Definition and Considerations
Last updated on 2024-12-17 | Edit this page
Estimated time: 20 minutes
Data ethics refers to the moral principles and guidelines that guide the collection, storage, use, and sharing of data. Data ethics aim to ensure that data-related practices and technologies are used in a responsible and ethical manner, taking into account the potential impact on individuals, society, and the environment. It involves making ethical decisions about how data is collected, processed, and used, as well as addressing issues of bias, discrimination, and inequality that may arise in data-driven activities. All these are incorporated in the main digital data ethical considerations, as described below.
Privacy and Consent
Ensuring that individuals associated with cultural heritage data are informed about how their data will be used and obtaining their consent for its collection and usage.
In the domain of cultural heritage data, privacy and consent are crucial considerations to ensure the ethical treatment of individuals associated with the data. This involves informing individuals about how their data will be used and obtaining their explicit consent for its collection and usage. For example, when digitising human remains or artefacts with identifiable features, researchers should obtain consent from relevant stakeholders, such as descendant communities or cultural representatives, before proceeding with digitisation efforts. Additionally, individuals should be informed about the purposes of digitisation, how the data will be stored, and who will have access to it. Consent processes should be transparent, ensuring that individuals understand the implications of providing their data and have the opportunity to withdraw consent if desired.
Cultural Sensitivity
Respecting the cultural significance of the data and avoiding any actions that may offend or disrespect cultural values or beliefs.
Cultural sensitivity is paramount when dealing with cultural heritage data to avoid actions that may offend or disrespect cultural values or beliefs. Researchers and practitioners should approach the digitisation, analysis and interpretation of cultural artefacts, including endangered knowledge witnesses and human remains, with respect for the cultural significance they hold. This includes consulting with relevant cultural communities or stakeholders to understand their perspectives and preferences regarding the handling of cultural heritage data. Additionally, researchers should be mindful of cultural protocols, traditions, and sensitivities when sharing or disseminating digitised data, ensuring that they are represented accurately and respectfully.
Ownership and Intellectual Property
Clarifying the ownership and rights associated with cultural heritage data, including issues related to intellectual property and copyright.
Clarifying ownership and rights associated with cultural heritage data is essential to address issues related to intellectual property and copyright. Researchers should be transparent about the ownership of digitised data, particularly when collaborating with cultural institutions, communities, or individuals. Clear agreements should be established regarding the rights to access, use, and distribute the data, taking into account the interests and concerns of all parties involved. Additionally, researchers should respect any intellectual property rights associated with cultural artefacts or materials being digitised, obtaining permissions or licenses as necessary to ensure compliance with legal and ethical standards.
Data Security
Implementing measures to protect cultural heritage data from unauthorised access, data breaches, or misuse, safeguarding its integrity and confidentiality.
Ensuring data security is paramount to protect cultural heritage data from unauthorised access, data breaches, or misuse. Researchers and institutions should implement robust security measures to safeguard the integrity and confidentiality of digitised data throughout its lifecycle. This includes encryption protocols, access controls, and regular audits to detect and mitigate potential security threats. Additionally, data storage systems should adhere to industry best practices for information security, complying with relevant regulations and standards to minimise risks to cultural heritage data.
Transparency and Standardisation
Being transparent about the sources of cultural heritage data, the methods used for its collection and analysis, and being accountable for any decisions or actions taken based on the data.
Transparency and standardisation are fundamental principles in the ethical treatment of cultural heritage data. Researchers should be transparent about the sources of data, the methods used for its collection and analysis, and any decisions or actions taken based on the data. This transparency fosters accountability and trust among stakeholders, ensuring that digitisation efforts are conducted ethically and responsibly. Additionally, standardising digitisation and data management practices enables consistency, reproducibility, and comparability across studies, facilitating collaboration and data sharing within the research community. Standardisation in the 3D data heritage domain is still a challenging topic and the digital humanities community is undertaking efforts towards this domain.
Yet, there is still a need to reach a consensus, and such efforts should be supported by adequate infrastructure as well as endeavours to enhance the digital skills capacity of humanities researchers who acquire, deploy, analyse, and share data for research and dissemination purposes.
Reliability
Ethical behaviour requires assessing and minimising errors in digital data, including those introduced during digitisation and reconstruction processes, to ensure the reliability of results.
Ensuring the reliability of cultural heritage data is essential to uphold ethical standards in research and practice. Researchers should assess and minimise errors in digitised data, including those introduced during digitisation and reconstruction processes. This involves validating reconstructed data through quality control measures. Quality control measures refer to the following data quality dimensions (Government Data Quality Hub, 2021):
Accuracy: Ensure data reflects reality, with correct and up-to-date values.
Completeness: Have all necessary data available for a specific purpose, minimising critical omissions.
Uniqueness: Eliminate duplicates to maintain data integrity and trustworthiness.
Consistency: Ensure data values are coherent within records and across datasets.
Timeliness: Ensure data is available when needed, especially in time-sensitive contexts.
Validity: Confirm data adheres to expected formats, types, and ranges, facilitating effective use and automation.
Reliability also includes assessing the methods used for data collection and analysis. By prioritising reliability, researchers can enhance the accuracy and validity of findings derived from cultural heritage data, contributing to the advancement of knowledge and understanding in the field.
