With the rapid uptake of generative AI, investigating human perceptions of generated responses has become crucial. A major challenge is their `aptitude' for hallucinating and generating harmful contents. Despite major efforts for implementing guardrails, human perceptions of these mitigation strategies are largely unknown. We conducted a mixed-method experiment for evaluating the responses of a mitigation strategy across multiple-dimensions: faithfulness, fairness, harm-removal capacity, and relevance. In a within-subject study design, 57 participants assessed the responses under two conditions: harmful response plus its mitigation and solely mitigated response. Results revealed that participants' native language, AI work experience, and annotation familiarity significantly influenced evaluations. Participants showed high sensitivity to linguistic and contextual attributes, penalizing minor grammar errors while rewarding preserved semantic contexts. This contrasts with how language is often treated in the quantitative evaluation of LLMs. We also introduced new metrics for training and evaluating mitigation strategies and insights for human-AI evaluation studies.

The pandemic highlighted the need for digital banking transformation more than ever, as transactions moved out of branches, work was conducted remotely, and customers expected increasingly personalized experiences. In the past, financial institutions were able to gain a competitive advantage with broader distribution footprints and better pricing. Today, leading banks and credit unions differentiate by leveraging data, artificial intelligence (AI), applied analytics, and the power of cloud computing to innovate and deliver personalized engagements in real time. This more intelligent way of applying technology allows financial institutions to learn and react to changes in the marketplace or in the behavior of customers in a manner that can benefit both the financial organization and the customer. Combined with the potential of Fifth Generation (5G) cellular, the result is an enhanced customer experience that provides a competitive advantage far greater than geographic location, transforming the financial services industry.

The pandemic highlighted the need for digital banking transformation more than ever, as transactions moved out of branches, work was conducted remotely, and customers expected increasingly personalized experiences. In the past, financial institutions were able to gain a competitive advantage with broader distribution footprints and better pricing. Today, leading banks and credit unions differentiate by leveraging data, artificial intelligence (AI), applied analytics, and the power of cloud computing to innovate and deliver personalized engagements in real time. This more intelligent way of applying technology allows financial institutions to learn and react to changes in the marketplace or in the behavior of customers in a manner that can benefit both the financial organization and the customer. Combined with the potential of Fifth Generation (5G) cellular, the result is an enhanced customer experience that provides a competitive advantage far greater than geographic location, transforming the financial services industry.

People walk around in the city of London. Financial institutions are increasingly adopting AI as technological barriers have fallen and its benefits and potential risks have become clearer. Artificial intelligence (AI) technologies hold big promise for the financial services industry, but they also bring risks that must be addressed with the right governance approaches, according to a white paper by a group of academics and executives from the financial services and technology industries, published by Wharton AI for Business. The white paper details the opportunities and challenges of implementing AI strategies by financial firms and how they could identify, categorize and mitigate potential risks by designing appropriate governance frameworks. "Professionals from across the industry and academia are bullish on the potential benefits of AI when its governance and risks are managed responsibly," said Yogesh Mudgal, AIRS founder and lead author of the white paper.

Parity is among a growing crop of startups promising organizations ways to develop, monitor, and fix their AI models. They offer a range of products and services from bias-mitigation tools to explainability platforms. Initially most of their clients came from heavily regulated industries like finance and health care. But increased research and media attention on issues of bias, privacy, and transparency have shifted the focus of the conversation. New clients are often simply worried about being responsible, while others want to "future proof" themselves in anticipation of regulation.

Contract life cycle management systems have been around for decades, but the latest generation of AI-enabled tools can help elevate the contracting function. In recent years, organizations that have struggled to understand and manage the entirety of their obligations to customers and suppliers have shown increasing interest in their company's contract life cycle management (CLM). Specifically, organizations seem to be focused on CLM operating models, processes, and enabling technologies to manage these critical obligations. That appetite has increased in the wake of COVID-19, as many companies wrestle with a lack of visibility into their contracts across the enterprise. In the past, some organizations have standardized their processes within certain silos or even implemented CLM technology.

As the Managing Director-Technology and Head of NY/Canada Business Unit at Synechron, Ravnit is currently responsible for leading multiple global client relationships, driving business development and sales, IT Strategy and consulting, execution and delivery, P&L and program management for strategic financial clients. In addition, a vital part of his role is providing thought leadership around the impact of emerging technologies (particularly AI/Machine Learning/RPA) on the financial industry. He is a key contributor to Synechron's Accelerator programs, leveraging emerging technologies to address key business challenges in the BFSI space. Ravnit has significant techno-functional hands-on experience as well as a proven track record for execution and delivery of IT Solutions in capital markets and wealth management functions across asset classes, lines of businesses and front-/mid-/back-office functions. Prior to Synechron, Ravnit was a Senior Developer at Polaris Consulting & Services Limited.

Drones are all the rage today. Not a day goes by that we don't read about someone using a drone for something (good or bad) somewhere on Earth. AI or Artificial Intelligence has also made a resurgence. I say that because there was a time, not too long ago (about 7 years ago) when AI was also very popular and a number of movies featuring AI were produced by Hollywood. Now if we combine the two, what do we get?