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The hype around AI may mask the real concerns about how it’s being built and operated. The debate about throttling AI garners more attention, with the ethics of AI taking a back seat. But, hype or not, AI exists in society and will continue to permeate more aspects of our lives going forward.
At HTCNXT, we understand the additive role of AI and its potential to propagate with adverse consequences. We have, therefore, brought together the finer aspects of Ethical and Usability Principles to create Humane AI, our ethical guide to delivering Responsible AI solutions. Our solutions follow the Humane AI guide and are tested against 7 fundamental principles for Responsible AI, Ethics of Artificial Intelligence, and AI Governance.
Effective IT platforms have traditionally incorporated control systems capable of auditing and tracing performance issues or failures. The same principle applies to AI systems. However, AI is frequently developed within black boxes, where failures are considered impenetrable glitches, rendering them unfixable.
AI is often credited with intelligence surpassing human decision-making capabilities. While it is true that AI systems can evaluate parameters and data beyond human capacity or understanding, these inputs frequently stem from historical human networks. Consequently, our biases become embedded in the AI systems we create. As AI systems are put into operation, they can veer further away from their initially well-designed intentions. Therefore, removing bias is a crucial aspect of developing ethical AI systems.
AI systems inherently possess a dense and extensive nature. Evaluating their effectiveness primarily relies on observing outcomes. However, achieving transparency can be challenging, especially when these systems deviate significantly from established ranges. Even within those parameters, subtle drifts can occur, making detection and transparency difficult. Therefore, it is crucial to incorporate explainable capabilities into AI systems to address this requirement effectively.
Our solutions follow the Human AI guide and are tested against 7 fundamental principles.
Violation of the right to personal information, both for an individual or a corporate entity, is a growing concern. With data being consumed by AI engines at a rapid pace and consumers being co-opted to give away their right to data with lengthy user agreements, data misuse is possible. Ensuring that an LLM creating content, code, visuals, or designs is not violating private data is important. Here’s how we ensure privacy compliance:
Today, AI applications are often used in ways they weren’t designed for, assuming they possess general intelligence and their outputs are unquestionably true. However, recent instances of AI hallucinations and unexpected outcomes due to usage creep highlight the necessity of robust governance for AI engines. This entails thorough monitoring, the capacity to resort to “dumb” processes when AI fails, and the ability to reproduce AI results consistently.
Effective IT platforms have traditionally incorporated control systems capable of auditing and tracing performance issues or failures. The same principle applies to AI systems. However, AI is frequently developed within black boxes, where failures are considered impenetrable glitches, rendering them unfixable.
Tech security is an ever-evolving battleground, and AI systems introduce heightened risks. These risks extend beyond data breaches to encompass data-driven decisions that can have tangible consequences. Consequently, security measures for AI must undergo extra scrutiny during implementation and necessitate ongoing attention and updates to address the ever-shifting threat landscape.
Accountability is a fundamental need in IT systems built on established principles and best practices. However, AI poses unique challenges due to the complexity of data and the opacity of underlying mathematical algorithms. When AI systems deviate from their governing parameters, linking their performance (not just technical performance) to human control structures within organizations becomes a distinct challenge for AI projects.
AI is often credited with intelligence surpassing human decision-making capabilities. While it is true that AI systems can evaluate parameters and data beyond human capacity or understanding, these inputs frequently stem from historical human networks. Consequently, our biases become embedded in the AI systems we create. As AI systems are put into operation, they can veer further away from their initially well-designed intentions. Therefore, removing bias is a crucial aspect of developing ethical AI systems.
AI systems inherently possess a dense and extensive nature. Evaluating their effectiveness primarily relies on observing outcomes. However, achieving transparency can be challenging, especially when these systems deviate significantly from established ranges. Even within those parameters, subtle drifts can occur, making detection and transparency difficult. Therefore, it is crucial to incorporate explainable capabilities into AI systems to address this requirement effectively.
AI has permeated every part of our lives, evolving from recognizing patterns to achieving human efficiency. Treading deeper into the AI landscape, generative AI (GenAI) has become the new normal, reshaping every industry.
All industry use cases and market predictions point in the direction of AI-driven contact centers — as the next strategic step for boosting agent productivity, supercharging customer experience, and increasing operational efficiency.
Generative AI (GenAI) has expanded the horizons of innovation and challenged us to rethink the potential of workflows, efficiency, and intelligence.
In the past year, Generative AI (GenAI) has emerged as one of the most remarkable breakthroughs, triggering a transformative wave across the global economic and IT landscape.
In this age and probably in the next century, artificial intelligence (AI) will be the cornerstone for futuristic enterprises seeking to make an impact.
In the dynamic landscape of artificial intelligence, Large Language Models (LLMs) stand as formidable entities, capable of processing vast amounts of information and making decisions that impact users.
The pressure is on. Every enterprise needs to be an AI-first organization. Yet, building formidable AI capabilities presents its own unique set of challenges.
Enterprises are no strangers to disruptions, with uncertainty lurking around every corner. In this dynamic environment, adaptability and resilience aren’t just admirable qualities but essential for business survival.
The advent of Generative AI models had a significant impact across industries – but most importantly, it accelerated the mainstream adoption of automation, thus enhancing speed and productivity.
Case Studies
We prevented more than 80% of wrong supplier codes in its first iteration, enabling a global automobile company to optimize its production with AI and ML-led solutions.
Case Studies
We helped a large public research university in California build AI/ML-driven solutions for managing technical data.
Case Studies
We enabled a multinational mass media and entertainment conglomerate enhance its interactive training modules with a VR-based 360° solution.
Case Studies
We enabled a 70% reduction in the turnaround time for auto claims, helping the insurer reimagine claims intake with an AI-based FNOL solution.
Machine learning and deep learning are crucial technology components to build robust foundations for AI implementation.
Can machines think? It’s a question that has ignited curiosity, contemplation, and even trepidation among tech enthusiasts and skeptics alike.
With the line between human and artificial intelligence fading every passing day, the application areas of AI are expanding incrementally.
AI has been envisioned as a business multiplier for decades, but its adoption has only recently gained pace.
The retail industry is rapidly adopting Machine Learning, Computer Vision AI, and smarter AI-led solutions to enhance customer experiences, drive supply chain optimization, smarter in-store operations, and more. Generative AI, however, can help them achieve more.
Generative AI models and similar architectures are known for their impressive and versatile features. These models have revolutionized natural language understanding and generation.
The insurance industry is vital to the growth of the global economy, providing financial protection and stability to individuals and businesses.
In the ever-evolving digital landscape, technological paradigm shifts have redefined how we interact with digital content.
AI-powered solutions are helping businesses gain deeper insights to make data-driven decisions with enhanced precision.
Talk to our domain experts to understand the best Enterprise AI use cases for your business.
Talk to our domain experts to understand the best use cases of Enterprise AI for your business.
© Copyright 2023 HTC Global Services. All Right Reserved
© Copyright 2023 HTC Global Services. All rights reserved