‘Trustworthy AI’ is a framework to help manage unique risk


Artificial intelligence (AI) technology continues to advance by leaps and bounds and is quickly becoming a potential disrupter and essential enabler for nearly every company in every industry. At this stage, one of the barriers to widespread AI deployment is no longer the technology itself; rather, it’s a set of challenges that ironically are far more human: ethics, governance, and human values.

As AI expands into almost every aspect of modern life, the risks of misbehaving AI increase exponentially—to a point where those risks can literally become a matter of life and death. Real-world examples of AI gone awry include systems that discriminate against people based on their race, age, or gender and social media systems that inadvertently spread rumors and disinformation and more.

Even worse, these examples are just the tip of the iceberg. As AI is deployed on a larger scale, the associated risks will likely only increase—potentially having serious consequences for society at large, and even greater consequences for the companies responsible. From a business perspective, these potential consequences include everything from lawsuits, regulatory fines, and angry customers to embarrassment, reputation damage, and destruction of shareholder value.

Yet with AI now becoming a required business capability—not just a “nice to have”—companies no longer have the option to avoid AI’s unique risks simply by avoiding AI altogether. Instead, they must learn how to identify and manage AI risks effectively. In order to achieve the potential of human and machine collaboration, organizations need to communicate a plan for AI that is adopted and spoken from the mailroom to the boardroom. By having an ethical framework in place, organizations create a common language by which to articulate trust and help ensure integrity of data among all of their internal and external stakeholders. Having a common framework and lens to apply the governance and management of risks associated with AI consistently across the enterprise can enable faster, and more consistent adoption of AI.

The Trustworthy AI framework

To better address the challenges related to AI ethics and governance—it helps to leverage a framework. Deloitte’s Trustworthy AI framework introduces six key dimensions that, when considered collectively in the design, development, deployment, and operational phases of AI system implementation, can help safeguard ethics and build a trustworthy AI strategy.

The Trustworthy AI framework is designed to help companies identify and mitigate potential risks related to AI ethics at every stage of the AI lifecycle. Here’s a closer look at each of the framework’s six dimensions.

The Trustworthy AI framework

Trustworthy AI must be designed and trained to follow a fair, consistent process and make fair decisions. It must also include internal and external checks to reduce discriminatory bias.

Bias is an ongoing challenge for humans and society, not just AI. However, the challenge is even greater for AI because it lacks a nuanced understanding of social standards—not to mention the extraordinary general intelligence required to achieve “common sense”— potentially leading to decisions that are technically correct but socially unacceptable. AI learns from the datasets used to train it, and if those datasets contain real-world bias then AI systems can learn, amplify, and propagate that bias at digital speed and scale.

For example, an AI system that decides on-the-fly where to place online job ads might unfairly target ads for higher paying jobs at a website’s male visitors because the real-world data shows men typically earn more than women. Similarly, a financial services company that uses AI to screen mortgage applications might find its algorithm is unfairly discriminating against people based on factors that are not socially acceptable, such as race, gender, or age. In both cases, the company responsible for the AI could face significant consequences, including regulatory fines and reputation damage.

To avoid problems related to fairness and bias, companies first need to determine what constitutes “fair.” This can be much harder than it sounds since for any given issue there is generally no single definition of “fair” upon which all people agree. Companies also need to actively look for bias within their algorithms and data, making the necessary adjustments and implementing controls to help ensure additional bias does not pop up unexpectedly. When bias is detected, it needs to be understood and then mitigated through established processes for resolving the problem and rebuilding customer trust.

  1. Transparent and explainable

For AI to be trustworthy, all participants have a right to understand how their data is being used and how the AI is making decisions. The AI’s algorithms, attributes, and correlations must be open to inspection, and its decisions must be fully explainable.

As decisions and processes that rely on AI increase both in number and importance, AI can no longer be treated as a “black box” that receives input and generates output without a clear understanding of what is going on inside.

For example, online retailers that use AI to make product recommendations to customers are under pressure to explain its algorithms and how recommendation decisions are made. Similarly, the US court system faces ongoing controversy over the use of opaque AI systems to inform criminal sentencing decisions.[i]

Important issues to consider in this area include identifying the AI use cases for which transparency and explainability are particularly important, and then understanding what data is being used and how decisions are being made for those use cases. Also, with regard to transparency, there is growing pressure to explicitly inform people when they are interacting with AI, instead of having the AI masquerade as a real person.

  1. Responsible and accountable

Trustworthy AI systems need to include policies that clearly establish who is responsible and accountable for their output. Blaming the technology itself for poor decisions and miscalculations just isn’t good enough – not for the people who are harmed, and certainly not for government regulators. This is a key issue that will likely only become more important as AI is used for an expanding range of increasingly critical applications such as disease diagnosis, wealth management, and autonomous driving.

