betekintés - Computer Security and Privacy - # Responsible Use of Synthetic Data in Machine Learning

The Risks of Using Synthetic Data: Diversity-Washing, Consent Circumvention, and Consolidation of Power

Q: How can we develop frameworks and guidelines to ensure synthetic data is used responsibly and ethically, with meaningful participation from affected communities?

To ensure the responsible and ethical use of synthetic data, frameworks and guidelines must be developed that prioritize transparency, accountability, and inclusivity. Here are some key steps to develop such frameworks: Inclusive Stakeholder Engagement: Involve a diverse set of stakeholders, including data subjects, community representatives, ethicists, and technologists, in the development of guidelines. This ensures that the perspectives of those affected by the data are considered. Ethical Impact Assessments: Conduct thorough ethical impact assessments before using synthetic data. These assessments should evaluate potential risks, biases, and unintended consequences of using synthetic data in AI systems. Data Minimization and Lineage: Implement principles of data minimization and lineage to track the origins of synthetic data. This helps in ensuring that the data used is ethically sourced and does not perpetuate harm. Consent and Transparency: Prioritize informed consent from data subjects when using synthetic data. Transparently communicate how the data will be used, by whom, and for what purposes. Accountability Mechanisms: Establish mechanisms for accountability, such as regular audits, reporting requirements, and avenues for redress in case of misuse or harm caused by synthetic data. Continuous Evaluation and Improvement: Regularly evaluate the impact of synthetic data usage on affected communities and AI systems. Use feedback to improve guidelines and frameworks over time. By incorporating these elements into frameworks and guidelines, we can ensure that synthetic data is used responsibly and ethically, with meaningful participation from affected communities.

Q: What are the potential unintended consequences of using synthetic data to address issues of bias and lack of representation in datasets, and how can we mitigate these risks?

While using synthetic data to address bias and lack of representation in datasets can be beneficial, it also poses potential unintended consequences. Some of these risks include: Reinforcement of Biases: Synthetic data generated from biased or incomplete datasets may inadvertently reinforce existing biases in AI systems, leading to discriminatory outcomes. Lack of Contextual Understanding: Synthetic data may lack the nuanced contextual understanding present in real-world data, leading to inaccurate or misleading results in AI models. Privacy Concerns: Procedurally created synthetic data may raise privacy concerns if it involves the generation of realistic representations of individuals without their consent. Unintended Amplification of Harm: Inaccurate or biased synthetic data used in AI systems can amplify harm, especially for marginalized or underrepresented groups. To mitigate these risks, it is essential to: Validate and Test: Thoroughly validate synthetic data to ensure it accurately represents the real-world context and does not introduce new biases. Diverse Representation: Ensure diverse representation in the creation of synthetic data to avoid reinforcing existing biases. Ethical Oversight: Implement ethical oversight mechanisms to monitor the use of synthetic data and address any unintended consequences promptly. Transparency and Accountability: Maintain transparency about the origins and limitations of synthetic data used in AI systems, and hold stakeholders accountable for any negative impacts. By proactively addressing these risks and implementing mitigation strategies, the potential unintended consequences of using synthetic data can be minimized.

Q: How might the use of synthetic data interact with and impact existing power structures and dynamics in the development and deployment of AI systems?

The use of synthetic data can interact with and impact existing power structures and dynamics in the development and deployment of AI systems in several ways: Consolidation of Power: Synthetic data creation is often controlled by those with the resources and expertise to generate it, leading to a consolidation of power in the hands of data creators and model developers. Reinforcement of Inequities: If synthetic data is not created responsibly, it can reinforce existing inequities and biases present in society, perpetuating power imbalances in AI systems. Lack of Representation: Synthetic data may not accurately represent the diversity of real-world populations, leading to underrepresentation or misrepresentation of certain groups, further marginalizing them in AI systems. Opacity and Control: The opaque nature of synthetic data creation can obscure the decision-making processes behind dataset generation, giving those in control the ability to shape narratives and outcomes in AI systems. To address these power dynamics and mitigate their negative impacts, it is crucial to: Promote Diversity and Inclusion: Ensure diverse representation in the creation and use of synthetic data to prevent the entrenchment of existing power structures. Transparency and Accountability: Maintain transparency in the processes of synthetic data creation and use, allowing for scrutiny and accountability. Empower Marginalized Communities: Involve marginalized communities in decision-making processes related to synthetic data to empower them and mitigate power differentials. By actively considering and addressing the power dynamics at play in the use of synthetic data, we can work towards more equitable and responsible development and deployment of AI systems.

Alapfogalmak

Synthetic data poses significant risks of diversity-washing, circumventing consent, and consolidating power away from those most impacted by algorithmic harms.

Kivonat

The paper examines two key risks of using synthetic data in machine learning development:

Diversity-Washing: Synthetic data offers a way to diversify datasets, but diversity in real-world faces often follows from cultural practices that are qualitative and meaning-laden rather than quantitative. Creating a synthetic dataset or adding synthetic data to existing datasets in an attempt to diversify that dataset runs the risk of diversity-washing - appearing to resolve valid criticism regarding a dataset's distribution and representation but in a way that is superficial. This risks legitimizing technologies like facial recognition despite potentially continuing to propagate bias.
Circumventing Consent: Synthetic data provides an avenue for model developers to side-step thorny issues around collecting large-scale representative datasets. Proper consent to data usage is foundational to privacy enforcement tools, but using synthetic data risks circumventing and obfuscating consent, thus complicating deterrence and enforcement.

