AI Ethics: Enable AI Innovation With Governance Platforms

As AI technologies become more common, AI ethics risks increase as a result of lack of alignment with societal human values, bias, limited transparency and data privacy concerns.

AI governance platforms allow you to address these and other factors that could stymie AI adoption, such as:

• Regulatory pressure: Increasing global regulations on AI use and data privacy mandate robust governance procedures. 

• Public awareness: Companies must respond to skyrocketing public concern about AI. 

• The negative side of AI advancements: More advanced and autonomous AI systems can easily create convincing yet potentially harmful content. 

Major tech companies are leading the way on AI ethics by adopting more responsible AI guidelines and methods. There is real money behind these efforts, including from major U.S. foundations, which recently formed a $200 million funding coalition to promote responsible AI. Organizations are also appointing executive-level positions to oversee AI systems. These will become more common in major U.S. corporations.

AI governance platforms enable organizations to automate aspects of AI ethics oversight. Unique features and capabilities of these platforms include:

• Built-in responsible AI methods: AI governance tools provide transparency, which fosters trust, accountability and informed decision making.

• Risk assessments: Ethical AI governance platforms assess potential risks such as bias, privacy violations and negative societal impacts.

• Model life cycle management Platforms monitor model development by applying appropriate gates and controls, enabling the delivery and maintenance of ethical AI models at scale while adhering to AI engineering best practices.

• AI system auditing and monitoring: Instrumentation ensures that AI systems remain aligned with responsible AI governance standards over time.

• Compliance management: Tools can monitor existing AI ethics regulations and evolving data protection laws, such as GDPR and CCPA, to ensure compliance.

• Accountability: These platforms identify functions that oversee responsible AI while enabling diverse stakeholders to design, test and develop AI systems.

AI governance platforms represent an opportunity to quickly scale up your ethics stance by delivering:

• Comprehensive AI governance: Achieve better regulatory compliance, revenue growth and cost optimization.

• Responsible AI leadership: Build trust with customers, employees and regulators by prioritizing AI ethics through C-suite-level monitoring, and promoting responsible AI advocacy and thought leadership.

• AI governance training: Develop in-house AI governance expertise and establish responsible AI policies on which to train employees.

To effectively implement AI governance platforms, technical teams must focus on several key areas, including data quality, sanitization, and comprehensive governance strategies. To get started:

• Ensure data quality and sanitization. Generate high-volume and high-quality data. Work with identity and access management (IAM) vendors to produce the diverse data sets necessary for training and tuning AI models. Incorporate legal and ethical considerations to ensure data generation and model tuning comply with legal standards and align with organizational principles and values.

• Drive AI-ready data. Establish a comprehensive governance strategy that includes trusted and well-governed data, high-quality master data and robust data quality management. Extend governance practices to ensure the compliant, responsible and ethical use of AI systems, which will drive better outcomes and foster innovation.

• Read the Planning Guide for Identity and Access Management access action plans for AI governance and other key technology trends impacting security. 

Learn more about how Gartner works with technical teams to execute efficiently and drive business results.