Vaccine hesitancy threatens public health, leading to delayed or rejected vaccines. Social media is a vital source for understanding public concerns, and traditional methods like topic modelling often struggle to capture nuanced opinions. Though trained for query answering, large Language Models (LLMs) often miss current events and community concerns. Additionally, hallucinations in LLMs can compromise public health communication. To address these limitations, we developed a tool (VaxPulse Query Corner) using the Retrieval Augmented Generation technique. It addresses complex queries about public vaccine concerns on various online platforms, aiding public health administrators and stakeholders in understanding public concerns and implementing targeted interventions to boost vaccine confidence. Analysing 35,103 Shingrix social media posts, it achieved answer faithfulness (0.96) and relevance (0.94).

The recently released artificial intelligence conversational agent, ChatGPT, has gained significant attention in academia and real life. A multitude of early ChatGPT users eagerly explore its capabilities and share their opinions on it via social media. Both user queries and social media posts express public concerns regarding this advanced dialogue system. To mine public concerns about ChatGPT, a novel Self-Supervised neural Topic Model (SSTM), which formalizes topic modeling as a representation learning procedure, is proposed in this paper. Extensive experiments have been conducted on Twitter posts about ChatGPT and queries asked by ChatGPT users. And experimental results demonstrate that the proposed approach could extract higher quality public concerns with improved interpretability and diversity, surpassing the performance of state-of-the-art approaches.

The debate over the police using machine learning is intensifying – it is considered in some quarters as controversial as stop and search. Stop and search is one of the most contentious areas of how the police interact with the public. It has been heavily criticized for being discriminatory towards black and minority ethnic groups, and for having marginal effects on reducing crime. In the same way, the police use of machine learning algorithms has been condemned by human rights groups who claim such programs encourage racial profiling and discrimination along with threatening privacy and freedom of expression. Broadly speaking, machine learning uses data to teach computers to make decisions without explicitly instructing them how to do it.

The vital role that cybersecurity plays in protecting our privacy, rights, freedoms, and everything up to and including our physical safety will be more prominent than ever during 2020. More and more of our vital infrastructure is coming online and vulnerable to digital attacks, data breaches involving the leak of personal information are becoming more frequent and bigger, and there's an increasing awareness of political interference and state-sanctioned cyberattacks. The importance of cybersecurity is undoubtedly a growing matter of public concern. We put our faith in technology to solve many of the problems we are facing, both on a global and personal scale. But as the world becomes increasingly connected, the opportunities for bad guys to take advantage for profit or political ends inevitably increases.

The debate over the police using machine learning is intensifying – it is considered in some quarters as controversial as stop and search. Stop and search is one of the most contentious areas of how the police interact with the public. It has been heavily criticised for being discriminatory towards black and minority ethnic groups, and for having marginal effects on reducing crime. In the same way, the police use of machine learning algorithms has been condemned by human rights groups who claim such programmes encourage racial profiling and discrimination along with threatening privacy and freedom of expression. Broadly speaking, machine learning uses data to teach computers to make decisions without explicitly instructing them how to do it.

Facebook founder Mark Zuckerberg has been receiving special coaching on how to present himself when he appears before US politicians demanding to know what he is doing to protect users' data, and how Russia was able to use his platform to allegedly meddle in the 2016 presidential election. Amid continuing controversy over the inappropriate harvesting of the data of up to 87 million Facebook users by British political consulting firm Cambridge Analytica, Mr Zuckerberg will try and reassure Congress he is taking the concerns of them and the general public seriously. He will also try to deflect the efforts of those who favour more stringent government regulation. "It's clear now that we didn't do enough to prevent these tools from being used for harm," he is expected to tell the House Committee on Energy and Commerce, according to written testimony released ahead of his appearance. "We didn't take a broad enough view of our responsibility, and that was a big mistake. It was my mistake, and I'm sorry. I started Facebook, I run it, and I'm responsible for what happens here."

Harry Farmer is a policy researcher at Involve. He is fascinated by the power of deliberative processes to enable governments to negotiate controversial policy decisions - particularly those presented by emerging technologies and demographic change. He currently works primarily on the Citizens for Public Service programme. Involve submitted evidence to the Science and Technology Select Committee's inquiry on Robotics and Artificial Intelligence. In our submission, we contend that the government's success in harnessing the potential of robotics and AI will depend to a large extent on its ability to develop policy that is sensitive to, and informed by, public concerns about these new technologies.