Addressing Bias in OSINT Methodology

While many OSINT-focused blogs highlight data collection and automated processing, the topic of bias doesn’t often receive the same attention. Yet, objectivity is essential for a strong intelligence product. OSINT practitioners use various methods to gather, analyse, and synthesise publicly available information, supporting investigations, identifying potential threats, and developing situational awareness. However, like all intelligence-gathering processes, OSINT can be influenced by biases that can shape investigations in ways that lead to skewed or incomplete outcomes. For those working with publicly available information (PAI), understanding and mitigating bias is crucial.

Types of Bias in OSINT

Bias can surface at different stages of OSINT work, from data collection through to analysis, report writing, and dissemination. Here are some common types of bias encountered in OSINT investigations and workflows:

1. Selection Bias
   • Definition: Selection bias occurs when the dataset or information used in an investigation is unrepresentative, potentially due to reliance on sources that don't cover all perspectives or relevant aspects of an investigation.
   • Example in OSINT: When gathering posts from social media, relying on one platform may not capture the full picture. Platforms may be more popular in specific countries or with certain political groups, so collected content may not reflect the behaviours or opinions of people who prefer different platforms. This can lead to skewed conclusions about individuals or groups if alternative perspectives are overlooked.
2. Confirmation Bias
   • Definition: Confirmation bias is the tendency to search for, interpret, or prioritise information that confirms pre-existing beliefs or hypotheses, potentially disregarding conflicting evidence.
   • Example in OSINT: Analysts may focus on connections that support an existing hypothesis, such as associating an individual with an organized crime group based on weak links, without equally considering evidence that might suggest otherwise. This bias can be particularly problematic in high-stakes investigations where initial assumptions may be strong, such as in terrorism cases or when profiling repeat offenders.
3. Availability Bias
   • Definition: Availability bias occurs when more readily accessible or recent information is overemphasised, even if it’s not the most relevant or accurate.
   • Example in OSINT: A recent incident that received extensive media coverage, such as an act of terrorism, may lead an analyst to overestimate the likelihood of similar incidents occurring, even if historical data doesn’t support such conclusions. This could skew the assessment, leading to a reactive rather than proactive approach.
4. Cultural Bias
   • Definition: Cultural bias arises when an investigator’s own cultural norms and values influence their interpretation of information, affecting analysis and reporting.
   • Example in OSINT: When examining behaviours of groups from different cultural backgrounds, analysts might misinterpret actions due to a lack of cultural understanding, leading to inaccurate characterisations. For example, a UK analyst assessing the behaviours of a group based in Asia may misinterpret culturally normative actions as suspicious if lacking relevant cultural context.
5. Data Source Bias
   • Definition: Data source bias occurs when the information or source itself has inherent biases that impact the conclusions drawn.
   • Example in OSINT: An investigation that relies heavily on media outlets may reflect biases present in those sources or their journalists, especially if the publication has known political leanings or sensationalism.

Mitigating Bias in OSINT Investigations

Awareness of these biases is the first step toward mitigating their effects. OSINT practitioners can take a range of approaches to reduce bias, ensuring their findings are accurate, comprehensive, and fair.

1. Diversify Data Sources
   • Application: In both entity investigations and situational awareness workflows, using a variety of data sources helps mitigate selection and data source bias. Cross-referencing sources, such as commercially available information (CAI) from data brokers with other data, can provide a more complete picture. Public Insights, for example, offers access to diverse UK datasets, from planning permissions to rental properties, helping investigators avoid over-reliance on a single source.
   • Benefit: This approach reduces the influence of any single source’s inherent bias, helping OSINT practitioners capture a more balanced and multi-dimensional view of subjects or incidents.
2. Implement Structured Analytical Techniques (SATs)
   • Application: Techniques like “Analysis of Competing Hypotheses” (ACH) encourage practitioners to evaluate multiple hypotheses and counteract confirmation bias. Other SATs, such as “Devil’s Advocacy,” where an analyst critiques a hypothesis, also help reduce bias.
   • Benefit: By intentionally challenging initial conclusions, these techniques help uncover overlooked evidence and foster a more objective analysis, especially valuable in complex investigations and link analysis.
3. Enhance Cultural Competency
   • Application: Developing an understanding of different cultures can help analysts interpret behaviours or statements more accurately, particularly when researching subjects from diverse backgrounds. Leveraging publicly available information about cultural norms or consulting experts in the field are practical ways to improve cultural awareness.
   • Benefit: This approach mitigates cultural bias and results in more accurate characterisations of subjects from different backgrounds, ensuring that intelligence does not inadvertently misrepresent individuals or groups.
4. Use Balanced Scoring and Weighting Systems
   • Application: When assessing risk or relevance, applying a structured scoring or weighting system can help counter availability bias. For instance, when building a social network chart, using a rating system for connection strength based on available data can balance interpretation rather than relying on assumptions.
   • Benefit: A consistent scoring system ensures that recent or memorable events do not disproportionately influence the assessment, maintaining a balanced view of the situation.
5. Automate Routine Tasks Where Possible
   • Application: Automation tools that scan a wide range of sources can assist in collecting data systematically, ensuring similar processes are applied across investigations and reducing manual selection bias. For example, automating data collection for subject profiles can minimise the risk of overlooking crucial sources.
   • Benefit: Automated processes standardize information collection, ensuring consistency and reducing subjective decisions that could introduce bias.

Achieving Objectivity and Depth in OSINT Investigations

OSINT plays a pivotal role in informed decision-making across various sectors, from corporate security to law enforcement.  Recognizing and mitigating bias is essential to ensure that intelligence remains accurate and actionable. By understanding the types of bias that can affect OSINT investigations and actively working to address them, practitioners can produce intelligence that is both reliable and comprehensive. Using diversified data sources, structured analysis techniques, and automated tools like those provided by Public Insights, OSINT professionals can create balanced, multi-dimensional intelligence—a critical foundation for any successful investigation.

Explore how Public Insights can support your OSINT efforts with a trial at cradle.publicinsights.uk.