IG-Fashion Chatbot Assistant
1.4 AI & Legislation
Data protection and privacy:
Definition
Data protection is the process of protecting sensitive information from damage, loss, or corruption Failure to protect data can cause financial losses, loss of customer trust, and legal liability. Data protection is one of the key challenges of digital transformation in organisations of all sizes.
Most data protection strategies have three key focuses:
- Data security: protecting data from malicious or accidental damage
- Data availability: restoring data in the event of damage or loss
- Access control: ensuring that data is accessible to those who actually need it, and not to anyone else
Data Protection Strategy
Every organisation needs a data protection strategy. Here are a few pillars of a reliable strategy:
- Audit of Sensitive Data: Before adopting data protection controls, organisations must first perform an audit of data, identifying data sources, data types, and storage infrastructure used in the organisation, classifying data into sensitivity levels, and see what data protection measures already exist, how effective they are, and which can be extended to protect more sensitive data.
- Assessing Internal and External Risks: The security team in the organisation should regularly assess security risks that may arise inside and outside the organisation. Data protection programs must be designed around these known risks.
- Internal risks include errors in IT configuration or security policies, the lack of strong passwords, poor authentication, and user access management, etc..
- External risks include social engineering strategies such as phishing, malware distribution, and attacks
Defining a Data Protection Policy
Based on the organisation’s analysis of its data and the most relevant threats, it should develop a data protection policy that determines:
- The tolerance for risk for every data category: and protection measures must be applied in accordance with the sensitivity of the data.
- Authorization and authentication policy: use best practices and historical information to identify which business applications or user accounts should have access to sensitive data.
Security Strategy
With respect to data protection, an organisation’s security strategy should:
- Take measures to prevent threat actors from accessing sensitive data
- Ensure that security measures do not hurt productivity or prevent employees from accessing data when and where they need it
- Manage backups effectively to prevent ransomware or other threats, and ensure constant data availability
The most significant regulation in EU affecting data protection is:
European Union (EU): the GDPR
The General Data Protection Regulation (GDPR) applies to all organisations that do business with EU citizens, regardless of whether the company is located inside or outside the EU. Failure to comply can result in fines of up to 4% of worldwide sales or 20 million euros. The GDPR protects personal data such as name, ID number, date or address of birth, web analytics data, medical information, and biometric data.
Regulations on the use of AI in fashion
Definition
The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating AI. The regulation and policy for AI is an emerging issue in jurisdictions all over the world, including the European Union and supra-national bodies like OECD.
The use of AI in the fashion industry presents evolving legal implications, particularly in intellectual property and privacy. AI challenges established norms in intellectual property rights, where ownership of AI-created content remains uncertain, posing risks of easy replication. Privacy concerns arise as AI targets individual consumers, collecting unexpected personal data.
AI and Copyright Law: In the realm of copyright, the interaction between AI and intellectual property raises questions of ownership, especially in designs generated by AI for fabric patterns and colours. Currently, U.S. copyright law lacks clear protection for AI-generated fashion designs, leaving uncertainties about originality thresholds and human authorship requirements.
AI and Trademarks/Counterfeiting: While legal uncertainties persist, AI provides benefits in trademarks and counterfeiting, offering image processing capabilities to identify infringement and verify authenticity. As these technologies advance, AI becomes a valuable tool for global brands to combat counterfeiting, reducing losses and enforcement costs.
AI and Privacy: The use of AI in fashion, and business in general, raises privacy concerns due to extensive consumer data collection. Despite the absence of federal regulation in the U.S., global brands should consider GDPR, regulating data collection from EU residents. Adhering to such laws is crucial for companies to protect consumer data, ensuring informed consent and implementing necessary measure.
Ethics in the use of AI
Definition
AI ethics is a system of moral principles and techniques intended to inform the development and responsible use of artificial intelligence technology. An AI ethics framework is important because it highlights the risks and benefits of AI tools and establishes guidelines for their responsible use.
