AI is transforming how businesses operate and engage the world, delivering the power of prediction to augment human decision-making. However, humans must trust predictive data recommendations and outcomes for AI to realize its full potential. The recurrent question: how to build and create trust in data.

At IBM’s recent Chief Data and Technology Officer Summit, where we had a record number of attendees, we were joined by CDOs, CTOs, and COOs across Government, Technology, Healthcare, Digital, and Automotive industry to discuss the challenges surrounding this topic and get to know how they are building trust within their organizations. In addition, we shared IBM’s approach to helping our clients achieve greater trust, transparency, and confidence in business predictions and outcomes by applying the industry’s most comprehensive data and AI governance solutions.

Trust in Data, Trust in AI

For more than a century, IBM has been committed to the responsible stewardship of data and technology. Trust is part of our DNA, and we’ve been operationalizing our values and principles for AI governance through the AI Ethics Board. Today, IBM is a leader in advancing global progress around ethical AI – from principles and policy advocacy to putting it into practice, and we continue to engage stakeholders worldwide as they explore the critical questions posed by the advancement of AI, to ensure that its full potential for positive impact can be reached. You can learn more about how to advance AI ethics beyond compliance in this recent IBV Study.

To build trust in new AI-driven technologies, we must start with ensuring the right data and AI foundations are in place. Building trust begins with governance to ensure that data and AI can be trusted. Data must be accurate, accessible, governed, secure, privacy respected, and relevant. Organizations recognize that it takes a holistic approach to manage and govern the AI solutions across the entire AI lifecycle. That’s why IBM continually brings innovative governed data and AI technology and approaches to market that are built on five focus areas: transparency, explainability, robustness, fairness, and privacy.

1. Transparency

   Users must be able to see how the service works, evaluate its functionality and comprehend its strengths and limitations. Transparency reinforces trust, and the best way to promote transparency is through disclosure. Transparent AI systems share information on what data is collected, how it will be used and stored, and who has access to it.

2. Explainability

   Any AI system on the market that is making determinations or recommendations with potentially significant implications for individuals should be able to explain and contextualize how and why it arrived at a particular conclusion.

3. Robustness

   AI-powered systems must be actively defended from adversarial attacks, minimizing security risks and enabling confidence in system outcomes. Robust AI effectively handles exceptional conditions, such as abnormalities in input or malicious attacks, without causing unintentional harm.

4. Fairness

   Instrumenting AI for fairness is essential. Properly calibrated, AI could assist humans in making more informed choices, process and evaluate facts faster and better, or allocate resources more fairly — allowing us to break the chain of human biases.

5. Privacy

   AI systems must prioritize and safeguard consumers’ privacy and data rights and provide explicit assurances to users about how their personal data will be used and protected. Respect for privacy means full disclosure around what data is collected, how it will be used and stored, and who has access to it.

When people do not trust data, they start to fall back on their intuition and experience. Building a culture of trusting data can be challenging, but very important to ensure the future of the business. Another aspect that we consider essential to foster trust in data is empowerment. A data-driven culture and employee empowerment go hand in hand – while companies must provide the governance and tools to enable employees to act upon data, employees must be empowered to go ahead and make informed decisions. And always start with the outcomes in mind – knowing what these are up-front and ensuring everyone is on the same page is key.

Managing Data for AI

Data is an integral element of digital transformation for enterprises. But as organizations seek to leverage their data, they encounter challenges resulting from diverse data sources, types, structures, environments and platforms. Copying data, consolidating and moving it can affect its quality. Data silos typically complicate data integration, prevent centralized data management, and keep data from being easily accessible.

Data quality and integration can become major issues when pulling from multiple cloud environments. What is bringing companies back to a successful path for digital transformation is the employment of a new data architecture concept known as data fabric. With the new data fabric and AI capabilities, IBM is delivering what we anticipate should be a significant differentiator for customers by automating the data and AI lifecycle – the potential to free up time, money and resources – and connect the right data to the right people at the right time, while conserving resources. Get to know how data fabric differs from previous architectures, what it can achieve for businesses, and IBM’s role in implementing it in this white paper.

Remember, trusting in data is fundamental to achieving higher confidence in your predictions’ quality, developing deeper insights, unlocking discoveries, and making decisions exponentially faster. To hear more from our peers and learn tangible actions that you can customize and implement into your organization, watch the replay of CDO/CTO Summit “Building and Creating Trust in Data.”

Our next event in the IBM series will be on Wednesday October 20, 2021, where we’ll explore “Balancing Innovation and Growth with Risk and Regulation.” Find out more and register for the event.