AI adoption is essential for any business that wants to remain competitive in its industry. However, technology poses several risks that must be managed to protect the organization’s reputation and keep systems running smoothly. A wise leader will develop risk management plans that ensure smooth operations and reduce reputational damage while establishing the company as an industry leader.

The Risk: Bias

Artificial intelligence sources information from the internet, a system with intentional and unintentional biases. Without human oversight, the technology can integrate biases in various applications. It can bias applicant tracking systems to discriminate against genders, reduce accuracy in AI healthcare diagnostics, target marginalized communities in law enforcement, and publish biases in the organization’s content.

Addressing Risk

Organizations can minimize bias in AI by:

• Creating a governance strategy for the responsible use of AI technology
• Develop human teams and datasets to establish metrics and eliminate biases
• Establish a bias mitigation process throughout the AI lifecycle, including the learning model, data processing, and monitoring.

The Risk: Cybersecurity

The more digital the world becomes, the higher the risk of cybersecurity. AI is critical; bad actors can hack into systems to launch cyberattacks. There have also been instances where AI publishes private data in public forums.

Addressing Risk

Companies can ensure information is secure by:

• Creating an AI security strategy
• Conducting a risk assessment to identify and address gaps and vulnerabilities
• Provide AI training that focuses on cybersecurity

The Risk: Data Privacy

AI is often used to scan vast datasets that may contain personal and sensitive information. For example, AI may scan applications during hiring, providing access to sensitive data. Chatbots and web crawlers may also access information without the owners’ consent.

Addressing Risk

Organizations can reduce data privacy issues by:

• Seeking consent from parties before allowing AI data sharing
• Inform parties on how data is collected and what it is used for
• If possible, use synthetic data for reviewal purposes

The Risk: Environmental Harm

AI requires large data centers that use vast amounts of energy. The data centers also generate a lot of heat, which requires water for cooling. Statistics show that a natural processing model emits over 600,000 pounds of carbon dioxide while training GPT models consume 5.4 million liters of water.

Addressing Risk

Companies can reduce the environmental harm associated with AI technology by:

• Considering energy-efficient models, frameworks, and data centers, or those powered by renewable energy
• Training on less data and simplified architecture
• Reusing existing models or implementing transfer learning, which involves using pre-trained models to make data sets more efficient
• Consider serverless architecture and hardware for AI applications

The Risk: Intellectual Property Infringement

AI often mimics copyrighted online property, including artwork, music, images, sounds, and content. Organizations that use these properties may find themselves with a lawsuit, even if the crime was unintentional.

Addressing Risk

Companies can minimize the risk of using copyrighted property by:

• Implementing checks to ensure you remain compliant with copyright laws
• Being cautious when feeding data into algorithms to avoid exposing protected properties
• Monitoring output to prevent IP infringement

The Risk: Job Loss

AI threatens to replace humans in various roles, leading to organizational job losses. Even though AI is expected to create new jobs, it can also make some positions obsolete. Threatened roles include data entry, customer service, and administrative positions.

Addressing Risk

Organizations can ensure a thriving workforce by:

• Focusing on enhancement, not replacement, by training employees to work alongside AI for more efficient processes
• Investing in technology that requires human oversight
• Transforming business processes to create an evolving ecosystem that integrates humans and machines.

The Risk: Lack of Explainability and Transparency

Artificial intelligence generates content and supports decision-making, but it doesn’t explain what it bases its facts on and where it got its information. Organizations that put blind faith in AI may be questioned, resulting in a lack of explainability and transparency. It can lead to reputational damage and inaccurate output.

Addressing Risk

Companies can promote transparency by:

• Adopting explainable techniques such as Interpretable Model-Agnostic Examples (LIME), which explain how algorithms support decisions, and Deep Learning Important Features (DeepLIFT), which establish a traceable link between neurons in networks
• AI governance to establish audits and reviews
• Exploring AI tools that provide explainability

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