Behavioural Data Science Week
Ganna Pogrebna
Keynote Speaker| Author| Host| Professor, Behavioral Data Science Pioneer| Exec Dir. AI & Cyber Futures Institute| Lead, Behavioral Data Science at Turing| Winner, TechWomen100 & Women in AI APAC | YouTube: Data-driven
Issue 10
August 29, 2024
Editorial Note
Welcome to this week's edition of Behavioural Data Science Week. In both our personal and professional lives, ideas flow constantly. They emerge in brainstorming sessions, during quiet moments of reflection, or through spontaneous conversations. However, many of these ideas, despite their potential, never see the light of day. They end up in what can be metaphorically described as a "graveyard of ideas"—a place where unexecuted concepts, unfinished projects, and abandoned initiatives linger, often forgotten.
This phenomenon is not unique to individuals; businesses, too, accumulate a vast number of unimplemented ideas. The question arises: Can Artificial Intelligence (AI) serve as a tool to resurrect, organise, and breathe new life into these dormant ideas, or does it risk adding more layers to the graveyard?
As you read through, remember that everything written below is my own personal take on the topic. You are welcome to agree or disagree. Enjoy!
If you found this content interesting or helpful but do not have time to write a comment, please, leave "10" in the comments section so I know this content resonates with you.
Yours in discovery,
Ganna
Navigating the Graveyard of Ideas – Can AI Help or Hinder?
The "graveyard of ideas" is a familiar concept for both people and organisations. Many projects never progress past the initial stages, and ambitious concepts often get set aside, creating a lingering sense of missed opportunities. As AI increasingly influences our lives and work environments, we must consider whether it can effectively revive and organize these neglected ideas, or if it might complicate matters further by adding additional layers of complexity.
It is estimated that individuals have between 6,000 to 60,000 thoughts per day, many of which are fleeting ideas that are never acted upon. In the corporate world, the situation is equally complex. A study by Accenture found that up to 70% of corporate ideas never reach the implementation stage, leaving a vast "graveyard" of potential innovations that could have transformed businesses. This graveyard of ideas is a space where unfulfilled potential languishes, often because there simply isn't enough time, resources, or structured processes to nurture every idea into reality.
On the one hand, Artificial Intelligence (AI) may present a compelling proposition when it comes to managing this vast array of sidelined ideas. On the other hand, is it really so?
The Promise of AI in Reviving Ideas
By its very nature, AI can process and analyse enormous volumes of information much more efficiently than any human could. This ability to sift through large datasets and identify patterns is particularly useful in organising the chaotic jumble of ideas that often clutter the minds of individuals and the databases of organisations. For example, Google employs AI-driven systems to manage its vast troves of data, allowing it to surface innovative concepts that might otherwise be buried under the sheer weight of information. In this context, AI serves as a powerful tool for bringing order to chaos, enabling both individuals and businesses to identify the most promising concepts and discard those that are less viable. The idea is that by using AI to categorise and prioritise ideas, organisations may potentially focus their resources on the concepts that have the highest potential for success, thereby turning the graveyard of ideas into a wellspring of opportunity.
Moreover, AI’s capability to evaluate the feasibility of ideas is a significant advantage, especially in business settings where resources are limited, and the stakes are high. AI can analyse market trends, historical data, and resource availability to assess whether a particular idea is worth pursuing. This has already been implemented in product development processes, where AI simulations can predict how a new product might perform in the market, providing valuable insights that guide decision-making. In such cases, AI doesn’t just help in organising ideas but also in making informed decisions about which ideas to bring to life.
The Risks of AI in Idea Management
However, the very strengths of AI—its reliance on data, its ability to analyse and predict based on past patterns—can also be its greatest weaknesses when it comes to fostering true innovation. AI, by design, works best when there is a wealth of data to draw upon. It identifies what has worked in the past and projects those patterns into the future. But this is precisely where AI can fall short, particularly when it comes to nurturing groundbreaking ideas that defy conventional wisdom.
In creative industries, this limitation is particularly pronounced. While AI can generate content or art that mirrors existing styles, it often struggles to produce something genuinely original. AI-generated music or visual art, for instance, might adhere to the structure and aesthetics of what has been popular in the past, but it rarely captures the emotional depth or innovative flair that comes from human creativity. In a business context, this means that AI might prioritize ideas that fit neatly within established norms, while potentially overlooking disruptive concepts that could lead to significant breakthroughs. Furthermore, the integration of AI into the idea management process can lead to what is often referred to as "analysis paralysis." AI can generate an overwhelming number of options, each backed by data and analysis, but the sheer volume of possibilities can make it difficult for decision-makers to take action. This flood of analysed data can paralyse rather than empower, adding more ideas to the graveyard rather than resurrecting them.
Algorithmic bias is another significant risk. AI systems are trained on historical data, and if that data contains biases—whether in terms of gender, race, or socioeconomic status—those biases will be perpetuated in the AI’s outputs. This can result in AI systematically undervaluing or ignoring ideas from underrepresented groups, reinforcing existing inequalities and narrowing the scope of innovation. For example, an AI system trained predominantly on data from successful startups might favor ideas that align with Silicon Valley norms, potentially missing out on innovative concepts from diverse cultural or economic backgrounds.
