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Imparting Transparency into AI Technology
The upcoming regulations pushing for transparency in automation technology will spur organizations to employ knowledge engineering into their AI operations.
FREMONT, CA – The rise in automation has led to organizations employing sophisticated artificial intelligence (AI) algorithms to massive datasets. However, the sophistication involved in the development of AI-powered automation cannot be understood by everyone but data scientists. It has led to a rising uneasiness regarding the accessibility and transparency in AI operations, and their potential impact.
AI is being incorporated into every sector across the industrial landscape. Consequently, organizations will need to build a standardized ethical approach to AI. Europe, China, and the US are steadily implementing regulations to promote trustworthy and accountable AI development. Hence, organizations must employ auditable technology that can accommodate transparency-based regulations.
The governance of AI is challenging since it is not easy to define. Although not yet comprehensive to cover the ethics, the general data protection regulation (GDPR) guarantees minimum transparency when it comes to automation technology. With the French, German, and UK governments endorsing consumer favoring laws, the EU is seeking to enhance accountability and data privacy.
The AI decision-making needs to be modeled after human expertise than complex data-driven matrices. Keeping humans in the loop during automated decision-making can enable organizations to impart the trust and understanding that will benefit both users and consumers.
Automation is revolutionizing several processes, including the security landscape. AI has enhanced the efficiency and volume of fraud processing. However, enterprises can ensure more transparency and engagement by enabling their consumers to understand the mechanisms involved in the process. Organizations can base their fraud detection models on data set by business experts rather than sophisticated data sets so that the decisions made by the fraud engines are transparent with an in-built audit trail.
The potential EU regulations will likely spur organizations to have a firmer grasp on the functionality of their tools. The AI implementation will require the involvement of technical AI specialists and subject matter experts. The demand for transparency is fueling the growth of human-governed AI.
The rise of explainable AI and auditable technology is integral to enhance transparency and ensure compliance. AI transparency is no longer a fringe issue and is gaining mainstream importance for organizations considering the incorporation of AI technology. Transparency will likely increase as enterprises incorporate knowledge engineering to accommodate the upcoming regulations.