The four keys to trustworthy AI
Artificial intelligence is a major factor in people’s lives. It influences who gets a loan, how companies hire and compensate employees, how customers are treated, even where infrastructure and aid are allocated. It is already deeply embedded in our businesses, organizations and governments, including the 40,000 client engagements with IBM Watson across 20 industries in 80 countries. As the world increasingly relies on AI to help make major predictions and decisions, it becomes essential that people can trust the process and results of that AI. IBM is working on building that trust.
Organizations that neglect their ethical duties in AI can face lawsuits, regulatory fines, angry customers, embarrassment, reputational damage, and destruction of shareholder value. For example, consider the fairness of your organization’s hiring practices. If your HR department uses an existing machine-learning-based application to score prospective employees, how do you ensure trustworthy implementation of this technology?
From a technical perspective, governed data and AI technology should meet your criteria of transparency, explainability, fairness, robustness and privacy. For your hiring application to be fair, it must counter human biases and promote inclusivity and equitable treatment. But that’s not enough. You must also be ready to provide explanations to hiring managers. Your application must work well in exceptional conditions, withstand threats, and correct for drift. And you must keep applicant data private and secure to prevent inappropriate use.
A major obstacle to the widespread deployment of AI is a lack of trust. According to Morning Consult, 77% of global IT professionals report that it is critical to their business that they can trust the AI’s output is fair, safe and reliable. The way to gain trust is to earn it by building fair, robust, explainable, transparent and privacy-preserving AI models and implementations. Leveraging IBM’s leadership, expertise, tools, and governance frameworks is the best path to trustworthy AI on which we can build our future.
Widespread adoption of AI across an enterprise can be achieved by building systems delivering understandable and trusted outcomes. However, designing and implementing trustworthy AI solutions first requires a deep understanding of the humans’ problems that we want to solve as well as their business needs. Keeping in mind during the entire solution design cycle who you are trying to create value for is crucial to deliver trusted outcomes that users rely on. This can be accomplished by using a framework, such as Enterprise Design Thinking for Data and AI, that illuminates how to employ data and AI to build responsible and trusted solutions that provide business value while solving human-centric problems.
So how do you get started? Taking for example the case of AI technology and solutions your company might build to assess and hire candidates, there are four key areas to consider.
How do you know that the AI models your HR team is using to automate hiring practices are fair? Are you confident that the methods being used by your HR AI solutions are robust enough to stand up to scrutiny? Can you make assurances that your AI solutions can be explained?
To ensure your AI solutions are trustworthy, you need guidance and tools to help you to assess, audit and mitigate risk.
IBM is a leader when it comes to trustworthy AI, as named in a 2021 Gartner report. Our deeply experienced data science and design teams, along with our industry subject-matter expertise, positions us to help you set up an assessment, audit and risk mitigation framework, leveraging IBM Watson AI products to mitigate risk. IBM’s guidance and tools allow you to assess your current AI-enabled business processes, creating scorecards and recommendations for the customer to address the trustworthiness of your AI solutions.