Striking a Balance: The Way Forward
So, does AI offer a solution to the graveyard of ideas, or does it risk making things worse? The answer lies in striking a balance between leveraging AI's capabilities and retaining the essential human elements of creativity and intuition. AI can indeed play a role in organising and evaluating ideas, especially in contexts where data-driven decision-making is paramount. However, the final judgment on which ideas to pursue should always involve human insight. The "human-in-the-loop" approach is one promising model that has gained traction. In this approach, AI and human decision-makers collaborate, each bringing their unique strengths to the table. AI can handle the heavy lifting of data analysis and pattern recognition, allowing humans to focus on the creative and intuitive aspects of decision-making. For instance, in the pharmaceutical industry, AI can analyse large datasets to identify potential drug candidates, but it is the human researchers who interpret these findings, design experiments, and make strategic decisions about which compounds to develop further.
领英推荐
While AI has the potential to help manage and act on the graveyard of ideas, it is not a magic bullet. Its effectiveness depends on how it is integrated into the broader decision-making process. By recognising the limitations of AI and ensuring that human creativity and judgment remain central, individuals and organisations can harness AI as a tool to foster innovation rather than contribute to the accumulation of unrealised potential. By finding this balance, we can turn the graveyard of ideas into a thriving garden of innovation.
Research Highlights
These are the studies combining behavioural science and data science components, which caught my eye this week. Note that inclusion in this list does not constitute an endorsement or a recommendation. It is just something I found interesting to read.
Our contribution aims to propose a novel research agenda for behavioural public administration (BPA) regarding one of the most important developments in the public sector nowadays: the incorporation of artificial intelligence into public sector decision-making. We argue that this raises the prospect of distinct set of biases and challenges for decision-makers and citizens, that arise in the human-algorithm interaction, and that thus far remain under- investigated in a bureaucratic context. While BPA scholars have focused on human biases and data scientists on ‘machine bias’, algorithmic decision-making arises at the intersection between the two. In light of the growing reliance on algorithmic systems in the public sector, fundamentally shaping the way governments make and implement policy decisions, and given the high-stakes nature of their application in these settings, it becomes pressing to remedy this oversight. We argue that behavioural public administration is well-positioned to contribute to critical aspects of this debate. Accordingly, we identify concrete avenues for future research, and develop theoretical propositions.
Personality psychology has long been grounded in data typologies, particularly in the delineation of behavioural, life outcome, informant–report, and self–report sources of data from one another. Such data typologies are becoming obsolete in the face of new methods, technologies, and data philosophies. In this article, we discuss personality psychology's historical thinking about data, modern data theory's place in personality psychology, and several qualities of big data that urge a rethinking of personality itself. We call for a move away from self–report questionnaires and a reprioritization of the study of behaviour within personality science. With big data and behavioural assessment, we have the potential to witness the confluence of situated, seamlessly interacting psychological processes, forming an inclusive, dynamic, multiangle view of personality. However, big behavioural data come hand in hand with important ethical considerations, and our emerging ability to create a ‘personality panopticon’ requires careful and thoughtful navigation. For our research to improve and thrive in partnership with new technologies, we must not only wield our new tools thoughtfully, but humanely. Through discourse and collaboration with other disciplines and the general public, we can foster mutual growth and ensure that humanity's burgeoning technological capabilities serve, rather than control, the public interest.
Policy-makers are increasingly turning to behavioural science for insights about how to improve citizens’ decisions and outcomes1. Typically, different scientists test different intervention ideas in different samples using different outcomes over different time intervals2. The lack of comparability of such individual investigations limits their potential to inform policy. Here, to address this limitation and accelerate the pace of discovery, we introduce the megastudy—a massive field experiment in which the effects of many different interventions are compared in the same population on the same objectively measured outcome for the same duration. In a megastudy targeting physical exercise among 61,293 members of an American fitness chain, 30 scientists from 15 different US universities worked in small independent teams to design a total of 54 different four-week digital programmes (or interventions) encouraging exercise. We show that 45% of these interventions significantly increased weekly gym visits by 9% to 27%; the top-performing intervention offered microrewards for returning to the gym after a missed workout. Only 8% of interventions induced behaviour change that was significant and measurable after the four-week intervention. Conditioning on the 45% of interventions that increased exercise during the intervention, we detected carry-over effects that were proportionally similar to those measured in previous research3,4,5,6. Forecasts by impartial judges failed to predict which interventions would be most effective, underscoring the value of testing many ideas at once and, therefore, the potential for megastudies to improve the evidentiary value of behavioural science.
Events and Opportunities
You may find the following events and opportunities of interest. Note that inclusion in this list does not constitute an endorsement or a recommendation.
Events:
Vacancies:
Verian, London, UK
Amazon, Seattle, USA
Penn State University, University Park, USA
Adobe, San Francisco, USA
Google, London, UK
YouGov, London, UK
NatWest, Edinburgh, UK
Your Feedback
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