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CMO Insights and Analysis from Deloitte

AI Ethics Come Into Focus as Workplace Evolves

As AI becomes more powerful, the duty falls on management and boards to ensure its responsible use.

A growing number of companies see AI as critical to their future, but concerns about possible misuse of the technology are on the rise too, according to a recent Deloitte survey. More than half (56%) of surveyed executives say they expected cognitive technologies to “transform” their companies within three years, while about a third of respondents (32%) name ethical risks as one of their top three concerns about the technology.

Many initiatives to tackle the ethical questions surrounding AI have taken shape in recent years, primarily driven by advancements in AI technologies, growing adoption, and the growing influence of AI in business decision-making. Still, AI can present significant opportunities for businesses even as it introduces some novel risks to be managed.

Diverse Ethical Risks

Prominent risks can emerge associated with ethical AI design, development, and deployment. For example, companies are already using AI to automate tasks, with some aiming to reduce their workforces in the process. The survey also noted that 36% of respondents saw job cuts from AI-driven automation rising to the level of an ethical risk. Even jobs that are not eliminated may be affected in some way by AI, prompting some organizations to find ways to use the technology to increase opportunities for employees while mitigating negative effects.

Bias is another area of risk. AI systems learn from the datasets with which they are trained. Depending on how a dataset is compiled or constructed, the potential exists that the data could reflect assumptions or biases—such as gender, race, or income—that could influence the behavior of a system based on that data. Generally, these systems’ developers intend no bias, but many have reported AI-driven instances of bias or discrimination in application areas such as recruiting, credit scoring, and judicial sentencing.¹

Additional challenges related to data use may arise when companies collect personal data from consumers when they register for or use products or services. That data can be used to train AI-based systems for purposes such as targeted advertising and promotions and personalization. However, ethical issues arise when that information is used for a different purpose—say, to train a model for making employment offers—without users’ knowledge or consent.

Making AI Ethics a Priority

Accelerating adoption of AI technologies, and growing awareness of various ethical risks associated with them, calls for urgency in designing approaches and mechanisms to manage those risks. In fact, enterprises across industries such as financial services, life sciences and health care, retail, and media are joining consortia to collaborate with technology vendors, universities, governments, and others in their respective industries to promote ethical AI standards and solutions.²

Whether AI will eliminate jobs or transform them, an ethical response for companies is to begin advising employees on how AI may affect them in the future.

To protect their stakeholders and reputation, and to fulfill ethical commitments, companies may need to think differently about how AI-powered systems are designed, built, and deployed. Some considerations for management and boards include:

Alleviate employee anxiety. Whether AI will eliminate jobs or transform them, an ethical response for companies is to begin advising employees on how AI may affect them in the future. This could include retaining workers whose tasks are targeted for automation or whose work may entail using automated systems—or giving them time to seek new employment. Companies in technology, financial services, energy and resources, and telecom have already started preparing their employees to stay relevant in AI-driven future.³

Establish AI governance. Designing ethics into AI often starts with determining what matters to stakeholders, including employees, customers, regulators, and the general public. Companies may want to set up a dedicated AI governance and advisory committee that includes cross-functional leaders and external advisers who engage with stakeholders and oversee governance of AI-enabled solutions. The group also could focus on regulatory issues to not only track evolving developments but help shape them.

Avoid bias. Training is one way to help AI developers test for and remediate systems that unintentionally encode bias and treat users or other affected parties unfairly. Researchers and companies are introducing tools and techniques for that purpose. For example, there are analytics tools that can automatically detect how data variables may be correlated with sensitive variables such as age, sex, or race; tests to identify algorithmic bias that may generate decisions that are unfair to certain populations; and methods for auditing and explaining how machine learning algorithms generate their outputs. To effectively manage the risk, the analytics can be integrated into new AI-based technologies, control structures, and processes. That is one way to help monitor that AI technologies are making decision fairly and not propagating biases when providing recommendations.

Build trust through transparency. In an era of opaque, automated systems and deepfakes (realistic but synthetic images, videos, speech, or text generated by AI systems), companies can help build trust with stakeholders by being transparent about their use of AI. For instance, rather than masquerade as humans, intelligent agents or chatbots can identify themselves as such. Companies also may want to disclose the use of automated decision systems that affect customers, and where possible, clearly explain what data is collected, what is done with it, and how its usage affects customers.


New technology always comes with benefits and risks, and AI is no different. Effective leaders strike an appropriate balance between achieving their goals and fulfilling their responsibilities to a diverse set of stakeholders including employees, customers, and investors. Even as they seek to take advantage of AI to improve business performance, companies can strive to leverage it in an ethically responsible way.

―by David Schatsky, managing director, U.S. Innovation, Deloitte LLP; Vivek Katyal, Global Data Risk and Analytics leader, Deloitte & Touche LLP’s Risk and Financial Advisory practice; Satish Iyengar, senior manager, Deloitte & Touche LLP; and Rameeta Chauhan, assistant manager, Deloitte Services India Pvt. Limited.

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