Recently, research into chatbots (also known as conversational agents, AI agents, voice assistants), which are computer applications using artificial intelligence to mimic human-like conversation, has grown sharply. Despite this growth, sociology lags other disciplines (including computer science, medicine, psychology, and communication) in publishing about chatbots. We suggest sociology can advance understanding of human-chatbot interaction and offer four sociological theories to enhance extant work in this field. The first two theories (resource substitution theory, power-dependence theory) add new insights to existing models of the drivers of chatbot use, which overlook sociological concerns about how social structure (e.g., systemic discrimination, the uneven distribution of resources within networks) inclines individuals to use chatbots, including problematic levels of emotional dependency on chatbots. The second two theories (affect control theory, fundamental cause of disease theory) help inform the development of chatbot-driven interventions that minimize safety risks and enhance equity by leveraging sociological insights into how chatbot outputs could attend to cultural contexts (e.g., affective norms) to promote wellbeing and enhance communities (e.g., opportunities for civic participation). We discuss the value of applying sociological theories for advancing theorizing about human-chatbot interaction and developing chatbots for social good.

Despite significant advancements in conversational AI, large language model (LLM)-powered chatbots often struggle with personalizing their responses according to individual user characteristics, such as technical expertise, learning style, and communication preferences. This lack of personalization is particularly problematic in specialized knowledge-intense domains like IT/cybersecurity (ITSec), where user knowledge levels vary widely. Existing approaches for chatbot personalization primarily rely on static user categories or explicit self-reported information, limiting their adaptability to an evolving perception of the user's proficiency, obtained in the course of ongoing interactions. In this paper, we propose ProfiLLM, a novel framework for implicit and dynamic user profiling through chatbot interactions. This framework consists of a taxonomy that can be adapted for use in diverse domains and an LLM-based method for user profiling in terms of the taxonomy. To demonstrate ProfiLLM's effectiveness, we apply it in the ITSec domain where troubleshooting interactions are used to infer chatbot users' technical proficiency. Specifically, we developed ProfiLLM[ITSec], an ITSec-adapted variant of ProfiLLM, and evaluated its performance on 1,760 human-like chatbot conversations from 263 synthetic users. Results show that ProfiLLM[ITSec] rapidly and accurately infers ITSec profiles, reducing the gap between actual and predicted scores by up to 55--65\% after a single prompt, followed by minor fluctuations and further refinement. In addition to evaluating our new implicit and dynamic profiling framework, we also propose an LLM-based persona simulation methodology, a structured taxonomy for ITSec proficiency, our codebase, and a dataset of chatbot interactions to support future research.

Peter Kyle, the UK's secretary of state for science, innovation and technology, has said he uses ChatGPT to understand difficult concepts The UK's technology secretary, Peter Kyle, has asked ChatGPT for advice on why the adoption of artificial intelligence is so slow in the UK business community – and which podcasts he should appear on. This week, Prime Minister Keir Starmer said that the UK government should be making far more use of AI in an effort to increase efficiency. "No person's substantive time should be spent on a task where digital or AI can do it better, quicker and to the same high quality and standard," he said. Now, New Scientist has obtained records of Kyle's ChatGPT use under the Freedom of Information (FOI) Act, in what is believed to be a world-first test of whether chatbot interactions are subject to such laws. These records show that Kyle asked ChatGPT to explain why the UK's small and medium business (SMB) community has been so slow to adopt AI.

On Monday, users found that their history contained titles pertaining to unfamiliar topics or functions, as well as titles written in other languages indicating that the flaw was a worldwide issue. Some on Reddit have reported seeing other types of information, but did not provide verifiable evidence to back up these claims. "I see someone else's phone number as the phone number tied to my account. I'm concerned but not concerned enough to quit the app," stated one user. Another alleged that they had signed up for ChatGPT Plus, the $20 (£16) per month subscription plan for the platform, under another email that had become linked to their account and as a result were not granted access to the service.

Artificial Intelligence (AI), as one of the leading technological trends, continues to grow in popularity among marketers and sales professionals, and has evolved into an essential tool for brands seeking to provide a hyper-personalized, exceptional customer experience. AI-enhanced customer relationship management (CRM) and customer data platform (CDP) software is now available, bringing AI to the enterprise without the high costs previously associated with the technology. On the basis of exclusive interactions with leaders in the BFSI sector, Nidhi Shail Kujur of Elets News Network (ENN) explores how with constantly evolving technologies, the banking and financial services industry promises to exceed customer expectations. The banking industry is undergoing significant change, particularly with the spread of customer-centricity. We live in a world where the majority of people have access to the internet.

Machine learning is streamlining and optimizing processes ranging from credit decisions to quantitative trading and financial risk management. This exciting technology has the potential to transform financial services business models and markets for trading, credit and blockchain-based finance, reduce friction and enhance product offerings. Machine learning is a subset of artificial intelligence that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon's Alexa and Apple's Siri improve every year thanks to constant use by consumers coupled with the machine learning that takes place in the background. Machine learning has grown substantially within the finance industry, enabled by the abundance of available data and the increase in the affordability of computing capacity.

Through advances in their conversational abilities, chatbots have started to request and process an increasing variety of sensitive personal information. The accurate disclosure of sensitive information is essential where it is used to provide advice and support to users in the healthcare and finance sectors. In this study, we explore users' concerns regarding factors associated with the use of sensitive data by chatbot providers. We surveyed a representative sample of 491 British citizens. Our results show that the user concerns focus on deleting personal information and concerns about their data's inappropriate use. We also identified that individuals were concerned about losing control over their data after a conversation with conversational agents. We found no effect from a user's gender or education but did find an effect from the user's age, with those over 45 being more concerned than those under 45. We also considered the factors that engender trust in a chatbot. Our respondents' primary focus was on the chatbot's technical elements, with factors such as the response quality being identified as the most critical factor. We again found no effect from the user's gender or education level; however, when we considered some social factors (e.g. avatars or perceived 'friendliness'), we found those under 45 years old rated these as more important than those over 45. The paper concludes with a discussion of these results within the context of designing inclusive, digital systems that support a wide range of users.

The Co-arthers of this post are Yue Cathy Chang an SVDS alumi, and Cindi Thompson,Principal Data Scientist at Silicon Valley Data Science. There article was originally posted on the Silicon Valley Data Science blog. From asking Amazon Alexa for traffic conditions, to receiving helpful tips from Slackbot, to using WeChat to book doctor's appointments, bots are becoming omnipresent in our lives. The bot market is hot! There's a plethora of companies and investments in bots: VentureBeat's 2016 Bots Landscape shows just under 200 companies ranging from personal assistants to AI tools to messaging, $22B in funding, and a very hefty $159B in valuation.

From asking Amazon Alexa for traffic conditions, to receiving helpful tips from Slackbot, to using WeChat to book doctor's appointments, bots are becoming omnipresent in our lives. The bot market is hot! There's a plethora of companies and investments in bots: VentureBeat's 2016 Bots Landscape shows just under 200 companies ranging from personal assistants to AI tools to messaging, $22B in funding, and a very hefty $159B in valuation. In this post, we explain why chatbots are rising in popularity with banks, the opportunities and challenges chatbots present, and where data and data science fit into the puzzle. Increasingly, banking institutions are using chatbots for "conversational commerce" or "Voice-First Banking"--allowing banks to interact with customers (in real-time if desired) via messaging and digital platforms.