Ethical Implications of AI in Fashion: The rise of AI-driven autonomous fashion design raises ethical concerns, potentially causing job losses, reduced wages, and insecurity for industry workers. Machines, lacking the creativity and skill of human designers, may compromise product quality. These implications extend to the supply chain, with increased automation possibly exploiting workers in countries with weak labour laws and compromising material quality. Governments, fashion companies, and labour organisations must collaborate to ensure fair treatment, worker rights, and responsible technology use to prevent exploitation and maintain product quality.
Intellectual Property Challenges: The emergence of autonomous fashion design poses challenges to intellectual property rights. Distinguishing between human and machine-created designs becomes complex, raising uncertainty about rights allocation. Existing laws grant intellectual property rights to creators, but with autonomous design, clarity on ownership is lacking. The evolving technology’s impact on the legal system and the industry remains uncertain, requiring adaptation to safeguard designers’ rights.
AI Shaping Consumer Preferences: AI in autonomous fashion design transforms how consumers express style, offering personalised experiences through algorithm-driven designs. This may enhance efficiency and reduce production costs, keeping up with evolving trends and preventing fashion obsolescence. However, there’s a risk of concentration of power as companies rely more on AI than individual designers, potentially limiting diversity. Responsible use of AI is crucial to avoid exploiting vulnerable populations, manipulating consumers, and stifling creativity and innovation. Companies and designers must take steps to ensure ethical and responsible use of autonomous fashion design.
Loosely Based Upon: https://ts2.space/en/the-ethics-of-artificial-intelligence-in-autonomous-fashion-design/#gsc.tab=0
Best practices related to AI & Legislation
Best practices: DupeKiller , ArentFox Schiff
| Name: | DupeKiller AI |
| Link: | https://www2.deloitte.com/uk/en/pages/legal/solutions/dupekiller.html |
| Industry sector: | AI & Legislation |
| Location: | Developed in UK, operates worldwide |
| Description: | DupeKiller AI is an innovative, patent-pending legal technology developed by Deloitte. It uses artificial intelligence to identify copycat products by learning the shape or configuration of a product and seeking out copies. This technology is distinct from counterfeit detection as it focuses on lookalike rather than fake products. |
| Impact in numbers: | DupeKiller AI finds thousands of copycat products for its clients each month, equivalent to around a dozen per hour. |
| Environmental benefits: | By protecting intellectual property and reducing the market for copycat products, it indirectly supports sustainable business practices and discourages waste associated with mass production of copied goods. |
| Social & economic benefits: | DupeKiller AI helps protect the livelihoods, profits, and innovation of original creators and businesses. By safeguarding intellectual property, it supports the creative industries which are significant contributors to the economy. |
| Technological & innovative benefits: | This technology represents a significant advancement in legal tech, transforming how intellectual property rights are protected in the digital age. It offers clients greater certainty over costs and enables more informed, intelligence-led decisions. By using AI for design recognition, DupeKiller AI provides a systematic and strategic approach to tackling the issue of copycat products. |
| Name: | ArentFox Schiff |
| Link: | https://www.afslaw.com/services/fashion-retail-law/fashion-retail-ai |
| Industry sector: | AL & Legislation |
| Location: | USA |
| Description: | ArentFox Schiff provides legal services to clients in the fashion and retail industries, particularly in areas where artificial intelligence intersects with legal compliance and risk management. Their services span various applications of AI in fashion, from product design to distribution and logistics, ensuring that clients navigate the evolving legal landscape effectively. |
| Environmental benefits: | The firm advises on ESG (Environmental, Social, and Governance) initiatives which are driven by AI technologies, potentially aiding clients in enhancing their environmental responsibility. |
| Social & economic benefits: | ArentFox Schiff supports clients with legal matters that have social implications, such as labor laws, and contributes to economic benefits by assisting in the protection of intellectual property, mitigating risks, and ensuring regulatory compliance, which can be economically advantageous for businesses. |
| Technological & innovative benefits: | The firm offers strategic legal counsel on the use of AI for innovative processes in fashion retail, such as streamlining design, using virtual models, conducting AI-driven advertising campaigns, and optimizing product distribution. They address the legalities of software development, data security, content creation, and future AI regulations to protect and empower clients as they adopt transformative technologies. |
