How can you prevent bias in information systems data?

1 Identify potential sources of bias

The first step to prevent bias in IS data is to identify where it can come from. Bias can originate from different sources, such as the data itself, the methods used to collect or process it, the people involved in the IS project, or the context in which the data is used. For example, data can be biased if it is incomplete, inaccurate, outdated, or unrepresentative of the population or phenomenon of interest. Methods can be biased if they use inappropriate or inconsistent criteria, algorithms, or models to analyze or interpret data. People can be biased if they have personal or professional interests, beliefs, or values that affect their data-related choices or actions. Context can be biased if it creates expectations, pressures, or incentives that influence the data or its use.

2 Apply ethical principles and standards

The second step to prevent bias in IS data is to apply ethical principles and standards that guide the design, development, and management of IS. Ethical principles and standards are norms or rules that define what is right or wrong, good or bad, fair or unfair, in relation to IS and data. They can be derived from various sources, such as laws, regulations, codes of conduct, best practices, or moral values. For example, some ethical principles and standards that can help prevent bias in IS data are: respect for human dignity and diversity, protection of privacy and confidentiality, accuracy and quality of data, transparency and accountability of data processes and outcomes, and social responsibility and justice.

3 Implement data quality and governance practices

The third step to prevent bias in IS data is to implement data quality and governance practices that ensure the reliability, validity, and usefulness of data. Data quality and governance practices are procedures or mechanisms that control, monitor, and improve the data lifecycle, from creation to disposal. They can involve various activities, such as data cleaning, verification, validation, integration, security, audit, or evaluation. For example, some data quality and governance practices that can help prevent bias in IS data are: defining and enforcing data standards and policies, using data quality tools and techniques, such as data profiling or data cleansing , conducting data quality assessments and audits, and establishing data quality roles and responsibilities.

4 Involve diverse and inclusive stakeholders

The fourth step to prevent bias in IS data is to involve diverse and inclusive stakeholders in the IS project. Stakeholders are individuals or groups that have an interest or influence in the IS project, such as users, customers, managers, developers, or analysts. Diversity and inclusion refer to the representation and participation of different perspectives, backgrounds, experiences, or identities in the IS project. For example, some benefits of involving diverse and inclusive stakeholders in the IS project are: increasing the relevance and acceptance of the data and the IS, enhancing the creativity and innovation of the data and the IS, reducing the risks and costs of data errors or failures, and promoting the ethical and social values of the data and the IS.

5 Test and evaluate data for bias

The fifth step to prevent bias in IS data is to test and evaluate data for bias before, during, and after the IS project. Testing and evaluation are methods or techniques that measure or compare the data against certain criteria, standards, or expectations. They can use various approaches, such as quantitative or qualitative, descriptive or inferential, formative or summative, or internal or external. For example, some methods or techniques that can help test and evaluate data for bias are: using data quality indicators or metrics, such as accuracy or completeness , applying data analysis methods or tools, such as statistical tests or visualizations , conducting data audits or reviews, such as peer review or external audit , and collecting data feedback or satisfaction, such as surveys or interviews .

6 Learn and improve from data bias incidents

The sixth and final step to prevent bias in IS data is to learn and improve from data bias incidents that may occur despite the previous steps. Data bias incidents are situations or events that reveal or cause bias in IS data, such as complaints, errors, breaches, or harms. Learning and improving from data bias incidents are processes or actions that identify, analyze, resolve, and prevent data bias incidents. They can involve various steps, such as reporting, investigating, documenting, communicating, correcting, or implementing changes. For example, some ways to learn and improve from data bias incidents are: creating and following data bias incident response plans, using data bias incident reporting systems or tools, such as incident logs or dashboards , conducting data bias incident root cause analysis or lessons learned, and updating data quality and governance practices or ethical principles and standards.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?