Building a Data-Empowered Society: UCL’s Vision for Inclusive Innovation and Transformation

Written by Professor James Hetherington and Professor Alison Littlejohn, Pro-Vice-Provosts to the UCL Grand Challenges theme of Data and Empowered Societies

Purpose and Authors

This blog, authored by the Pro-Vice-Provosts Professor James Hetherington and Professor Alison Littlejohn, together with the UCL Grand Challenges team, outlines our initial thoughts on the Data Empowered Societies Grand Challenge. It aims to start a conversation about creating a human-centred, data-fuelled society. We have been reaching out to colleagues across UCL and have included areas flagged as critical within the sub themes. We have planned a number of meetings across UCL to refine this vision, starting with the Grand Challenge Kickoff event on January 23, 2025.

Introduction

Data is transforming our daily life; medicine and healthcare is being transformed, climate modelling is advancing, the way humans live and learn are all being transformed through new forms of data measurement, sharing, analysis and decision making. However, rapid advances can leave parts of society behind due to issues like machine bias and exclusion.

Therefore, our vision for a ‘data empowered society’ is one in which these advances enrich our society and enable us to make informed, inclusive decisions about technological advances. Our goal is that by the end of the decade, UCL will be independently assessed as a leader in inclusive, data-intensive research, education, and enterprise.

Key Terms

• Data: Information in various forms, including language, sound, and imagery. It encompasses all methods of working with data, from traditional knowledge systems to modern AI.
• Society: Groups of people making decisions and sharing work. Technological systems are social systems but often disconnected from other societal areas.
• Empowerment: Digital transformation should be human-centered, not just technology-driven. Decisions about technology’s benefits and harms are complex and often conflicted.

Goals

Our goal is to understand how change occurs during Digital Transformation, and use this to deliver beneficial impact — particularly to those most vulnerable in society as well as those who already have power and authority afforded by data.

To achieve this vision, we have worked with groups across UCL to identify and set out a series of potential sub themes to frame the Grand Challenge. These are our initial thoughts, intended to stimulate engagement, and we will continue to work with the UCL community to shape and deliver these goals.

On Monday 13 January 2025, the Prime Minister Sir Keir Starmer set out his government’s AI Action Plan at UCL East, identifying his vision and priorities for how AI could transform public services and deliver benefits for the UK. It outlined how innovations in AI technologies could address societal needs, such as cutting health waiting times, delivering economic benefits and supporting education, while prioritising a culture of data security, privacy and equipping the workforce with the skills they need for the future. UCL is at the forefront of AI innovation and has expertise across science and technology but also in subjects as diverse as data regulation, ethics and responsible innovation, which enables us to convene public conversations around AI and offer expert insights into how the UK can benefit from AI.

Sub-Themes

Knowing and Deciding in a Data Empowered Society

• Insight Generation: Data helps expand our understanding of the world, generating insights in novel ways. This includes combining diverse datasets (e.g., geospatial data with consumer data) to derive new insights, such as health outbreaks indicated by consumer purchases.
• Decision-Making: Data informs decisions in various sectors, including healthcare, urban planning, and crisis response. Governments and public bodies use data to choose new drugs for public healthcare, build new towns, and allocate funding for arts, culture, and research.
• Advanced Computational Methods: AI, machine learning, data analytics, and natural language processing aggregate data uniquely, revealing patterns and correlations that humans might miss.
• Digital Twins: Proxies based on machine learning and mechanistic reasoning guide planning and enable predictive maintenance. These models can be sophisticated, requiring large-scale compute power, or simple models providing useful insights through statistical inference.
• Uncertainties and Biases: Effective use of these technologies requires understanding and quantifying uncertainties. Machine-supported decisions must consider epistemological uncertainties and human factors. Marginalized communities often lack access to digital resources, making them susceptible to algorithmic biases.
• Data Stewardship: Ensuring data accuracy and standardization is crucial. Data gaps can obscure harm and hinder effective change. Governance processes and compliance frameworks must prevent harms from inappropriate data use.

Embodied Data

• Advancing Diagnostics and Patient Care: Genomic data is analyzed to diagnose genetic mutations linked to diseases, supporting targeted therapies. Real-world evidence methods assess treatment efficacy and safety in diverse populations.
• AI and Machine Learning: These technologies speed up drug development and personalized medicine. For example, Google AlphaFold predicts protein structures, reducing the need for bench research.
• Health and Beyond: Data’s empowerment extends to wellbeing, sports, entertainment, and culture. Robotics and novel sensing methods, such as digital touch, create new ways for our bodies to interact with the world.
• Environmental Challenges: Data and AI help understand and address rapid environmental changes and biodiversity loss.
• Human-Computer Interaction: Interdisciplinary methods support new forms of analysis and human-computer interaction. Mobile health and fitness applications collect real-time data for continuous monitoring and timely interventions.
• Ethical Considerations: Focus on data privacy, security, and equity. Transformations may advantage those already privileged, exacerbating societal divisions. Understanding access, equity, and representation issues is critical.

Data and the Lived Experience

• Daily Actions: Data influences almost every aspect of life, from purchasing decisions to forming relationships. Data-driven apps guide actions in workplaces, leisure, and retail activities.
• Workplace Integration: Office workers use apps that gather and assist with work data. Autonomous robots co-work with humans in warehouses and manufacturing sites. Health professionals use data-driven apps and AI for diagnoses and personalized healthcare.
• Leisure and Retail: Travelers and diners use online reviews and ratings. Fitness devices optimize training. Gamers analyze performance data. Retailers target shoppers based on purchase data.
• Disempowerment Risks: Social media algorithms can reduce the ability to disagree well. Digital technologies can mediate human interactions both helpfully and harmfully. Media influences attitudes and preconceptions, which can be exploited to misinform and manipulate.
• Challenges: Measuring and analyzing data representing values, identity, and feelings is difficult. Quantitative data can be reductive, and qualitative data is hard to record and analyze. Biases and misinformation can disadvantage and exclude swathes of society.
• Human-Centric Design: Approaches to data analysis should foreground human-centric methods to understand how data can empower communities.

Data Empowered University

• Innovation and Thought Leadership: Universities should lead in innovation and careful thought. Labs contain technologies that excite and astound, long before they leave the campus.
• Disconnect: There is often a disconnect between leadership in digital transformation and the reality of university operations. Students and staff compare their experience within the university to the rapidly transforming wider society.
• Data-Driven Activities: Universities should use rigorous statistical practices and academic-led thought in data-driven activities. This applies to professional services, teaching, and research.
• Prototypes and Proof of Concept: The Grand Challenge can signpost where work might take us through prototypes or proof of concept solutions. These can make a difference to small groups within the university.
• Community Collaboration: Bringing together researchers, teachers, students, and professional service teams to address digital transformation. Empowering projects in this space can make UCL a “living laboratory” for studying the transition to a data-empowered society.

Conclusion

Our vision is that through the Grand Challenges, we can make UCL a leader in data empowerment, working collaboratively across the university to address important issues facing our society and finding ways to use UCL’s expertise for public benefit. By harnessing UCL’s strengths across research, innovation and education, we can help support a more equitable society where data and innovation can improve and transform the lives of people across the world.