Category: Policy

Policymakers use ideas to figure out what to do: they analyse a problem, define goals, and formulate effective alternative policy strategies. Ideas are shared beliefs and experiences that arise from domestic and international interactions among political actors and shape policy responses. Policymakers use these ideational factors to lay the groundwork for analysing policy failures. The same factors establish a framework for cost–benefit analysis and goal-setting for future policies. Based on this converging set of beliefs, they propose alternative solutions. A policy then occupies a dominant space in the political arena as long as it aligns with the shared consensus. In this way, ideas are crucial in providing direction throughout the policymaking process.

Analysis of policy failure is the stepping-stone of policymaking. Ideas and shared beliefs play a key role in defining the criteria for identifying problems. After the first oil crisis, European governments’ common experience led them to view traditional Keynesian demand-management as the main cause of macroeconomic failure, which weakened post-war political and social arrangements. That shared belief created demand for an entirely different approach—and ultimately paved the way for the European Monetary Union years later. It is therefore essential for policymakers to rely on this ideational framework to analyze past mistakes and to define shared goals for future policies.

Political actors then draw on those same ideational factors to set objectives for alternative solutions. Goals are shaped by shared beliefs, since only politically resonant and dominant objectives survive implementation. Between the “Snake” exchange-rate mechanism (1972) and the European Monetary System (1979), monetarist ideas tilted the priority from full employment toward anti-inflation credibility. Because low-inflation policies fit the prevailing neoliberal consensus, they gained traction and dictated Europe’s monetary blueprint. Ideas that dominate the consensus shape policy, while those that deviate are sidelined—so dominant ideas drive the adoption of alternatives.

Once a policy is implemented, policymakers must demonstrate its effectiveness. A policy remains valid only as long as it aligns with the shared beliefs and goals of political actors. The European neoliberal consensus, which elevated low inflation over growth or employment, proved its worth by converging inflation rates downward—marking a clear break from the diverse priorities of the Bretton Woods era. Thus, ideational factors guide the choice of policy alternatives that stay politically dominant so long as they deliver on their promises.

In short, policymakers draw on ideas and shared beliefs to diagnose policy failures, set goals, and devise alternative solutions. Ideas solidify a policy’s dominance in the political arena. The same principle explains other historical shifts—for example, the fall of the League of Nations and the rise of the United Nations. Therefore, understanding ideational factors is essential for policymakers to know what to do next.

With rapid digitization, an unprecedented amount of data is being collected every day—frequently without users’ explicit consent. There remains a lack of transparency around the background processes of data collection and use. In 2018, the Cambridge Analytica scandal revealed how Facebook collected vast amounts of personal data, including call records and microphone access, without informed consent. Fast forward to 2024, and similar concerns are surfacing around AI-powered tools like OpenAI’s Sora and Google’s Gemini. While these platforms promise improved user experience and automation, privacy advocates argue that the sheer scale of behavioral and contextual data being fed into these systems—often with unclear permissions—continues to blur ethical boundaries.

More recently, in early 2025, controversy erupted over the popular video editing app “Morphix” that quietly collected facial movement data from its users to train deepfake generation models. Although the app had vague terms of service, most users were unaware that their biometric data could be repurposed for generative AI. This incident reignited debates over whether informed consent can truly exist in an age of algorithmic complexity.

The use and misuse of such data by tech companies and governments raise additional red flags. A key concern remains the commodification and sale of user data. In the aftermath of the 2024 Indian general elections, investigations found that third-party political marketing firms had acquired user behavior data through partnerships with shopping and fitness apps—enabling highly targeted political ads that some argued bordered on voter manipulation. This pattern of unregulated data use illustrates a persistent problem: users remain unaware of the trajectory their personal data follows after collection.

The stakes are even higher with the integration of big data into artificial intelligence and predictive systems. For example, predictive policing tools, piloted in parts of the U.S. and U.K. in 2024, came under fire after reports suggested they disproportionately flagged marginalized communities based on biased historical datasets. These developments highlight not just privacy concerns but broader societal risks such as discrimination, misinformation, and erosion of democratic processes.

In conclusion, the unchecked collection and use of big data raise serious ethical and legal concerns that call for immediate attention. As artificial intelligence becomes more deeply embedded in daily life, we must ask: Who owns the data? The fight for digital privacy and freedom hinges on this fundamental question. In its current unregulated state, the tech landscape presents a grey zone—one where personal autonomy, privacy, and democratic values are increasingly at stake.