Blog Post

Top 8 Ways Companies are Putting in Safeguards to Use AI Responsibly‍

Eddie Wang
Aug 11, 2023
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To ensure the responsible use of AI, companies must adopt rigorous safeguards to mitigate potential harm and ensure ethical deployment & AI trustworthiness. In this blog post, we will explore 8 of the top ways companies are putting in protections to promote AI ethics.
responsible AI

Artificial Intelligence (AI)’s rapid ascent from fringe novelty to mainstream business accelerant has transformed industries as far reaching as healthcare to finance, software to freight logistics. While AI brings tremendous benefits, it also presents unique challenges and potential risks for both companies and consumers.

To ensure the responsible use of AI, companies must adopt rigorous safeguards to mitigate potential harm and ensure ethical deployment & AI trustworthiness. In this blog post, we will explore 8 of the top ways companies are putting in protections to promote AI ethics.

Ethical AI Frameworks

One of the fundamental ways companies are promoting responsible AI use is by establishing ethical AI frameworks. These “begin with the end in mind” frameworks define clear guidelines and principles for AI development and deployment. Just like doctors have ethical frameworks from which they base patient treatment plans, businesses who regularly use AI must think holistically about how they can responsibly use AI before allowing for widespread deployments.

With ethical frameworks in place, companies can consider the moral, legal, and societal implications while developing or utilizing AI models. Ethical AI frameworks emphasize transparency, fairness, and accountability to ensure AI systems serve the greater good without harming individuals or communities. This form of self-policing is necessary even as government-backed AI regulation is in its infancy.

To get started building your own Ethical AI Framework, check out Harvard Business Review’s resource as a starting point.

Diversity and Inclusivity in AI Development

Companies are recognizing the importance of diversity in AI development teams. By fostering diverse teams with varying backgrounds and perspectives, companies can reduce bias in AI algorithms. A diverse team can identify potential biases and address them during the development phase, leading to more inclusive and fair AI solutions.

This also holds true to a lesser extent with AI deployment teams. While an AI deployment team won’t be able to see exactly how training data was used to construct systems, they can at least evaluate AI systems against one another and against the baseline status quo with an eye towards ensuring that AI drives towards the right results.

Continuous Monitoring and Auditing

Responsible AI governance demands ongoing monitoring and auditing of AI systems. Forward thinking companies are implementing mechanisms to continuously assess AI performance and ensure adherence to ethical guidelines. 

Regular audits by trained experts can also help to identify potential issues, such as biases that may emerge over time, and ensure that AI remains aligned with its intended purpose. This is particularly relevant as small 1% errors can magnify over time when reinforced by self-learning AI systems. 

One example of this is how generative AI can become inbred when systems are trained by the output of other AI systems (Futurism, 2023). These destructive feedback loops can quickly reduce the net benefit of an AI system to negative when left in place.

Data Privacy and Security

Protecting user data is paramount in the responsible use of AI. Companies must adopt robust data privacy and security measures to safeguard sensitive information collected by AI systems. Privacy-preserving techniques, data anonymization, and encryption are some methods used to minimize data exposure and potential misuse.

Beyond these more technical safeguards, smart companies also need clearly reinforced policy guidance for employees around what data can or cannot be shared with AI in order to promote AI security. 

Human-in-the-Loop Approach

The human-in-the-loop approach involves integrating human oversight into AI systems' decision-making processes. This means that AI algorithms provide recommendations, but human experts have the final say. This approach ensures that critical decisions remain in human hands, reducing the risk of AI making decisions without proper ethical consideration.

Business leaders must delineate what level of decision making is appropriate for an AI to take on independently (e.g. automatically ordering goods to restock a SKU) and what level is not appropriate (e.g. deciding to open a store in a particular zip code).

Responsible AI Education and Training

Companies should also consider investing in educating their workforce about the ethical implications of AI and providing training on responsible AI practices. By raising awareness among employees, companies can foster a culture of ethical AI usage, ensuring that everyone involved understands the importance of AI safeguards and acts accordingly.

Many larger organizations already adopt or create training materials for their employees on a range of topics from customer service to legal compliance. Responsible AI usage is a new training curriculum that needs to be created or adapted for everyday use. Otherwise, AI accountability is not possible if teams aren't trained to work effectively with AI.

Choose responsible AI companies

There are many providers of AI services out there. Businesses have their choice of vendors and must be rigorous in examining and selecting only those vendors which clearly demonstrate a healthy respect for data privacy, security, and responsible AI development. 

Xembly’s one such company (see: Xembly’s data security page) which is leading the way in creating an intelligent AI assistant to support knowledge workers while seeking to build a responsible product that appropriately handles customer data. 

Collaboration with External Experts

As companies increasingly recognize the complexity of AI's ethical challenges, thoughtful leaders must also learn to seek external expertise to assess and validate their AI systems. Collaboration with academic researchers, non-profit organizations, and other external experts brings in unbiased perspectives and ensures a thorough evaluation of AI technologies.

Similar to how a corporate accounting team still relies on paid external auditors to verify their numbers for public reporting standards, external AI experts have a lot to offer with their unbiased perspectives on how a company is handling its own data and implementing AI systems. 

As AI continues to revolutionize industries and transform our lives, the responsible use of AI becomes ever more critical. Companies must take increasingly meaningful and proactive steps to implement safeguards in their organizations. By doing so, companies can build their AI trustworthiness and help team members and customers feel comfortable in the new reality of working alongside AI.

If your company has yet to make the leap to AI productivity, consider exploring Xembly as a responsible first step toward embracing AI-assisted scheduling, note taking, and productivity while protecting the data and processes that are vital to your business. 

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