The paper illustrates these risks through real-world examples, including a facial recognition model evaluation using synthetic data, and the FTC's enforcement actions against models trained on deceptively collected data. It argues that these risks exemplify how synthetic data can consolidate power in the hands of model creators and decouple data from those it represents and those who are harmed by its improper use. The paper calls for future work to examine the breadth and usage of synthetic data and to work towards mitigating its risks while enabling its potential for participatory empowerment.

Összefoglaló testreszabása

Átírás mesterséges intelligenciával

Hivatkozások generálása

Forrás fordítása

Egy másik nyelvre

Gondolattérkép létrehozása

a forrásanyagból

Forrás megtekintése

arxiv.org

Statisztikák

"Synthetic data provides an avenue for model developers to side-step thorny issues around collecting large-scale representative datasets."
"Proper consent to data usage is foundational to privacy enforcement tools that the FTC has used to require companies delete ML models trained on improperly collected data."

Idézetek

"Synthetic data offers a way of diversifying datasets, but diversity in real-world faces often follows from cultural practices that are qualitative and meaning-laden rather than quantitative."
"Using synthetic data risks circumventing and obfuscating consent, thus complicating deterrence and enforcement."

Főbb Kivonatok

Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention

by Cedric Desla... : arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01820.pdf

Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention

Mélyebb kérdések

How can we develop frameworks and guidelines to ensure synthetic data is used responsibly and ethically, with meaningful participation from affected communities?

To ensure the responsible and ethical use of synthetic data, frameworks and guidelines must be developed that prioritize transparency, accountability, and inclusivity. Here are some key steps to develop such frameworks:

Inclusive Stakeholder Engagement: Involve a diverse set of stakeholders, including data subjects, community representatives, ethicists, and technologists, in the development of guidelines. This ensures that the perspectives of those affected by the data are considered.

Ethical Impact Assessments: Conduct thorough ethical impact assessments before using synthetic data. These assessments should evaluate potential risks, biases, and unintended consequences of using synthetic data in AI systems.

Data Minimization and Lineage: Implement principles of data minimization and lineage to track the origins of synthetic data. This helps in ensuring that the data used is ethically sourced and does not perpetuate harm.

Consent and Transparency: Prioritize informed consent from data subjects when using synthetic data. Transparently communicate how the data will be used, by whom, and for what purposes.

Accountability Mechanisms: Establish mechanisms for accountability, such as regular audits, reporting requirements, and avenues for redress in case of misuse or harm caused by synthetic data.

Continuous Evaluation and Improvement: Regularly evaluate the impact of synthetic data usage on affected communities and AI systems. Use feedback to improve guidelines and frameworks over time.

By incorporating these elements into frameworks and guidelines, we can ensure that synthetic data is used responsibly and ethically, with meaningful participation from affected communities.

What are the potential unintended consequences of using synthetic data to address issues of bias and lack of representation in datasets, and how can we mitigate these risks?

While using synthetic data to address bias and lack of representation in datasets can be beneficial, it also poses potential unintended consequences. Some of these risks include:

Reinforcement of Biases: Synthetic data generated from biased or incomplete datasets may inadvertently reinforce existing biases in AI systems, leading to discriminatory outcomes.

Lack of Contextual Understanding: Synthetic data may lack the nuanced contextual understanding present in real-world data, leading to inaccurate or misleading results in AI models.

Privacy Concerns: Procedurally created synthetic data may raise privacy concerns if it involves the generation of realistic representations of individuals without their consent.

Unintended Amplification of Harm: Inaccurate or biased synthetic data used in AI systems can amplify harm, especially for marginalized or underrepresented groups.

To mitigate these risks, it is essential to:

Validate and Test: Thoroughly validate synthetic data to ensure it accurately represents the real-world context and does not introduce new biases.
Diverse Representation: Ensure diverse representation in the creation of synthetic data to avoid reinforcing existing biases.
Ethical Oversight: Implement ethical oversight mechanisms to monitor the use of synthetic data and address any unintended consequences promptly.
Transparency and Accountability: Maintain transparency about the origins and limitations of synthetic data used in AI systems, and hold stakeholders accountable for any negative impacts.
By proactively addressing these risks and implementing mitigation strategies, the potential unintended consequences of using synthetic data can be minimized.

How might the use of synthetic data interact with and impact existing power structures and dynamics in the development and deployment of AI systems?

The use of synthetic data can interact with and impact existing power structures and dynamics in the development and deployment of AI systems in several ways:

Consolidation of Power: Synthetic data creation is often controlled by those with the resources and expertise to generate it, leading to a consolidation of power in the hands of data creators and model developers.

Reinforcement of Inequities: If synthetic data is not created responsibly, it can reinforce existing inequities and biases present in society, perpetuating power imbalances in AI systems.

Lack of Representation: Synthetic data may not accurately represent the diversity of real-world populations, leading to underrepresentation or misrepresentation of certain groups, further marginalizing them in AI systems.

Opacity and Control: The opaque nature of synthetic data creation can obscure the decision-making processes behind dataset generation, giving those in control the ability to shape narratives and outcomes in AI systems.

To address these power dynamics and mitigate their negative impacts, it is crucial to:

Promote Diversity and Inclusion: Ensure diverse representation in the creation and use of synthetic data to prevent the entrenchment of existing power structures.
Transparency and Accountability: Maintain transparency in the processes of synthetic data creation and use, allowing for scrutiny and accountability.
Empower Marginalized Communities: Involve marginalized communities in decision-making processes related to synthetic data to empower them and mitigate power differentials.
By actively considering and addressing the power dynamics at play in the use of synthetic data, we can work towards more equitable and responsible development and deployment of AI systems.