Equity and Accessibility
Ensuring equitable access to cultural heritage data for all stakeholders, including marginalised or underrepresented communities, and addressing any barriers to access.
Promoting equity and accessibility is crucial to ensure that cultural heritage data is accessible to all stakeholders, including marginalised or underrepresented communities. Researchers should actively address barriers to access, such as language barriers, technological limitations, or lack of resources, to ensure that diverse interpretations and voices are represented in digitisation and data sharing efforts. Participatory approaches can assist towards this direction. Within this frame, researchers should prioritise inclusivity in data dissemination and sharing practices, making digitised data available in accessible formats and platforms to maximize its utility and impact across diverse audiences.
Data Preservation
Taking measures to preserve cultural heritage data for future generations, including considerations for long-term storage, maintenance, and accessibility.
Preserving cultural heritage data for future generations is a responsibility that requires careful consideration of long-term storage, maintenance, and accessibility. Researchers should implement strategies for data preservation, including robust backup and archival systems, to safeguard digitised data from loss or degradation over time. Additionally, researchers should document metadata and paradata to facilitate data discovery and interpretation by future researchers.
Metadata can be described as data about data, e.g. information about an object in a museum collection which might be visualised through a 3D digital model in a Digital Asset Management System (DAMS)
Paradata can be described as data about processes, e.g. information about the digitisation and post-processing of the 3D model.
By prioritising data preservation, researchers can ensure that cultural heritage data remains accessible and relevant for generations to come, contributing to the ongoing study and appreciation of cultural heritage.
Further information about the preservation of Digital Heritage can be found in UNESCO’s Charter on the Preservation of Digital Heritage, as well as the resources on the websites of the Digital Preservation Coalition and the Community Standards for 3D Data Preservation.
Avoiding Bias and Discrimination
Being aware of and mitigating any biases or prejudices present in cultural heritage data or data analysis processes to prevent discrimination or unfair treatment.
Researchers should be vigilant in avoiding bias and discrimination in cultural heritage data and data analysis processes. This involves critically evaluating potential biases inherent in digitised data, such as sampling biases or cultural biases, and taking steps to mitigate their impact on research outcomes. Researchers should also be mindful of ethical considerations when interpreting digitised data, avoiding interpretations or conclusions that perpetuate stereotypes or marginalise certain groups. By promoting fairness and inclusivity in data collection, analysis, and interpretation, researchers can uphold ethical standards and contribute to a more equitable understanding of cultural heritage.
Challenge
Europeana provides access to millions of assets from heritage institutions across Europe. Go to the website of Europeana https://www.europeana.eu/en, look for assets which are related to your research and find an interesting object.
Examine carefully all the information provided about the object in Europeana (or the providing institution). Can you think about any ethical considerations which might arise by the way that such information has been handled, presented or interpreted?
Team up with a colleague and discuss your comments. Are there any differences or similarities between your findings?
One person from each team shares with the rest of the group their remarks about ethical considerations in data.
Data Protection Act and GDPR
A great amount of data managed by digital humanists in research and practice include information about people. It is important to remember that within ethical conduct, personal data should be protected and hence comply with national and international regulations.
In the UK, the Data Protection Act (DPA) is legislation that governs the processing of personal data. It provides guidelines and regulations on how personal data should be collected, stored, used, and shared. The DPA outlines the rights of individuals regarding their personal data and imposes obligations on organisations that handle such data to ensure its protection. The DPA is essentially the UK’s implementation of the European GDPR.
According to the DPA, personal data should be (Data Protection Act 2018):
- used fairly, lawfully and transparently - used for specified, explicit purposes
- used in a way that is adequate, relevant and limited to only what is necessary
- accurate and, where necessary, kept up-to-date
- kept for no longer than is necessary
- handled in a way that ensures appropriate security, including protection against unlawful or unauthorised processing, access, loss, destruction or damage.
Within this frame, it is particularly necessary to protect sensitive data, which refer to: race; ethnic background; political opinions; religious beliefs; trade union membership; genetics; biometrics; health; sex life or orientation.
The General Data Protection Regulation (GDPR) is a comprehensive data protection law enacted by the European Union (EU) in 2018. The GDPR harmonises data protection laws across all EU member states. It enhances the rights of individuals and imposes stricter obligations on organisations regarding the processing of personal data. These are the main protection and accountability principles according to the GDPR (GDPR 2016):
Lawfulness, fairness, and transparency: These principles must guide the processing of data.
Purpose limitation: Data should be used only for the purposes specified to the data subject when their data was collected.
Data minimization: Only the necessary data should be collected and processed for the specified purposes.
Accuracy: Personal data should be accurate and kept up-to-date.
Storage limitation: Personal data can be stored only for the time that is necessary for the specified purposes.
Integrity and confidentiality: Data processing must ensure the security, integrity, and confidentiality of the data.
Accountability: Data controllers must be able to demonstrate compliance with GDPR regulations.
Key Points
Researchers and practitioners in the cultural heritage domain can responsibly collect, manage, and use data while respecting the rights, values, and interests of all stakeholders involved by considering the following ethical aspects:
- Privacy and Consent
- Cultural Sensitivity
- Ownership and Intellectual Property
- Data Security
- Transparency and Standardisation
- Reliability
- Equity and Accessibility
- Data Preservation
- Avoiding Bias and Discrimination