For example, if a driverless vehicle causes a collision, who is responsible and accountable for the damage? The driver? The vehicle owner? The manufacturer? The AI programmers? The CEO?

Similarly, consider the example of an investment firm that uses an automated platform powered by AI to trade on behalf of its clients. If a client invests her life savings through the firm and then loses everything due to poor algorithms, there should be a mechanism in place to identify who is accountable for the problem, and who is responsible for making things right.

Key factors to consider include which laws and regulations might determine legal liability and whether AI systems are auditable and covered by existing whistleblower laws. Also, how will problems be communicated to the public and regulators, and what consequences will the responsible parties face?

In order for AI to achieve widespread adoption, it must be at least as robust and reliable as the traditional systems, processes, and people it is augmenting or replacing.

For AI to be considered trustworthy, it must be available when it’s supposed to be available and must generate consistent and reliable outputs – performing tasks properly in less than ideal conditions and when encountering unexpected situations and data. Trustworthy AI must scale up well, remaining robust and reliable as its impact expands and grows. And if it fails, it must fail in a predictable, expected manner.

Consider the example of a health care company that uses AI to identify abnormalities in brain scans and prescribe appropriate treatment. To be trustworthy, it is absolutely essential for the Al algorithms to produce consistent and reliable results since lives could be on the line.

To achieve AI that is robust and reliable, companies need to ensure their AI algorithms produce the right results for each new dataset. They also need established processes for handling issues and inconsistencies if and when they arise. The human factor is a critical element here: understanding how human input affects reliability; determining who the right people are to provide input; and ensuring those people are properly equipped and trained—particularly with regard to bias and ethics.

Privacy is a critical issue for all types of data systems, but it is especially critical for AI since the sophisticated insights generated by AI systems often stem from data that is more detailed and personal. Trustworthy AI must comply with data regulations and only use data for the stated and agreed-upon purposes.

The issue of AI privacy often extends beyond a company’s own walls. For example, the privacy of audio data captured by AI assistants has been making headlines recently, with controversies arising over the extent to which a company’s vendors and partners are given access to the data, and whether the data should be shared with law enforcement agencies.

Companies need to know what customer data is being collected and why, and whether the data is being used in the way customers understood and agreed. Also, customers should be given the required level of control over their data, including the ability to opt in or opt out of having their data shared. And if customers have concerns about data privacy, they need an avenue to voice those concerns.

To be trustworthy, AI must be protected from cybersecurity risks that might lead to physical and/or digital harm. Although safety and security are clearly important for all computer systems, they are especially crucial for AI due to AI’s large and increasing role and impact on real-world activities.

For example, if an AI-based financial system gets hacked, the result might be reputation damage and lost money or data. Those are serious consequences, of course. However, they are not nearly as serious as the potential consequences of an AI-driven vehicle getting hacked, which could put people’s lives at risk.

Another example of AI cybersecurity risk is a recent data breach involving millions of fingerprint and facial recognition records. ii This breach was particularly serious because it involved people’s biometric data, which is permanent and cannot be altered (unlike a stolen password or other standard type of data that can quickly and easily be changed to limit the damage).

To help ensure the safety and security of their AI systems, companies need to thoroughly consider and address all kinds of risks—external, physical, and digital among many others—and then communicate those risks to users. Although external risks tend to get the most attention, internal risks such as fraud can be just as serious. For each AI use case, companies need to assess whether the potential benefits sufficiently outweigh the associated risks.

AI you can trust

AI ethics is emerging as the single biggest challenge to continued AI progress and widespread deployment—and it’s a challenge companies can no longer ignore now that AI is becoming an essential business capability. The Trustworthy AI framework offers a structured and comprehensive way to think about AI ethics, helping companies design, develop, deploy, and operate AI systems they can trust.

Contributing Authors:

Irfan Saif, Principal, Deloitte Risk and Financial Advisory

Irfan Saif is a principal with Deloitte & Touche LLP and has more than 20 years of IT Consulting experience, specializing in cybersecurity and risk management. He is responsible for driving innovation for Deloitte’s cyber business. He also serves as a member of Deloitte’s CIO program and Cyber Risk leadership teams.

Benna Ammanath – Managing Director, Deloitte Consulting LLP

Beena Ammanath is a managing director with Deloitte Consulting LLP and award-winning senior executive with extensive global experience in artificial intelligence and digital transformation. Her knowledge spans across e-commerce, financial, marketing, telecom, retail, software products, services, and industrial domains with companies such as HPE, GE, Thomson Reuters, British Telecom, Bank of America, E*TRADE and a number of Silicon Valley startups. She is the founder and CEO of Humans For AI Inc. She has co-authored the book AI Transforming Business.


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