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11	Competition and Data Protection Law in Conflict : Data Protection as a Justification for Anti-Competitive Conduct and a Consideration in Designing Competition Law Remedies Bornudd, David January 2022 (has links) Competition and data protection law are two powerful regimes simultaneously shaping the use of digital information, which has given rise to new interactions between these areas of law. While most views on this intersection emphasize that competition and data protection law must work together, nascent developments indicate that these legal regimes may sometimes conflict. In the first place, firms faced with antitrust allegations are to an increasing extent invoking the need to protect the privacy of their users to justify their impugned conduct. Here, the conduct could either be prohibited by competition law despite of data protection or justified under competition law because of data protection. In the EU, no such justification attempt has reached court-stage, and it remains unclear how an enforcer ought to deal with such a claim. In the second place, competition law can mandate a firm to provide access to commercially valuable personal data to its rivals under a competition law remedy. Where that is the case, the question arising in this connection is whether an enforcer can and should design the remedy in a way that aligns with data protection law. If so, the issue remains of how that ought to be done. The task of the thesis has been to explore these issues, legally, economically, and coherently. The thesis has rendered four main conclusions. First, data protection has a justified role in EU competition law in two ways. On the one hand, enhanced data protection can increase the quality of a service and may thus be factored in the competitive analysis as a dimension of quality. On the other, data protection as a human right must be guaranteed in the application of competition law. Second, these perspectives can be squared with the criteria for justifying competition breaches, in that data protection can be invoked to exculpate a firm from antitrust allegations. Third, in that context, the human rights dimension of data protection may entail that the enforcer must consider data protection even if it is not invoked. However, allowing data protection interests to override competition law in this manner is relatively inefficient as it may lead to less innovation, higher costs, and lower revenues. Fourth, the profound importance of data protection in the EU necessarily means that enforcers should accommodate data protection interests in designing competition law remedies which mandate access to personal data. This may be done in several ways, including requirements to anonymize data before providing access, or to oblige the firm to be compliant with data protection law in the process of providing access. The analysis largely confirms that anonymization is the preferable option. competition law antitrust data protection law GDPR the charter of fundamental rights big tech abuse of a dominant position objective justifications efficiencies article 102 TFEU consumer welfare competition remedies anonymization big data personal data German Facebook Case Google/Fitbit konkurrensrätt konkurrensbegränsning dataskydd dataskyddsförordningen missbruk av dominerande ställning objektivt rättfärdigande artikel 102 FEUF anonymisering Law Juridik
12	Examining the Privacy Aspects and Cost-Quality Balance of a Public Sector Conversational Interface Meier Ström, Theo, Vesterlund, Marcus January 2024 (has links) This thesis explores the implementation of a conversational user interface for Uppsala Municipality, aimed at optimising the balance between cost of usage and quality when using large language models for public services. The central issue addressed is the effective integration of large language models, such as OpenAI's GPT-4, to enhance municipal services without compromising user privacy and data security. The solution developed involves a prototype that utilises a model chooser and prompt tuner, allowing the interface to adapt the complexity of responses based on user input. This adaptive approach reduces costs while maintaining high response quality. The results indicate that the prototype not only manages costs effectively, but also adheres to standards of data privacy and security. Clear information on data use and transparency improved user trust and understanding. In addition, strategies were effectively implemented to handle sensitive and unexpected input, improving overall data security. Overall, the findings suggest that this approach to implementing conversational user interfaces in public services is viable, offering valuable insights into the cost-effective and secure integration of language models in the public sector. The success of the prototype highlights its potential to improve future municipal services, underscoring the importance of transparency and user engagement in public digital interfaces. / Den här masteruppsatsen undersöker implementeringen av ett konversationsgränssnitt för Uppsala kommun, med målet att optimera balansen mellan kostnad och kvalitet vid användning av stora språkmodeller för den offentliga sektorn. Den centrala frågan som besvaras är hur stora språkmodeller, såsom OpenAI:s GPT-4, kan integreras för att förbättra kommunala tjänster utan att kompromissa med användarnas integritet och datasäkerhet. Den utvecklade lösningen innefattar en prototyp som använder en modellväljare och promptjusterare, vilket gör det möjligt för gränssnittet att anpassa svarens komplexitet baserat på användarens meddelande. Detta tillvägagångssätt reducerar kostnaderna samtidigt som en hög svarskvalitet bibehålls. Resultaten visar att prototypen inte bara hanterar kostnaderna effektivt, utan också upprätthåller standarder för datasekretess och säkerhet. Tydlig information om dataanvändning och transparens förbättrade avsevärt användarnas förtroende och förståelse. Dessutom implementerades strategier effektivt för att hantera känslig och oväntad data, vilket förbättrade den övergripande datasäkerheten. Sammanfattningsvis tyder resultaten på att detta tillvägagångssätt för implementering av konversationsgränssnitt i offentliga tjänster är möjligt och erbjuder lärdomar om kostnadseffektiv och säker integration av språkmodeller i offentlig sektor. Prototypens framgång påvisar dess potential att förbättra framtida kommunala tjänster, men lyfter också vikten av transparens och användarengagemang i offentliga digitala gränssnitt. CUI Chatbot Chat bot Municipal OpenAI LLM Public sector Generative AI Public sector AI AI Conversational UI Privacy Cost Transparency XAI Sensitive data Presidio Anonymization CUI Chatbot Chattbot Chatt Kommunal OpenAI LLM Offentlig sektor Generativ AI Offentlig sektor AI AI Konversation UI Integritet Kostnad Transparens XAI Känslig data Presidio Anonymisering Computer and Information Sciences Data- och informationsvetenskap

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Competition and Data Protection Law in Conflict : Data Protection as a Justification for Anti-Competitive Conduct and a Consideration in Designing Competition Law Remedies

Examining the Privacy Aspects and Cost-Quality Balance of a Public Sector Conversational Interface