Five Ways to Build Ethics and Trust in AI

Five Ways to Build Ethics and Trust in AI-01

The applications of Artificial Intelligence (AI) are constantly expanding, opening up new possibilities in workflows, processes, and technological solutions. In a digitally connected future, where machines and humans work together for remarkably impressive results, companies that successfully adopt ethical AI will have an advantage.

As AI becomes more pervasive in people’s daily lives, the conversation shifts from technological advancement to the ramifications of using AI. When it comes to AI applications, security, privacy, ethics and bias are becoming increasingly important. Artificial intelligence (AI) helps decision-making, posing risks such as mimicking or amplifying human biases. As a result, it is critical for businesses to ensure that AI systems are transparent and fair

However, how does one ensure that AI is aligned with their business models and fundamental values while leveraging it to achieve the best possible results? How do they create trustworthy AI systems?

Designing AI systems that are responsible

The ethics of high-stakes AI applications have become a controversial issue. Even though AI technologies help with decision-making they come with several risks such as simulating human biases through the ML programming. Due to historical human biases, even when datasets reflect adequate demographic representations, AI output may still present compromised results.

Also Read: Three Enterprise AI Trends in 2022

While ethics in AI is still a work in progress, responsible AI entails creating systems that are bound by fundamental guidelines that discriminate between permissible and illicit uses of AI. AI systems must be transparent, human-centric, interpretable and socially helpful in order to be considered as responsible AI.

Here are five steps to developing trustworthy AI that organizations can adopt. 

Begin at the very top

Most top-level management are aware of common ethical or compliance risks in their industry, but many are still unaware of how AI is built and deployed within their companies. Leaders must be educated on the principles of trustworthy AI so that they can take a clear stand on ethics and AI while also ensuring that it complies with applicable laws and regulations.

Perform risk assessments

The risks must be understood. Because AI is an emerging technology, its regulation and standards are yet undefined, and the threats are difficult to identify. To map high-risk operations and prepare mitigation, a risk assessment framework will be crucial.

Determine the baseline

The processes for trustworthy AI should be integrated into the management system of the company. Policies must be updated to communicate the company’s expectations for preventing AI solutions from having a negative impact on human rights and to assist in the resolution of any issues that arise. A reliable AI ethics and compliance policy will require a mix of non-technical and technical safeguards. 

Drive company-wide awareness of AI and ethics

Companies must educate their employees about the legal, societal, and ethical implications of collaborating with artificial intelligence. The risks associated with AI, as well as any business strategies for minimizing these risks, should be explained. Rather than focusing on compliance rules, workshops on ethics and values will be required to train a multidisciplinary workforce on trustworthy AI.

Bring Third Parties on Board

Companies rarely handle the development of AI-integrated products and services on their own. They should seek reciprocal commitments from third parties involved in AI development to ensure that the technology is reliable and created in accordance with the business standards. During the development of AI solutions, supplier audit procedures will need to be broadened to include an assessment of how suppliers manage potential detrimental human rights implications.

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Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.