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 building and scaling AI models for your organization becomes more business critical, achieving Responsible AI (RAI) should be considered a highly relevant topic. There is a growing need to proactively drive responsible, fair, and ethical decisions, designed to:
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 build trust. Incorrect or biased use of AI based on faulty datasets or assumptions can result in lawsuits and an erosion of stakeholder, customer, stockholder and employee trust. Ultimately, this can lead to reputational damage, lost sales and decreased revenue.
Adhere to ethical principles
The importance of driving ethical decisions – not favoring one group over another, requires AI systems that achieve fairness. This necessitates the detection of bias during data acquisition, building, training, deploying and monitoring models. Fair decisions require the ability to adjust to changes in behavioral patterns and profiles. This may demand model retraining or rebuilding.
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 to meet local and country specific rules and regulations. Organizations in highly regulated markets such as healthcare, government and financial services have additional challenges in meeting industry regulations around data and models.
“The average cost of compliance came in at $5.47 million, while the average cost of non-compliance was $14.82 million. The average cost of non-compliance has risen more than 45% in 10 years. The true cost of non-compliance for organizations due to a single non-compliance event is an average of $4 million in revenue.” The True Cost of Noncompliance
Responsible AI requires governance
Despite good intentions and evolving technologies, achieving responsible AI can be challenging. AI requires AI governance, not after the fact but baked into AI strategy of your organization. So what is AI governance? It is the process of defining policies and establishing accountability to guide the creation and deployment of AI systems.
For many of today’s organizations today, governing AI requires a lot of manual work that include the use of multiple tools, applications and platforms. Lack of automation can lead to lengthy model approval, validation and deployment cycles during which model drift and bias can happen. Manual processes can lead to “black box models” that lack transparent and explainable analytic results.
Explainable results are crucial when facing questions on the performance of AI algorithms and models. Your company’s management, stakeholders and stockholders expect accountability. Your customers deserve and are holding your organization accountable to explain reasons for analytics-based decisions. These may include credit, mortgage and school denials, or the details of healthcare diagnosis or treatment. Documented, explainable model facts are necessary when defending analytic decisions.
An AI Governance solution driving responsible, transparent and explainable AI workflows
The right AI governance solution can help to better direct, manage and monitory your organization’s AI activities. With the right end-to-end automated platform, your organization can strengthen the ability to meet regulatory requirements, protect the reputation of your organization and address ethical concerns.
The IBM AI Governance solution automates across the AI lifecycle from data collection, model building, deploying and monitoring. Model facts are centralized for AI transparency and explainability. This comprehensive solution comes without the excessive costs of switching from your current data science platform. This solution includes:
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. Dynamic dashboards provide customizable results for your stakeholders and enhance collaboration across multiple regions and geographies.
Regulatory compliance
Translate external AI regulations into policies for automated enforcement. This results in enhanced adherence to regulations for audit and compliance purposes and provides customized reporting to key stakeholders.
Source: ibm.com
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