Generative AI Model: ChatGPT
ChatGPT processes user input to generate contextual responses. OpenAI has implemented privacy measures, including anonymization of data and strict access protocols. Nevertheless, there are concerns regarding compliance with the General Data Protection Regulation (GDPR). One specific example is the decision by the Italian data protection authority Garante per la protezione dei dati personali, which temporarily blocked access to ChatGPT at the beginning of 2023. The authority criticized the lack of a legal basis for the collection and storage of personal data as well as inadequate age controls to protect minors [1].
Legal issues
The use of ChatGPT in Europe requires compliance with the GDPR. The processing of personal data must be transparent, purposeful, and limited to what is necessary. As a result, OpenAI has updated its privacy policy and introduced features that allow users to view and delete their data [2]. Another legal problem is the potential liability for content generated by the AI. If ChatGPT reproduces copyrighted content or makes defamatory statements, there may be legal consequences for users and developers [3].
Ethical aspects
Although OpenAI has developed policies to minimize the creation of inappropriate or biased content, there are documented cases of discriminatory or biased responses. For example, stereotypes or biases in the training data can cause the model to reproduce such biases.
A well-known example is the study by Sheng et al. (2019), which shows that language models can reinforce social prejudices by portraying certain demographic groups in a negative light [4].
Generative AI Model: Google Gemini
Google Gemini is an AI model that builds on Google’s extensive data resources. This raises significant privacy concerns, particularly in relation to the amount and type of data collected. The GDPR requires data minimization and purpose limitation, which can be problematic with Google’s mass data collection.
One specific problem is the integration of user data from various services (e.g., Gmail, Google Maps), which can be used to create profiles without users being sufficiently informed or their express consent being obtained [5].
Legal issues
Google has already committed several privacy violations in Europe. In 2019, the French data protection authority CNIL imposed a fine of 50 million euros for a lack of transparency and a lack of legal basis for personalized advertising [6]. The use of Google Gemini could raise similar legal concerns, especially if personal data is used for generative AI models without sufficient consent.
Ethical aspects
Although Google has developed ethical guidelines for AI, there is criticism of their implementation. The dismissal of ethics researchers, such as Dr. Timnit Gebru in 2020, has raised concerns about the company’s commitment to ethical AI [7]. One example is the debate about the potential risks of AI language models, including the spread of misinformation and the reinforcement of prejudices.
Generative AI Model: Claude
Claude from Anthropic places a strong focus on privacy and anonymity. The model is designed to require and process less personal data, which meets the requirements of the GDPR.
However, there is also a risk that personal data could be processed unintentionally, especially if the model is trained on publicly available data that could contain personal information [8].
Legal issues
Anthropic strives to comply with international and European data protection laws. The reduced dependence on personal data facilitates GDPR compliance. Nevertheless, privacy impact assessments must also be carried out here and privacy principles such as transparency and purpose limitation must be observed [9]. One example is the need to clearly inform users about data processing practices and to obtain consent where necessary.
Ethical aspects
Claude was developed according to the principle of “Constitutional AI”, in which ethical guidelines are integrated directly into the model architecture. This is intended to prevent the generation of inappropriate or harmful content. A concrete example is the implementation of mechanisms that prevent the model from responding to requests for illegal activities or hate speech [10].
Final consideration
The three generative AI models differ in their approach to privacy, legal compliance and ethical considerations:
- Privacy: Claude has an advantage due to the reduced processing of personal data. ChatGPT and Google Gemini must pay more attention to data minimization and transparency. The incidents with ChatGPT in Italy [1] and the fines against Google [6] underline the importance of strict GDPR compliance.
- Legal compliance: All generative AI models must comply with the GDPR and local data protection laws. Legal problems arise primarily due to a lack of transparency and a lack of legal bases for data processing. Companies must take proactive measures to minimize legal risks [2][9].
- Ethical aspects: While all providers have ethical guidelines, they differ in their implementation. Claude places particular emphasis on ethical principles within the model architecture [10]. ChatGPT and Google Gemini must continuously work to avoid bias and discriminatory content [4][7].
Recommendations
For users and organizations in Germany and Europe it is crucial to:
- carry out privacy impact assessments before generative AI models are implemented
- demand transparent privacy guidelines and ensure that providers comply with the GDPR
- conduct ethical reviews to ensure that the AI models do not generate harmful content
Responsible use of AI requires not only technological innovation, but also a strong commitment to privacy and ethics.
Literature
- Garante per la protezione dei dati personali. (2023). Italian data protection authority blocks ChatGPT. https://www.garanteprivacy.it
- OpenAI. (2023). ChatGPT Privacy Policy Updates
- Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76-99.
- Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2019). The Woman Worked as a Babysitter: On Biases in Language Generation. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 3407-3412.
- Data Protection Conference (DSK). (2019). Guidance from the supervisory authorities for providers of online services
- CNIL. (2019). CNIL’s Restricted Committee imposes a financial penalty of 50 million euros against GOOGLE LLC. EDPB
- Wakabayashi, D. (2020). Google Researcher Says She Was Fired Over Paper Highlighting Bias in A.I.The New York Times.
- Article 29 Working Party. (2014). Opinion on the concept of personal data
- European Commission. (2016). General Data Protection Regulation (GDPR)
- Anthropic. (2022). Constitutional AI: Harmlessness from AI Feedback. Anthropic
