplainable, or biased AI. Organizations need to protect individuals’ privacy and drive trust. Incorrect or biased actions based on faulty data or assumptions can result in lawsuits and customer, stakeholder, stockholder and employee mistrust. Ultimately, this can lead to damaging to the organization’s reputation and lost sales and revenues.

Adhere to ethical principles

The importance of driving ethical decisions — not favoring one group over another — requires building in fairness and detecting bias during data acquisition, building, deploying and monitoring models. Fair decisions also require the ability to adjust to changes in behavioral patterns and profiles which may require model retraining or rebuilding throughout the AI lifecycle.

Protect and scale against government regulations

AI regulations are growing and changing at a rapid pace and noncompliance can lead to costly audits, fines and negative press. Global organizations with branches in multiple countries are challenged in meeting local and country specific rules and regulations. While organizations in highly regulated markets such as healthcare, government and financial services have additional challenges in meeting industry specific regulations.

“The potential costs of non-compliance are staggering and extend far beyond simple fines. For starters, organizations lose an average of $5.87 Million in revenue due to a single non-compliance event. But this is only the tip of the iceberg — the financial impact goes far beyond your bottom line.” The True Cost of Noncompliance

Responsible AI requires governance

Gartner defines AI governance as “the process of creating policies, assigning decision rights and ensuring organizational accountability for risks and investment decisions for the application and use of artificial intelligence techniques.” 

Despite good intentions and evolving technologies, achieving responsible AI can be challenging.  Responsible AI requires AI Governance and for many organizations this requires a lot of manual work which is amplified by changes in data and model versions and the use of multiple tools, applications and platforms. Manual tools and processes can lead costly human errors and to models that lack transparency, proper cataloguing and monitoring. These “black box” models can produce analytic results that are unexplainable even by the data scientist and other key stakeholders.  

Explainable results are crucial when facing questions on model performance from management, stakeholders and stockholders. Customers deserve and are holding companies accountable to explain reasons for analytic decision including things like credit, mortgage and school acceptance denials, as well as the details of healthcare diagnosis or treatment. Documented, explainable model facts are also necessary when defending analytic decisions with auditors or regulators.

Coming soon: IBM watsonx.governance—driving responsible, transparent and explainable AI workflows

The IBM automated approach to governance helps you to direct, manage, and monitor your organization’s AI activities. By employing software automation, this solution helps strengthen your ability to meet regulatory requirements and address ethical concerns (without the excessive costs of switching from your current data science platform).

Spanning the entire AI lifecycle, the watsonx.governance solution monitors and manages model building, deploying, monitoring, and centralizing facts for AI transparency and explainability. Components of the solution include:

• Lifecycle governance – monitor, catalog and govern AI models from where they reside. Automate the capture of model metadata and increase predictive accuracy to identify how AI is used and where models need to be reworked.
• Risk management – automate model facts and workflows for compliance to business standards. Identify, manage, monitory and report on risk and compliance at scale. Use dynamic dashboards to provide customizable results for stakeholders. Enhance collaboration across multiple regions and geographies.
• Regulatory compliance – translate external AI regulations into policies for automated enforcement. Enhance adherence to regulations for audit and compliance and provide customized reporting to key stakeholders.   

Want to talk about how IBM watsonx.governance can help your organization? Book a meeting today.